Interval-valued data (IVD) is a kind of data where each feature is an interval. The midpoint and boundary are the two commonly used methods for representing IVD. However, their structure information (such as location, size) may be incomplete because only midpoint or endpoint is adopted which will lead to poor results of data processing. To better depict the structural information of IVD, a unified representation frame (URF) for IVD is proposed. It not only takes into account the size and location information, but the relationship between them as well. This frame can also represent the midpoint and boundary methods in a unified way. Besides, symmetrical uncertainty (SU) is adopted to measure the relationship between features and classes quantitatively, and irrelevant features will be eliminated based on SU. The proposed URF_ SU is applied in some traditional classifiers like LIBSVM, CART Tree and KNN. The experimental results on synthetic and real-world datasets demonstrate that the proposed approach is more effective than other representation methods of IVD in classification tasks.

Accurate face recognition technology is of great significance for face anti-counterfeiting. Due to illumination, posture, angle, and other reasons, the existing face liveness detection technology is difficult to adapt the environmental changes, resulting in low detection accuracy. To address this issue, this paper presents a novel high-performance face anti-spoofing detection method named RGCS_ConvNeXt. The data-enhanced face images are fed into the ConvNext network, which group convolution is added to extract the correlation between different features, and the coordinate attention mechanism is used to enhance the facial feature extraction capability both spatially and directionally. Then SPPF is used to extract the features at different scales to enhance the representation of the feature map. Finally, the facial key point detection technique is utilized to calculate the eye EAR value to achieve accurate face anti-counterfeiting recognition. The proposed algorithm shows an average classification error rate of 0.3%, 1.7%, 1.9%±1.5% and 2.8%±3.4%, respectively, on the four protocols of the OULU-NPU public dataset. On the Siw dataset, the average classification error rate is 0.69%, a reduction of 0.02% compared to the MA-Net network. The half-error rate on the MSU-MFSD dataset is 2.39%, a 0.21% reduction compared to the DPCNN network. The algorithm shows good accuracy on the OULU-NPU, MSU-MFSD and Siw datasets, reaching 99.64%, 98.40% and 99.25% respectively, 0.26% higher than the SE-FeatherNet network’s average accuracy.

Diabetes is a chronic disease, which is characterized by abnormally high blood sugar levels. It may affect various organs and tissues, and even lead to life-threatening complications. Accurate prediction of diabetes can significantly reduce its incidence. However, the current prediction methods struggle to accurately capture the essential characteristics of nonlinear data, and the black-box nature of these methods hampers its clinical application. To address these challenges, we propose KCCAM_DNN, a diabetes prediction method that integrates Kendall’s correlation coefficient and an attention mechanism within a deep neural network. In the KCCAM_DNN, Kendall’s correlation coefficient is initially employed for feature selection, which effectively filters out key features influencing diabetes prediction. For missing values in the data, polynomial regression is utilized for imputation, ensuring data completeness. Subsequently, we construct a deep neural network (KCCAM_DNN) based on the self-attention mechanism, which assigns greater weight to crucial features affecting diabetes and enhances the model’s predictive performance. Finally, we employ the SHAP model to analyze the impact of each feature on diabetes prediction, augmenting the model’s interpretability. Experimental results show that KCCAM_DNN exhibits superior performance on both PIMA Indian and LMCH diabetes datasets, achieving test accuracies of 99.090% and 99.333%, respectively, approximately 2% higher than the best existing method. These results suggest that KCCAM_DNN is proficient in diabetes prediction, providing a foundation for informed decision-making in the diagnosis and prevention of diabetes.

The feature selection for interval-valued data (IVD) aims to identify representative features from a large set of features, which can reduce the model complexity, minimize the training time, and enhance the generalization ability of the model. Addressing the inter-feature correlations in IVD, we propose a feature selection method called the maximum information coefficient for interval-valued data (IVD_MIC). First, the method balances the relationship between the midpoint and radius of IVD with an adjustment factor, constructing the interval-valued data unified representation frame (URF). Based on the URF, the method measures the degree of correlation between two features by calculating the maximum information coefficient, and obtains the maximum information coefficient matrix for IVD. Then the features with strong correlation are progressively removed from three perspectives(row, column, and both row and column), generating a series of corresponding candidate feature subsets. Finally, IVD_MIC is validated on candidate feature subsets to obtain the final classification accuracy and optimal feature subset. The experiment results on synthetic and real-world datasets with different classifiers demonstrate that the overall performance of IVD_MIC surpasses other methods. The average accuracy of IVD_MIC is higher, improving by 0.23%, 0.53% and 0.45% compared to the second-best method on LIBSVM, CART Tree and KNN, respectively.

The precise identification of energy poor households is a critical step towards achieving the United Nations Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 1 (No Poverty), while also intersecting with climate action (SDG 13). As the world’s largest developing country, China faces unique energy poverty challenges characterized by significant regional disparities and uneven access to modern energy services. To support targeted interventions and equitable policy-making, this study proposes an explainable artificial intelligence (XAI) framework for predicting and interpreting energy poverty. Utilizing nationally representative data from the China Family Panel Studies (CFPS) from 2014 to 2020, we developed a predictive model that integrates a Convolutional Neural Network with SHapley Additive exPlanations (SHAP). Our model, EPPE-FCS, demonstrated exceptional predictive performance, achieving an average accuracy of 98.23%, outperforming several mainstream benchmarks. Crucially, the SHAP interpretability analysis revealed that annual per capita household expenditure is the most influential driver, while the contribution of energy burden indicators (electricity and gas expenses) exhibited a significant decreasing trend. This trend likely reflects the positive impact of China’s national policies, such as the “Clean Heating Initiative” and “Targeted Poverty Alleviation,” on improving energy infrastructure and affordability. The findings underscore the necessity of a dual-track policy that combines immediate energy cost subsidies with long-term strategies for income enhancement and clean energy transition. This research provides policymakers with a robust tool to alleviate energy poverty, thereby advancing a just, sustainable, and climate-resilient energy future in China and other developing regions.

Cardiovascular disease (CVD) is one of the leading causes of death worldwide, early and accurate prediction is crucial for reducing both incidence and mortality rates. Aiming at the problems of insufficient feature association mining and poor generalisation ability of statically weighted ensemble in existing models, this paper proposes a CVD prediction method based on the ensemble of cross-combination strategy and dynamic weighted stacking ensemble (CCS-DWSE). The method firstly constructs a heterogeneous base model through a full combination of multi-feature selection techniques and multi-paradigm classifiers. Next, it designs a dynamic weighted stacking ensemble framework that adjusts the base model weights in real time, and adaptively fuses the prediction results of the K-Nearest Neighbors (KNN) meta-model. Finally, SHAP-based interpretability analysis is employed to quantify the contribution of each feature. The experimental results show that CCS-DWSE achieves accuracies of 98.32%, 92.33%, and 82.10% on three public datasets, with an area under the curve (AUC) of up to 0.9992, representing a 2.76% improvement over the best existing models. This study provides an efficient and scalable solution for CVD prediction and offers a novel perspective for collaborative multi-model learning on complex medical data.