Search Results (795)

Micro-expressions, characterized by brief and subtle facial muscle movements, are essential for conveying nuanced emotions in digital humans, yet existing rendering techniques often produce rigid or emotionally monotonous animations due to the inadequate modeling of temporal dynamics and action unit interdependencies. This paper proposes a graph-driven framework for micro-expression rendering that generates emotionally diverse and lifelike expressions. We employ a 3D-ResNet-18 backbone network to perform joint spatio-temporal feature extraction from facial video sequences, enhancing sensitivity to transient motion cues. Action units (AUs) are modeled as nodes in a symmetric graph, with edge weights derived from empirical co-occurrence probabilities and processed via a graph convolutional network to capture structural dependencies and symmetric interactions. This symmetry is justified by the inherent bilateral nature of human facial anatomy, where AU relationships are based on co-occurrence and facial anatomy analysis (as per the FACS), which are typically undirected and symmetric. Human faces are symmetric, and such relationships align with the design of classic spectral GCNs for undirected graphs, assuming that adjacency matrices are symmetric to model non-directional co-occurrences effectively. Predicted AU activations and timestamps are interpolated into continuous motion curves using B-spline functions and mapped to skeletal controls within a real-time animation pipeline (Unreal Engine). Experiments on the CASME II dataset demonstrate superior performance, achieving an F1-score of 77.93% and an accuracy of 84.80% (k-fold cross-validation, k = 5), outperforming baselines in temporal segmentation. Subjective evaluations confirm that the rendered digital human exhibits improvements in perceptual clarity, naturalness, and realism. This approach bridges micro-expression recognition and high-fidelity facial animation, enabling more expressive virtual interactions through curve extraction from AU values and timestamps. Full article

Efficient labor productivity forecasting is a critical challenge in construction engineering, directly influencing scheduling, cost control, and resource allocation. In reinforced concrete projects, accurate prediction of rebar-fixing productivity enables managers to optimize workforce deployment and mitigate delays. This study proposes a machine learning-based framework to forecast rebar-fixing labor productivity under varying site and environmental conditions. Four regression algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN)—were trained, tuned, and validated using grid search with k-fold cross-validation. RF achieved the highest accuracy, with an $R^2$ of 0.901 and RMSE of 19.94 on the training set and an $R^2$ of 0.877 and RMSE of 22.47 on the test set, indicating strong generalization. Model interpretability was provided through SHapley Additive exPlanations (SHAP), revealing that larger quantities of M32 and M25 rebars increased productivity, while higher temperatures reduced it, likely due to lower labor efficiency. Humidity, wind speed, and precipitation showed minimal influence. The integration of accurate predictive modeling with explainable machine learning offers practical insights for project managers, supporting data-driven decisions to enhance reinforcement task efficiency in diverse construction environments. Full article

The persistent issue of unemployment and the mismatch between graduate skills and labor market demands has drawn increasing attention from academics and educational institutions, especially in light of rapid advancements in technology. Emerging technologies such as Artificial Intelligence (AI) and Machine Learning (ML) offer valuable opportunities to analyze job market dynamics. In this work, we present a novel framework aimed at predicting graduate employability using current labor market data from Morocco. Our approach combines Multi-Criteria Decision-Making (MCDM) techniques with ML-based predictive models. AHP prioritizes employability factors and TOPSIS ranks skill demands—together forming input features for machine learning models. 2100 job listings obtained through web scraping, we trained and evaluated several ML models. Among them, the K-Nearest Neighbors (KNN) classifier demonstrated the highest accuracy, achieving 99.71% accuracy through 5-fold cross-validation. While the study is based on a limited dataset, it highlights the practical relevance of combining MCDM methods with ML for employability prediction. This study is among the first to integrate AHP–TOPSIS with KNN for employability prediction using real-time Moroccan labor market data. Full article

Epidemics caused by respiratory infections have become a global and systemic threat since humankind has become highly connected via modern transportation systems. Any new pathogen with human-to-human transmission capabilities has the potential to cause public health disasters and severe disruptions of social and economic activities. During the COVID-19 pandemic, we learned that proper mask-wearing in closed, restricted areas was one of the measures that worked to mitigate the spread of respiratory infections while allowing for continuing economic activity. Previous research approached this issue by designing hardware–software systems that determine whether individuals in the surveilled restricted area are using a mask; however, most such solutions are centralized, thus requiring massive computational resources, which makes them hard to scale up. To address such issues, this paper proposes a novel decentralized, federated learning (FL) solution to mask-wearing detection that instantiates our lightweight version of the MobileNetV2 model. The FL solution also ensures individual privacy, given that images remain at the local, device level. Importantly, we obtained a mask-wearing training accuracy of 98% (i.e., similar to centralized machine learning solutions) after only eight rounds of communication with 25 clients. We rigorously proved the reliability and robustness of our approach after repeated K-fold cross-validation. Full article

Controlling dissolved nitrogen is critical to meeting increasingly stringent steel quality targets, yet the variable kinetics of gas absorption and removal across production stages complicate real-time decision-making. Leveraging a total of 291 metal samples, the research applied ordinary least squares (OLS) regression, enhanced by cointegration diagnostics, to develop four stage-specific models covering pig iron after desulfurization, crude steel in the basic oxygen furnace (BOF) before tapping, steel at the beginning and end of secondary metallurgy processing. Predictor selection combined thermodynamic reasoning and correlation analysis to produce prediction equations that passed heteroscedasticity, normality, autocorrelation, collinearity, and graphical residual distribution tests. The k-fold cross-validation method was also used to evaluate models’ performance. The models achieved an adequate accuracy of 77.23–83.46% for their respective stages. These findings demonstrate that statistically robust and physically interpretable regressions can capture the complex interplay between kinetics and the various processes that govern nitrogen pick-up and removal. All data are from U. S. Steel Košice, Slovakia; thus, the models capture specific setup, raw materials, and production practices. After adaptation within the knowledge transfer, implementing these models in process control systems could enable proactive parameter optimization and reduce laboratory delays, ultimately minimizing excessive nitrogenation in finished steel. Full article

Background: Diabetes remains a major global health challenge, contributing significantly to premature mortality due to its potential progression to organ failure if not diagnosed early. Traditional diagnostic approaches are subject to human error, highlighting the need for modern computational techniques in clinical decision support systems. Although these systems have successfully integrated deep learning (DL) models, they still encounter several challenges, such as a lack of intricate pattern learning, imbalanced datasets, and poor interpretability of predictions. Methods: To address these issues, the temporal inception perceptron network (TIPNet), a novel DL model, is designed to accurately predict diabetes by capturing complex feature relationships and temporal dynamics. An adaptive synthetic oversampling strategy is utilized to reduce severe class imbalance in an extensive diabetes health indicators dataset consisting of 253,680 instances and 22 features, providing a diverse and representative sample for model evaluation. The model’s performance and generalizability are assessed using a 10-fold cross-validation technique. To enhance interpretability, explainable artificial intelligence techniques are integrated, including local interpretable model-agnostic explanations and Shapley additive explanations, providing insights into the model’s decision-making process. Results: Experimental results demonstrate that TIPNet achieves improvement scores of 3.53% in accuracy, 3.49% in F1-score, 1.14% in recall, and 5.95% in the area under the receiver operating characteristic curve. Conclusions: These findings indicate that TIPNet is a promising tool for early diabetes prediction, offering accurate and interpretable results. The integration of advanced DL modeling with oversampling strategies and explainable AI techniques positions TIPNet as a valuable resource for clinical decision support, paving the way for its future application in healthcare settings. Full article

This study proposes a novel, scalable framework for the automated classification and synthesis of survey literature by integrating state-of-the-art Large Language Models (LLMs) with robust ensemble voting techniques. The framework consolidates predictions from three independent models—GPT-4, LLaMA 3.3, and Claude 3—to generate consensus-based classifications, thereby enhancing reliability and mitigating individual model biases. We demonstrate the generalizability of our approach through comprehensive evaluation on two distinct domains: Question Answering (QA) systems and Computer Vision (CV) survey literature, using a dataset of 1154 real papers extracted from arXiv. Comprehensive visual evaluation tools, including distribution charts, heatmaps, confusion matrices, and statistical validation metrics, are employed to rigorously assess model performance and inter-model agreement. The framework incorporates advanced statistical measures, including k-fold cross-validation, Fleiss’ kappa for inter-rater reliability, and chi-square tests for independence to validate classification robustness. Extensive experimental evaluations demonstrate that this ensemble approach achieves superior performance compared to individual models, with accuracy improvements of 10.0% over the best single model on QA literature and 10.9% on CV literature. Furthermore, comprehensive cost–benefit analysis reveals that our automated approach reduces manual literature synthesis time by 95% while maintaining high classification accuracy (F1-score: 0.89 for QA, 0.87 for CV), making it a practical solution for large-scale literature analysis. The methodology effectively uncovers emerging research trends and persistent challenges across domains, providing researchers with powerful tools for continuous literature monitoring and informed decision-making in rapidly evolving scientific fields. Full article

The increasing demand for energy efficiency in manufacturing has driven the need for advanced modeling techniques to optimize power consumption in machining processes. This study presents a novel approach to modeling power consumption in milling processes using machine learning, leveraging a custom-designed braking device integrated into the milling machine’s main spindle to measure friction forces with high precision. A comprehensive dataset of observations, including parameters such as speed, force, intensity, apparent power, active power, and power factor, was collected under loaded conditions. Nine machine learning models—Linear Regression, Random Forest, Support Vector Regression, Polynomial Regression, Multi-Layer Perceptron with 2 and 3 layers, K-Nearest Neighbors, Bagging, and a hybrid Probabilistic Random Forest—Multi-Layer Perceptron (PIRF–MLP)—were evaluated using 5-fold cross-validation to ensure robust performance assessment. The PIRF–MLP model achieved the highest performance, demonstrating superior accuracy in predicting utile power. The feature importance analysis revealed that force and speed significantly influence power consumption. The proposed methodology, validated on a milling machine, offers a scalable solution for real-time energy monitoring and optimization in machining, contributing to sustainable manufacturing practices. Future work will focus on expanding the dataset and testing the models across diverse machining conditions to enhance generalizability. Full article

A miniaturised bionic electronic nose system was developed to solve the problems of expensive equipment and long response time for soil pesticide residue detection. The structure of the bionic electronic nasal cavity is designed based on the spatial structure and olfactory principle of the sturgeon nasal cavity. Through experimental study, the structure of the nasal cavity of the sturgeon was extracted and analyzed. The 3D model of the bionic electronic nasal cavity was constructed and verified by Computational Fluid Dynamics (CFD) simulation. The results show that the gas flow distribution in the bionic chamber is more uniform than that in the ordinary chamber. The airflow velocity near the sensor in the bionic chamber is lower than in the ordinary chamber. The eddy current intensity near the bionic chamber sensor is 2.29 times that of the ordinary chamber, further increasing the contact intensity between odor molecules and the sensor surface and shortening the response time. The 10-fold cross-validation method of K-Nearest Neighbor (K-NN), Random Forest (RF) and Support Vector Machine (SVM) was used to compare the recognition performance of the bionic electronic nasal cavity with that of the ordinary electronic nasal cavity. The results showed that, when the bionic electronic nose detection system identified the concentration of pesticide residues in soil, the recognition rate of the above three recognition algorithms reached 97.3%, significantly higher than that of the comparison chamber. The bionic chamber electronic nose system can improve the detection performance of electronic noses and has a good application prospect in soil pesticide residue detection. Full article

Sustainable product development demands components that last longer, consume less energy, and can be refurbished within circular supply chains. This study introduces a digital replica-based predictive prototyping workflow for industrial crusher blades that meets these goals. Six commercially used blade geometries (A–F) were recreated as high-fidelity finite-element models and subjected to an identical 5 kN cutting load. Comparative simulations revealed that a triple-edged hooked profile (Blade A) reduced peak von Mises stress by 53% and total deformation by 71% compared with a conventional flat blade, indicating lower drive-motor power and slower wear. To enable fast virtual prototyping and condition-based maintenance, deformation was subsequently predicted using a data-efficient machine-learning model. Multi-view image augmentation enlarged the experimental dataset from 6 to 60 samples, and an XGBoost regressor, trained on computer-vision geometry features and engineering parameters, achieved R² = 0.996 and MAE = 0.005 mm in five-fold cross-validation. Feature-importance analysis highlighted applied stress, safety factor, and edge design as the dominant predictors. The integrated method reduces development cycles, reduces material loss via iteration, extends the life of blades, and facilitates refurbishment decisions, providing a foundation for future integration into digital twin systems to support sustainable product development and predictive maintenance in heavy-duty manufacturing. Full article

Shallow foundations are widely used in both terrestrial and marine environments, supporting critical structures such as buildings, offshore wind turbines, subsea platforms, and infrastructure in coastal zones, including piers, seawalls, and coastal defense systems. Accurately determining the soil bearing capacity for shallow foundations presents a significant challenge, as it necessitates considerable resources in terms of materials and testing equipment, as well as a substantial amount of time to perform the necessary evaluations. Consequently, our research was designed to approximate the forecasting of soil bearing capacity for shallow foundations using machine learning algorithms. In our research, four ensemble machine learning algorithms were employed for the prediction process, benefiting from previous experimental tests. Those four models were AdaBoost, Extreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRTs), and Light Gradient Boosting Machine (LightGBM). To enhance the model’s efficacy and identify the optimal hyperparameters, grid search was conducted in conjunction with k-fold cross-validation for each model. The models were evaluated using the R² value, MAE, and RMSE. After evaluation, the R² values were between 0.817 and 0.849, where the GBRT model predicted more accurately than other models in training, testing, and combined datasets. Moreover, variable importance was analyzed to check which parameter is more important. Foundation width was the most important parameter affecting the shallow foundation bearing capacity. The findings obtained from the refined machine learning approach were compared with the well-known empirical and modern machine learning equations. In the end, the study designed a web application that helps geotechnical engineers from all over the world determine the ultimate bearing capacity of shallow foundations. Full article

Diabetes, a chronic condition caused by insufficient insulin production in the pancreas, presents significant health risks. Its increasing global prevalence necessitates the development of accurate and efficient predictive algorithms to support timely diagnosis. While recent advancements in deep learning (DL) have demonstrated potential for diabetes prediction, conventional models face limitations in handling class imbalance, capturing complex feature interactions, and providing interpretability for clinical decision-making. This paper proposes a DL framework for diabetes mellitus prediction. The framework ensures high predictive accuracy by integrating advanced preprocessing, effective class balancing, and a novel EchoceptionNet model. An analysis was conducted on a diabetes prediction dataset obtained from Kaggle, comprising nine features and 100,000 instances. The dataset is characterized by severe class imbalance, which is effectively addressed using a proximity-weighted synthetic oversampling technique, ensuring balanced class distribution. EchoceptionNet demonstrated notable performance improvements over state-of-the-art deep learning models, achieving a 4.39% increase in accuracy, 8.99% in precision, 2.19% in recall, 5.55% in F1-score, and a 7.77% in area under the curve score. Model robustness and generalizability were validated through 10-fold cross-validation, demonstrating consistent performance across diverse data splitting. To enhance clinical applicability, EchoceptionNet integrates explainable artificial intelligence techniques, Shapley additive explanations, and local interpretable model-agnostic explanations. These methods provide transparency by identifying the critical importance of features in the model’s predictions. EchoceptionNet exhibits superior predictive accuracy and ensures interpretability and reliability, making it a robust solution for accurate diabetes prediction. Full article

Grain boundary segmentation in scanning electron microscope (SEM) images of pure copper presents substantial challenges for traditional image processing methods, including constrained segmentation precision and difficulties in identifying elongated grain boundaries and intricate topological structures. To overcome these constraints, this research introduces a comprehensive framework that integrates dataset development, advanced data augmentation, and model optimization to achieve precise grain boundary segmentation. This work proposes three principal innovations. First, a meticulously curated small-scale dataset, combined with a sophisticated adaptive data augmentation strategy, addresses data scarcity and ensures high-quality, robust training data. Second, the U-Net model was refined by incorporating a self-attention mechanism, markedly enhancing its capability to capture global contextual information and accurately detect complex grain boundary features. Third, an optimized stratified K-fold cross-validation method was implemented to ensure equitable data partitioning and reduce overfitting, thereby strengthening the model’s generalization capability. Experimental results demonstrate that the proposed framework delivers exceptional performance on the validation dataset, achieving a global accuracy of 0.96, a Dice coefficient of 0.91, and a mean Intersection over Union (mIoU) of 0.85. These metrics underscore significant advancements in grain boundary segmentation precision for polycrystalline metal systems. The framework validates the power of deep learning in microstructural characterization and establishes a reliable computational tool for quantitative metallographic analysis. It is well-positioned to extend to the microstructural analysis of a broad range of heterogeneous materials, enabling deeper insights into microstructure–property relationships in materials engineering. Full article

We have developed a generalisable machine learning framework for reservoir quality prediction in deeply buried clastic systems. Applied to the Lower Jurassic deltaic sandstones of the Tilje Formation (Halten Terrace, North Sea), the approach integrates sedimentological facies modelling with mineralogical and petrophysical prediction in a single workflow. Using supervised Extreme Gradient Boosting (XGBoost) models, we classify reservoir facies, predict permeability directly from standard wireline log parameters and estimate the abundance of porosity-preserving grain coating chlorite (gamma ray, neutron porosity, caliper, photoelectric effect, bulk density, compressional and shear sonic, and deep resistivity). Model development and evaluation employed stratified K-fold cross-validation to preserve facies proportions and mineralogical variability across folds, supporting robust performance assessment and testing generalisability across a geologically heterogeneous dataset. Core description, point count petrography, and core plug analyses were used for ground truthing. The models distinguish chlorite-associated facies with up to 80% accuracy and estimate permeability with a mean absolute error of 0.782 log(mD), improving substantially on conventional regression-based approaches. The models also enable prediction, for the first time using wireline logs, grain-coating chlorite abundance with a mean absolute error of 1.79% (range 0–16%). The framework takes advantage of diagnostic petrophysical responses associated with chlorite and high porosity, yielding geologically consistent and interpretable results. It addresses persistent challenges in characterising thinly bedded, heterogeneous intervals beyond the resolution of traditional methods and is transferable to other clastic reservoirs, including those considered for carbon storage and geothermal applications. The workflow supports cost-effective, high-confidence subsurface characterisation and contributes a flexible methodology for future work at the interface of geoscience and machine learning. Full article

Accurate nitrogen (N) content estimation in grassland vegetation is essential for ecosystem health and optimizing pasture quality, as N supports plant photosynthesis and water uptake. Traditional lab methods are slow and unsuitable for large-scale monitoring, while remote sensing models often face accuracy challenges due to hyperspectral data complexity. This study improves N content estimation in the typical steppe of Inner Mongolia by integrating hyperspectral remote sensing with advanced machine learning. Hyperspectral reflectance from Leymus chinensis and Cleistogenes squarrosa was measured using an ASD FieldSpec-4 spectrometer, and leaf N content was measured with an elemental analyzer. To address high-dimensional data, four spectral transformations—band combination, first-order derivative transformation (FDT), continuous wavelet transformation (CWT), and continuum removal transformation (CRT)—were applied, with Least Absolute Shrinkage and Selection Operator (LASSO) used for feature selection. Four machine learning models—Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nearest Neighbors (KNN)—were evaluated via five-fold cross-validation. Wavelet transformation provided the most informative parameters. The SVM model achieved the highest accuracy for L. chinensis (R² = 0.92), and the ANN model performed best for C. squarrosa (R² = 0.72). This study demonstrates that integrating wavelet transform with machine learning offers a reliable, scalable approach for grassland N monitoring and management. Full article