Search Results (77)

Under the combined pressures of dual carbon policy constraints, the integration of intelligent construction technologies, and intensifying market competition, the development of a scientific and robust performance evaluation system has become essential for building construction enterprises seeking to enhance their core competitiveness. Traditional evaluation methods, however, often suffer from incomplete indicator systems and limited capability in addressing high-dimensional and nonlinear problems, rendering them inadequate for the evolving demands of the industry. To address these challenges, this study proposes a performance evaluation model for building construction enterprises based on the least squares support vector machine (LSSVM), optimized by an improved Pied Kingfisher Optimizer (IPKO). Drawing on environment–behavior theory, the model incorporates three environmental and ten behavioral factors. To overcome the limitations of the original PKO algorithm—namely, insufficient exploration capability and weak local search—the exploration phase of PKO is integrated with that of the Marine Predators Algorithm. Empirical results demonstrate that: (1) the proposed IPKO outperforms Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Sparrow Search Algorithm (SSA), Dung Beetle Optimizer (DBO), Ospery Optimization Algorithm (OOA), and the original PKO in most benchmark functions; (2) the ReliefF feature selection algorithm improves the model’s test set accuracy by approximately 2.18%; and (3) the IPKO-LSSVM model achieves 6.53%, 4.16%, and 6.74% higher prediction accuracy than Backpropagation Neural Networks (BPNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), respectively. These findings highlight the model’s effectiveness in addressing small-sample, high-dimensional, and nonlinear problems, offering a scientifically sound and practical tool for performance evaluation in building construction enterprises. Full article

Reliable seed accession identification underpins germplasm conservation, traceability and breeding; however, conventional assays remain destructive, labour-intensive and difficult to scale. Here, visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) hyperspectral imaging (HSI; 449.54–2399.17 nm; 563 bands) was used to classify 32 grain–legume accessions (n = 3200 seeds; 100 seeds per accession), comprising 30 common bean (Phaseolus vulgaris L.) landraces plus two outgroup legumes (Vigna angularis (Willd.) Ohwi & Ohashi and Cajanus cajan (L.) Huth). Each seed was represented by one ROI-averaged spectrum obtained from mean representative pixels within a standardised 10 × 10 pixel window at the centre of each seed. A fixed stratified 70:30 seed-level training:test partition was used, with 70 seeds per accession (n = 2240) reserved for fully independent training and 30 seeds per accession (n = 960) reserved as a fully independent test set. Principal component analysis (PCA) captured 97.42% of the spectral variance in the first three components (PC1 = 63.34%, PC2 = 23.78%, and PC3 = 10.31%). One-versus-rest wavelength association mapping revealed a maximum R² of 0.775 at 461.37 nm, and ReliefF concentrated the strongest reduced-band signal within 449.54–456.30 nm and 577.02–597.54 nm. In the original ReliefF-selected 16-band benchmark, the subspace discriminant reached 68.25% macro-F1 and 68.54% balanced accuracy; after edge-band trimming, the alternative 16-band configuration decreased to 60.67% and 60.94%, respectively. With respect to the full-spectrum sensitivity benchmark, linear discriminant analysis achieved 96.35% balanced accuracy, followed by linear SVM (94.17%). Deep learning trained directly on the full 563-band spectra reached 84.90% test accuracy, 84.47% macro-F1, 86.27% precision and 84.90% recall, with MLP_Wide outperforming the convolutional, recurrent and attention-based alternatives. Overall, under controlled laboratory conditions, this benchmark shows that accession discrimination is driven mainly by visible-domain contrasts in the most compact representations, whereas the full spectral context remains important for the most confusable accessions and for cautious future sensor design. The reduced-band findings should therefore be interpreted as exploratory guidance for sensor design rather than as a validated deployment-ready specification. Full article

In this work, measurements of thermal diffusivity, heat capacity and thermal expansion of 40HM (42CrMo4, 1.7225, AISI 4140) steel manufactured conventionally and via Laser Powder Bed Fusion (L-PBF) were carried out in the temperature range from room temperature (RT) to 1000 °C. Thermophysical properties were tested using specialized test stands from NETZSCH. Thermal diffusivity was studied using both the LFA 427 laser flash apparatus and the LFA 467 xenon flash apparatus. Specific heat capacity was investigated using DSC 404 F1 Pegasus differential scanning calorimeter, and thermal expansion was investigated using the DIL 402 C. Inconel 600 and A310 steel were selected as the reference materials during the thermal diffusivity test using LFA467 in the RT÷500 °C range. The conventionally manufactured 40HM steel, in the form of hot-rolled bar stock, was subjected to standard heat treatment for this steel grade—quenching followed by high-temperature tempering. The additively manufactured 40HM steel was subjected to stress-relief annealing. The results revealed no significant differences between the thermophysical properties of the L-PBF-produced samples in the out-of-plane and in-plane build orientations. Furthermore, no substantial differences were observed between the thermophysical properties of the conventionally produced material and the material manufactured using the L-PBF technique. Full article

Background/Objectives: Osteoporosis is a major global health concern, and early risk assessment plays a crucial role in fracture prevention. Although demographic, clinical, and lifestyle factors are commonly incorporated into screening tools, their relative importance within data-driven prediction frameworks can vary substantially across datasets. Rather than aiming to identify novel predictors, this study evaluates the stability and behavior of established osteoporosis risk factors using statistical inference and machine learning-based feature selection methods across heterogeneous data sources. We further examine whether simplified and near-minimal models can achieve predictive performances comparable to that of full-feature configurations. Methods: An open-access Kaggle dataset (n = 1958) and a retrospective clinical dataset from the University of Phayao Hospital (n = 176) were analyzed. Feature relevance was assessed using logistic regression, likelihood ratio testing, MRMR, ReliefF, and unified importance scoring. Multiple predictor configurations, ranging from full-feature to minimal and near-minimal models, were evaluated using decision tree, support vector machine, k-nearest neighbor, naïve Bayes, and efficient linear classifiers. External validation was performed using hospital-based records. Results: Across all analyses, age consistently emerged as the dominant predictor, followed by corticosteroid use, while other variables showed limited incremental predictive contributions. Simplified models based on age alone or age combined with medication-related variables achieved performances comparable to full-feature models (accuracy ≈91% and AUC ≈ 0.95). In addition, near-minimal models incorporating gender alongside age and medications demonstrated a favorable balance between discrimination and computational efficiency under external validation. Although overall performance declined under distributional shift, naïve Bayes and efficient linear classifiers showed the most stable external behavior (AUC = 0.728–0.787). Conclusions: These findings indicate that stability-driven feature selection primarily reproduces well-established epidemiological risk patterns rather than identifying novel predictors. Minimal and near-minimal models—including those incorporating gender—retain acceptable performances under external validation and are methodologically efficient. Given the limited size and single-center nature of the external cohort, the results should be interpreted as preliminary methodological evidence rather than definitive support for clinical screening deployment. Further multi-center studies are required to assess generalizability and clinical relevance. Full article

Background/Objectives: Early-stage diabetes often presents with subtle symptoms, making timely screening challenging. This study aimed to develop an interpretable and robust machine learning framework for early-stage diabetes risk prediction using integrated statistical and machine learning–based feature selection, and to evaluate its generalizability using real-world hospital data. Methods: A Union Feature Selection approach was constructed by combining logistic regression significance testing with ReliefF and MRMR feature importance scores. Five machine learning models—Decision Tree, Naïve Bayes, SVM, KNN, and Neural Network—were trained on the UCI Early Stage Diabetes dataset (N = 520) under multiple feature-selection scenarios. External validation was performed using retrospective hospital records from the University of Phayao (N = 60). Model performance was assessed using accuracy, precision, recall, and F1-score. Results: The union feature-selection approach identified four core predictors—polyuria, polydipsia, gender, and irritability—with additional secondary features providing only marginal improvements. Among the evaluated models, Naïve Bayes demonstrated the most stable external performance, achieving 85% test accuracy, balanced precision, recall, and F1-score, along with a moderate AUC of 0.838, indicating reliable discriminative ability in real-world hospital data. In contrast, SVM, KNN, and Neural Network models, despite exhibiting very high internal validation performance (>96%) under optimally selected ML features, showed marked performance decline during external validation, highlighting their sensitivity to distributional shifts between public and clinical datasets. Conclusions: The combined statistical–ML feature selection method improved interpretability and stability in early-stage diabetes prediction. Naïve Bayes demonstrated the strongest generalizability and is well suited for real-world screening applications. The findings support the use of integrated feature selection to develop efficient and clinically relevant risk assessment tools. Full article

Speech is one of the earliest-affected modalities in Parkinson’s disease (PD). For more reliable PD evaluation, speech-based telediagnosis studies often use multiple samples from the same subject to capture variability in speech recordings. However, many existing studies split samples—rather than subjects—between training and testing, creating a biased experimental setup that allows data (samples) from the same subject to appear in both sets. This raises concerns for reliable PD evaluation due to data leakage, which results in over-optimistic performance (often close to 100%). In addition, detecting subtle vocal impairments from speech recordings using multiple feature extraction techniques often increases data dimensionality, although only some features are discriminative while others are redundant or non-informative. To address this and build a reliable speech-based PD telediagnosis system, the key contributions of this work are two-fold: (1) a uniform (fair) experimental setup employing subject-wise symmetric (stratified) splitting in 5-fold cross-validation to ensure better generalization in PD prediction, and (2) a novel hybrid data complexity-aware (HDC) feature selection method that improves class separability. This work further contributes to the research community by releasing a publicly accessible five-fold benchmark version of the Parkinson’s speech dataset for consistent and reproducible evaluation. The proposed HDC method analyzes multiple aspects of class separability to select discriminative features, resulting in reduced data complexity in the feature space. In particular, it uses data complexity measures (F4, F1, F3) to assess minimal feature overlap and ReliefF to evaluate the separation of borderline points. Experimental results show that the top-50 discriminative features selected by the proposed HDC outperform existing feature selection algorithms on five out of six classifiers, achieving the highest performance with 0.86 accuracy, 0.70 G-mean, 0.91 F1-score, and 0.58 MCC using an SVM (RBF) classifier. Full article

Background: In high-dimensional, small-sample omics studies such as metabolomics, feature selection not only determines the discriminative performance of classification models but also directly affects the reproducibility and translational value of candidate biomarkers. However, most existing methods primarily optimize classification accuracy and treat stability as a post hoc diagnostic, leading to considerable fluctuations in selected feature sets under different data splits or mild perturbations. Methods: To address this issue, this study proposes FRL-TSFS, a feature selection framework synergistically driven by filter-based Robust Rank Aggregation and L1-sparse modeling. Five complementary filter methods—variance thresholding, chi-square test, mutual information, ANOVA F test, and ReliefF—are first applied in parallel to score features, and Robust Rank Aggregation (RRA) is then used to obtain a consensus feature ranking that is less sensitive to the bias of any single scoring criterion. An L1-regularized logistic regression model is subsequently constructed on the candidate feature subset defined by the RRA ranking to achieve task-coupled sparse selection, thereby linking feature selection stability, feature compression, and classification performance. Results: FRL-TSFS was evaluated on six representative metabolomics and gene expression datasets under a mildly perturbed scenario induced by 10-fold cross-validation, and its performance was compared with multiple baselines using the Extended Kuncheva Index (EKI), Accuracy, and F1-score. The results show that RRA substantially improves ranking stability compared with conventional aggregation strategies without degrading classification performance, while the full FRL-TSFS framework consistently attains higher EKI values than the other feature selection schemes, markedly reduces the number of selected features to several tens of metabolites or genes, and maintains competitive classification performance. Conclusions: These findings indicate that FRL-TSFS can generate compact, reproducible, and interpretable biomarker panels, providing a practical analysis framework for stability-oriented feature selection and biomarker discovery in untargeted metabolomics. Full article

Background/Objectives: Differentiated thyroid cancer (DTC) represents over 90% of all hyroid malignancies and typically has a favorable prognosis. However, approximately 30% of patients experience recurrence within 10 years after initial treatment. Conventional risk classification frameworks such as the American Thyroid Association (ATA) and AJCC TNM systems rely heavily on pathological interpretation, which may introduce observer variability and incomplete documentation. This study aimed to develop an interpretable machine-learning framework for risk stratification in DTC and to identify major clinical predictors using SHapley Additive exPlanations (SHAP). Methods: A retrospective dataset of 345 patients was obtained from the UCI Machine Learning Repository. Thirteen clinicopathological features were analyzed, including Age, Gender, T, N, M, Hx Radiotherapy, Focality, Adenopathy, Pathology, and Response. Statistical analysis and feature selection (ReliefF and mRMR) were applied to identify the most influential variables. Two modeling scenarios were tested using an optimizable neural network classifier: (1) all 10 core features and (2) reduced features selected from machine learning criteria. SHAP analysis was used to explain model predictions and determine feature impact for each risk category. Results: Reducing the input features from 10 to 6 led to improved performance in the explainable neural network model (AUC = 0.94, accuracy = 92%), confirming that T, N, Response, Age, M, and Hx Radiotherapy were the most informative predictors. SHAP analysis highlighted N and T as the dominant drivers of high-risk classification, while Response enhanced postoperative biological interpretation. Notably, when Response was excluded (Scenario III), the optimizable tree model still achieved strong predictive performance (AUC = 0.93–0.96), demonstrating that accurate preoperative risk estimation can be achieved using only clinical baseline features. Conclusions: The proposed interpretable neural network model effectively stratifies recurrence risk in DTC while reducing dependence on subjective pathological interpretation. SHAP-based feature attribution enhances clinical transparency, supporting integration of explainable machine learning into thyroid cancer follow-up and personalized management. Full article

The leaf area index (LAI) is a key physiological parameter characterizing rice canopy structure and growth status. To face the limits of traditional destructive sampling, which is time-consuming, labor-intensive, and difficult to achieve large-scale dynamic detection, this study proposes a precise UAV-based hyperspectral inversion method for rice LAI using the fusion of Days After Transplantation and Meteorological Factors data (DATaMF). The study framework consisted of three key components: spectral preprocessing (smoothing-R_SG, resampling-R_RS, first derivative transformation-R_FD), spectral feature selection (SPA, CARS, Relief-F), and the construction and assessment of LAI inversion models (RF, ELM, XGBoost) that integrated DATaMF. The results show that (1) the three-level data preprocessing procedure—comprising R_SG, R_RS, and R_FD—coupled with the feature subset selected by the CARS method, demonstrates strong performance in LAI inversion; (2) the incorporation of DATaMF significantly improves rice LAI estimation, leading to improved model accuracy and robustness; and (3) the optimal LAI inversion model is achieved with the RF-based CARS-R_FD-DATaMF approach, yielding test set R², RMSE, and RPD values of 0.8015, 0.5745, and 2.2857, respectively. In conclusion, the hyperspectral LAI inversion method developed in this study, which integrates DATaMF, significantly enhances the model’s accuracy and stability under small-sample conditions. This approach provides reliable technical support for efficient, precise, and dynamic monitoring of rice growth. Full article

Earthquakes are sudden and destructive natural events caused by tectonic movements in the Earth’s crust. Although they cannot be predicted with certainty, rapid and reliable detection is essential to reduce loss of life and property. This study aims to automatically distinguish earthquake and noise signals from real seismic data by analyzing time-frequency features. Signals were scaled using z-score normalization, and extracted with Empirical Mode Decomposition (EMD), Discrete Wavelet Transform (DWT), and combined EMD+DWT methods. Feature selection methods such as Lasso, ReliefF, and Student’s t-test were applied to identify the most discriminative features. Classification was performed with Ensemble Bagged Trees, Decision Trees, Random Forest, k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM). The highest performance was achieved using the RF classifier with the Lasso-based EMD+DWT feature set, reaching 100% accuracy, specificity, and sensitivity. Overall, DWT and EMD+DWT features yielded higher performance than EMD alone. While k-NN and SVM were less effective, tree-based methods achieved superior results. Moreover, Lasso and ReliefF outperformed Student’s t-test. These findings show that time-frequency-based features are crucial for separating earthquake signals from noise and provide a basis for improving real-time detection. The study contributes to the academic literature and holds significant potential for integration into early warning and earthquake monitoring systems. Full article

Early and accurate detection of lemon disease is necessary for effective citrus crop management. Traditional approaches often lack refined diagnosis, necessitating more powerful solutions. The article introduces adaptive PSO-LemonNetX, a novel framework integrating a novel deep learning model, adaptive Particle Swarm Optimization (PSO)-based feature selection, and explainable AI (XAI) using LIME. The approach improves the accuracy of classification while also enhancing the explainability of the model. Our end-to-end model obtained 97.01% testing and 98.55% validation accuracy. Performance was enhanced further with adaptive PSO and conventional classifiers—100% validation accuracy using Naive Bayes and 98.8% testing accuracy using Naive Bayes and an SVM. The suggested PSO-based feature selection performed better than ReliefF, Kruskal–Wallis, and Chi-squared approaches. Due to its lightweight design and good performance, this approach can be adapted for edge devices in IoT-enabled smart farms, contributing to sustainable and automated disease detection systems. These results show the potential of integrating deep learning, PSO, grid search, and XAI into smart agriculture workflows for enhancing agricultural disease detection and decision-making. Full article

The accurate classification of rocks is crucial for applications such as earthquake prediction, resource exploration, and geological analysis. Traditional methods rely on expert examination of thin-section images under a microscope, making the process time-consuming and prone to errors. Recent advancements in deep learning have emerged as a powerful tool for automated rock classification; however, distinguishing between similar rock types such as sedimentary, metamorphic, and magmatic rocks remains a challenge. This study proposes a novel hybrid convolutional neural network (CNN) approach that combines the strengths of VGG16 and EfficientNetV2 architectures for the classification of thin-section rock images. The model, developed using the Feature-Selected Hybrid Network (FSHNet), demonstrates significant improvements over individual models, achieving a 5% increase in accuracy compared to Efficient-NetV2B0 and a 9% increase compared to VGG16. By employing the ReliefF algorithm for feature selection and Support Vector Machines (SVMs) for classification, the model further reduces the dimensionality of the feature space, enhancing computational efficiency. The proposed model has been applied to two different rock datasets. The first dataset consists of 2634 images, categorized into sedimentary, metamorphic, and magmatic rock classes. Additionally, the approach was tested on a second dataset comprising petrographic microfacies images, demonstrating its effectiveness in multiclass geological structure classification. Validation on both datasets shows that the proposed method outperforms popular deep learning models and previous studies, achieving a 3% increase in accuracy. These results highlight that the proposed approach provides a robust and efficient solution for automated rock classification, offering significant advancements for geological research and real-world applications. Full article

This study presents an advanced diagnostic methodology for assessing mechanical faults in high-performance jet engines using non-contact laser vibrometry and Fourier-based vi-bration analysis. Focusing on Pratt & Whitney F100-PW-229 engines used in F-16 aircraft, thise research identifies critical measurement locations, including the gearbox, turbine, and compressor supports. High-resolution vibration signals were collected under test bench conditions and processed using fFast Fourier tTransform (FFT) techniques to extract frequency-domain features indicative of rotor imbalances, bearing wear, and structural anomalies. Comparative analysis between nominal and degraded engines confirmed strong correlations between analytical predictions and empirical spectral patterns. Thise study introduces a signal processing framework combining time–frequency analysis with Relief-F-based feature selection, laying the groundwork for future integration with ma-chine learning algorithms. This non-intrusive, efficient diagnostic method supports early fault detection, enhances engine availability, and contributes to the development of a na-tional vibration reference database, especially vital in the absence of OEM-supplied tools. Full article

Reservoir evaporation is a vital component of the hydrological cycle and presents considerable challenges for sustainable water management, especially in arid and semi-arid regions. This study assesses the effectiveness of two Artificial Intelligence (AI) methods: Multilayer Perceptron (MLP) and Adaptive Neuro-Fuzzy Inference System (ANFIS), a combination ANN with fuzzy logic, in estimating daily evaporation from a large reservoir in a semi-arid region. Using eight years of hydrometeorological data from a nearby station, the study employed the ReliefF algorithm as a feature selection method for relevant input variables. The dataset was divided into training, validation, and testing subsets with 5% and 10% validation ratios, using four train–test splits of 70:30, 75:25, 80:20, and 85:15. Various training algorithms (e.g., Levenberg–Marquardt) and membership functions (e.g., generalized bell-shaped functions) were tested for both models. MLP consistently outperformed ANFIS on the test sets, showing higher R² and lower RMSE values. In the best-performing 70:30 split, MLP achieved an R² of 0.8069 and RMSE of 0.0923, compared to ANFIS with an R² of 0.3192 and RMSE of 0.2254. The findings highlight the AI-based approaches’ potential to support improved evaporation forecasting and integration into decision support tools for water resource planning amid changing climatic conditions. Full article

Introduction: Asthma is a chronic airway inflammatory disease characterized by variable airflow limitation and intermittent symptoms. In well-controlled asthma, auscultation and spirometry often appear normal, making diagnosis challenging. Moreover, bronchial provocation tests carry a risk of inducing acute bronchoconstriction. This study aimed to develop a non-invasive, objective, and reproducible diagnostic method using machine learning-based lung sound analysis for the early detection of asthma, even during stable periods. Methods: We designed a machine learning algorithm to classify controlled asthma patients and healthy individuals using respiratory sounds recorded with a digital stethoscope. We enrolled 120 participants (60 asthmatic, 60 healthy). Controlled asthma was defined according to Global Initiative for Asthma (GINA) criteria and was supported by normal spirometry, no pathological auscultation findings, and no exacerbations in the past three months. A total of 3600 respiratory sound segments (each 3 s long) were obtained by dividing 90 s recordings from 120 participants (60 asthmatic, 60 healthy) into non-overlapping clips. The samples were analyzed using Mel-Frequency Cepstral Coefficients (MFCCs) and Tunable Q-Factor Wavelet Transform (TQWT). Significant features selected with ReliefF were used to train Quadratic Support Vector Machine (SVM) and Narrow Neural Network (NNN) models. Results: In 120 participants, pulmonary function test (PFT) results in the asthma group showed lower FEV1 (86.9 ± 5.7%) and FEV1/FVC ratios (86.1 ± 8.8%) compared to controls, but remained within normal ranges. Quadratic SVM achieved 99.86% accuracy, correctly classifying 99.44% of controls and 99.89% of asthma cases. Narrow Neural Network achieved 99.63% accuracy. Sensitivity, specificity, and F1-scores exceeded 99%. Conclusion: This machine learning-based algorithm provides accurate asthma diagnosis, even in patients with normal spirometry and clinical findings, offering a non-invasive and efficient diagnostic tool. Full article