Search Results (2,691)

This study introduces a novel unsupervised framework, ICR-LLS, for detecting pathological gait patterns using instantaneous center of rotation (ICR) trajectories of the shank in the sagittal plane. ICR trajectories were computed from two-dimensional kinematic data captured at the lateral femoral epicondyle and lateral malleolus for both shanks, producing four-dimensional multivariate time series for each gait trial. Pairwise trajectory dissimilarities were quantified using circularly aligned Dynamic Time Warping (DTW), preserving temporal and spatial structure. The resulting dissimilarity matrix was embedded into a three-dimensional space using a force-directed network layout, enabling intuitive visualization of inter-subject gait relationships. Density-based clustering (DBSCAN), enhanced with a consensus-based ensemble approach, was employed to automatically identify clusters representing typical (healthy) gait patterns and outliers corresponding to pathological deviations. The framework is evaluated on a public dataset comprising individuals with Parkinson’s disease (PD) and healthy controls, achieving a normalized mutual information (NMI) of 0.449 and a Separation-to-Compactness Ratio (SCR) of 6.754, indicating a meaningful cluster structure. In addition, classification-oriented metrics yield an accuracy of 90%, sensitivity of 70%, and specificity of 96.7%, supporting the method’s effectiveness in distinguishing pathological gait. By combining minimal 2D kinematic inputs with unsupervised learning, ICR-LLS provides an interpretable framework for the exploratory analysis of gait variability, and although further validation is required, the findings suggest that ICR trajectories may serve as a meaningful biomechanical descriptor for characterizing pathological locomotion. Full article

The Internet of Medical Things (IoMT) is becoming a vital part of modern healthcare, enabling ongoing patient monitoring and remote diagnosis. However, as more devices connect to the internet, healthcare systems become more vulnerable to serious security issues such as unauthorized access, patient data manipulation, and Man-in-the-Middle attacks. Conventional Intrusion Detection Systems (IDSs) often struggle with the unclear and uncertain characteristics of IoMT traffic, which leads to reduced detection accuracy and increased false alarms. To address these challenges, this paper proposes ML-FSID-FIS, a multi-level feature selection-based Intrusion Detection System that employs a fuzzy inference system (FIS) for classification in IoMT networks. The model combines multiple feature selection techniques into a three-stage multi-level feature selection strategy to improve detection efficiency and strengthen the security of IoMT networks. In the first stage, four feature selection techniques—Random Forest, XGBoost, ReliefF, and Mutual Information—are applied to identify the most relevant features. In the second stage, a frequency-based consensus strategy is utilized to extract consistently selected features from the four top-ranked sets. In the third stage, an ensemble refinement using bagging-based ranking is employed to rank the remaining features, resulting in the selection of the top five features. From these, three candidate 3-feature groups are formed and evaluated, and the best-performing group is selected as the final input set for the fuzzy logic classifier. The FIS produces a continuous risk score that is mapped to a binary decision using a validation-selected threshold. When the proposed method was tested on the WUSTL-EHMS-2020 dataset and compared with other recent work using the same dataset, it showed strong detection performance while maintaining a very low false positive rate of 0.3%. This study is distinguished by its integrated design, which combines a three-stage multi-level feature selection strategy with fuzzy logic-based intrusion classification to improve feature efficiency and support interpretable intrusion detection in IoMT. Full article

This paper presents a reproducible system innovation for processing Digital Imaging and Communications in Medicine (DICOM) mammography images based on pixel-monotonic dynamic range management and engineering-verifiable intensity transformations. Standard DICOM conversion schemes to 8-bit representation often result in irreversible luminance-range compression, locality-dependent contrast distortions, and reduced robustness of deep learning models. The proposed framework preserves the physical consistency of the Modality LUT and photometric polarity, performs breast-aware robust Winsor normalization, and applies strictly monotonic global tone mapping while preserving the 16-bit depth of the training data. System validation was performed using architecture-independent metrics. Compared to standard processing, the median value of normalized mutual information increased from 0.878 to 0.892, the effective number of bits increased from 7.88 to 10.11 (+2.25), the representation entropy increased by 1.42 bits, and the clipping rate was reduced to almost zero. Experiments with the Faster R-CNN detector showed stable or improved calcification localization at Intersection over Union (IoU) ≥ 0.5 under controlled augmentation conditions. The results confirm that pixel-monotonic dynamic range control provides a reproducible, engineering-verifiable basis for AI-based mammography analysis within the evaluated dataset and experimental setting. Full article

Risk stratification of impaired glycemic control remains a major challenge in biomedical data analysis due to heterogeneous metabolic, behavioral, and therapeutic factors observed in large-scale populations. This study proposes a calibrated and interpretable decision–support framework, termed Calibrated Multi-Task Stacking Ensemble (CMSE), for joint modeling of clinically related glycemic outcomes. The framework integrates demographic variables, lipid profiles, renal and inflammatory biomarkers, dietary and smoking indicators, and therapy-related features within a unified predictive architecture. Robust modeling is ensured through leakage-aware preprocessing, quantile-based Winsorization, out-of-fold stacking, and isotonic calibration of probabilistic outputs. The physiological coherence between short-term and long-term glycemic markers is investigated using an explicit intertask coupling mechanism based on the estimated average glucose (eAG) ratio. Model interpretability is supported using SHAP analysis, mutual information, distance correlation, and feature importance metrics. In the primary medication-free screening configuration, the framework is evaluated on the NHANES 2017–March 2020 dataset, achieving ROC-AUC of 0.865 for diabetes classification and R² values of 0.385 and 0.366 for plasma glucose and HbA1c prediction, respectively. These results indicate that CMSE provides a reliable and explainable approach for calibrated glycemic risk assessment and clinical decision support. Full article

This study proposes an improved tree-augmented Bayesian network (TAN-BN) method for analyzing the severity of ship collision accidents by introducing the information contribution rate (ICR) for edge orientation and flexible filtering constraints for structure optimization. Based on 634 ship collision accident reports, a Bayesian network covering accident attributes and causal factors was constructed. The results show that the improved model achieved an overall AUC of 0.864, higher than that of the traditional TAN model (0.827). Mutual information analysis identified ship length as the factor most strongly associated with accident severity, with a mutual information value of 0.0868. Sensitivity analysis based on true risk impact (TRI) further showed that ship length, time, and ship type were the most influential factors, with average TRI values of 19.4%, 8.8%, and 7.2%, respectively. The proposed model effectively captures the dependency relationships between accident severity and multiple influencing factors and can provide quantitative support for risk warning and accident prevention in maritime traffic safety. Full article

Multimodal biomedical machine learning increasingly integrates heterogeneous data sources (including medical imaging, multi-omics profiles, electronic health records, and wearable sensor signals) to support clinical diagnosis, prognosis, and treatment response prediction. Despite strong empirical performance, most existing multimodal systems lack a principled theoretical foundation for understanding why fusion improves prediction, how information is distributed across modalities, and when models can be trusted under incomplete or shifting data. This paper develops a unified information-theoretic framework that formalizes multimodal biomedical learning as an information optimization problem. We formulate multimodal representation learning through the information bottleneck principle, deriving a variational objective that balances predictive sufficiency against informational compression in an architecture-agnostic manner. Building on this foundation, we introduce information-theoretic tools for decomposing modality contributions via conditional mutual information, quantifying redundancy and synergy, and diagnosing fusion collapse. We further show that robustness to missing modalities can be cast as an information consistency problem and extend the framework to longitudinal disease modeling through transfer entropy and sequential information bottleneck objectives. Applications to multimodal foundation models, uncertainty quantification, calibration, and out-of-distribution detection are developed. Empirical case studies across three biomedical datasets (TCGA breast cancer multi-omics, TCGA glioma clinical-plus-molecular data, and OASIS-2 longitudinal Alzheimer’s data) show that the framework’s key quantities are computable and interpretable on real data: MI decomposition identifies modality dominance and redundancy; the VMIB traces a compression–prediction tradeoff in the information plane; entropy-based selective prediction raises accuracy from 0.787 to 0.939 at 50% coverage; transfer entropy reveals stage-dependent modality influence in disease progression; and pretraining/adaptation diagnostics distinguish efficient from wasteful fine-tuning strategies. Together, these results develop entropy and mutual information as organizing principles for the design, analysis, and evaluation of multimodal biomedical AI systems. Full article

Against the backdrop of the global energy structure accelerating its transition towards a clean and low-carbon model, rooftop-distributed photovoltaic (PV) systems are playing an increasingly prominent strategic role in urban energy supply systems, owing to their notable advantages such as environmental friendliness and high spatial utilization efficiency. Consequently, they are becoming a critical pillar in advancing urban energy transformation and enhancing sustainable development. This paper aims to explore deep learning-based techniques for assessing the potential of large-scale distributed PV installations. To accurately evaluate their dynamic power generation capability, a hybrid prediction model integrating variational mode decomposition (VMD), the mutual information (MI) method, and a cascaded xLSTM-Informer network is proposed. Firstly, the model preprocesses key meteorological sequences using VMD, decomposing them into modal components of different frequencies. Subsequently, the MI method is employed to extract critical sequences. Then, the xLSTM module is utilized to learn the long-term complex dependencies between meteorological conditions and PV power output, while the Informer network captures key global temporal patterns, achieving high-precision time-series forecasting of PV generation. Finally, employing the forecasted time-series power curve as the core input, a comprehensive analytical framework for PV installation potential is constructed, integrating assessments of technical feasibility, economic viability, and environmental performance. This framework aims to scientifically estimate the admissible installed capacity and system value of distributed PV systems, thereby providing a dynamic basis for decision-making in urban planning. Full article

Efficient extraction of building footprints from aerial and satellite imagery is essential for urban planning, infrastructure management, and large-scale geospatial analysis. Traditional raster-based approaches provide limited geometric precision, while existing polygon-generation methods often rely on detecting and ordering small-scale building vertices, which can lead to incomplete structures, distorted shapes, and high computational cost. To address these limitations, this study proposes an Edge-Attentive Dual-Branch Frame Field Network (EA-DBFFN) for automated and high-precision building polygon extraction. The method is built upon frame field learning and introduces a dual-branch architecture that separately predicts building masks and edges. A Dual-Task Decoder enlarges and adapts receptive fields while applying spatial attention to enhance the representation of structural details. Fixed Sobel and Laplacian filters are incorporated to strengthen boundary detection. In addition, a Dual-Task Mutual Guidance Module promotes the exchange of complementary information between the mask and edge branches, improving geometric consistency and reducing boundary errors. Experiments conducted on the Inria Aerial dataset and the CrowdAI dataset demonstrate that EA-DBFFN achieves superior performance in region-based metrics, with an AP75 of 72.9% on CrowdAI, representing a 2.3% improvement over competing methods. Furthermore, EA-DBFFN produces geometrically higher-quality polygons, with the Max Tangent Angle error reduced by 6.4%, the Invalid Polygon Ratio reduced by 66.3%, and Edge Smoothness improved by 72.7% compared to the best competing method. The results show that EA-DBFFN provides an effective and computationally efficient framework for generating high-quality vectorized building footprints suitable for large-scale urban analysis. Full article

Private set intersection (PSI) is a fundamental cryptographic task that allows two mutually distrusting parties, each holding a private set of elements, to jointly compute the intersection of their sets. It ensures a symmetric information structure where neither party gains any knowledge about the other’s elements beyond those in the shared intersection. Traditional PSI protocols are primarily designed for static settings, which limits their applicability and efficiency in dynamic scenarios where input sets continuously evolve. To address this challenge, the notion of updatable PSI (UPSI) was introduced, enabling repeated PSI computations over changing inputs while preserving the symmetric privacy guarantees between participants. Despite the numerous recent advancements in UPSI research, it still suffers from significant communication overhead. In this paper, we address this challenge by introducing LcUPSI (low-communication UPSI), a new updatable PSI protocol that achieves remarkably low communication overhead. We formally prove that LcUPSI is secure in the semi-honest model. Furthermore, we compare the LcUPSI protocol with the state-of-the-art UPSI protocol BMSTZ24 (ASIACRYPT). The results demonstrate that LcUPSI significantly reduces communication overhead, highlighting its advantages in low-bandwidth conditions. Full article

Sub-pixel matching of multimodal optical images is a critical step in the combined application of multiple sensors. However, structural noise and inconsistencies arising from variations in multimodal image responses usually limit the accuracy of matching. Phase congruency mutual-structure weighted least absolute deviation (PCWLAD) is developed as a coarse-to-fine framework. In the coarse matching stage, we preserve the complete structure and use an enhanced cross-modal similarity criterion to mitigate structural information loss by phase congruency (PC) noise filtering. In the fine matching stage, a mutual-structure filtering and weighted least absolute deviation-based method is introduced to enhance inter-modal structural consistency and to accurately estimate sub-pixel displacements adaptively. Experiments on three multimodal datasets—Landsat visible-infrared, short-range visible-near-infrared, and unmanned aerial vehicle (UAV) optical image pairs—show that PCWLAD achieves superior average performance compared with eight state-of-the-art methods, attaining an average matching accuracy of approximately 0.4 pixels. Full article

To unveil the underlying risk structures in complex industrial systems, this paper proposes a hybrid analytical framework that integrates BERTopic modeling, a large language model (LLM), and social network analysis (SNA). This framework aims to extract systemic safety intelligence from unstructured accident reports. It first employs BERTopic to identify latent causal topics based on 745 Chinese accident investigation reports and utilizes DeepSeek-V3.1 (LLM) for semantic refinement and causal mapping of these topics. Subsequently, a semantic network of causal keywords based on positive pointwise mutual information (PPMI) is constructed, and its topological structure is analyzed using SNA methods. The study identifies and analyzes five major risk communities: confined spaces, fire, mining, construction, and road traffic. It reveals that accident causation exhibits the small-world characteristics of multi-factor coupling and non-linearity, with core risk nodes concentrated in systemic inducements such as organizational management and compliance deficiencies. The results demonstrate that this framework effectively identifies the latent systemic risk patterns embedded within the texts, providing methodological support for developing sustainable safety management mechanisms based on design for safety. Full article

Feature selection is essential for high-dimensional multi-source feature analysis, particularly in Internet of Things (IoT) environments characterized by data heterogeneity, redundancy, and noise. To address the need to balance classification performance, dimensionality reduction, and selection stability, this study proposes a residual-based conditional mutual information and feedback fusion (RCMF) feature-selection method. Inspired by the idea of conditional mutual information, the proposed method first introduces a residual-based indicator to characterize the incremental discriminative information retained by a candidate feature under given conditional constraints. On this basis, model-driven predictive contribution and stability score are further incorporated, and the weights of different evaluation components are iteratively updated during the feature-selection process to achieve adaptive fusion. In this way, the method jointly considers conditional discriminative information, task relevance, and selection consistency within a unified feature-evaluation procedure. Experiments on multiple publicly available benchmark and IoT-related datasets validate the rationality and effectiveness of the proposed method. Full article

With the rapid growth of data, feature selection has become essential for improving machine learning performance. However, most existing unsupervised feature selection methods rely on greedy strategies, which often lead to suboptimal solutions. Moreover, traditional information–theoretic approaches are primarily designed for discrete data and require discretization when applied to continuous data, potentially causing information loss. To address these issues, this paper proposes a global unsupervised feature selection method based on fuzzy mutual information (UFS-FMI). The proposed method integrates fuzzy set theory with information measures to quantify feature relevance and redundancy, and formulates a fractional optimization model. A combination of projection neural networks and kWTA neural networks is employed to achieve global optimization. Experimental results on nine UCI benchmark datasets demonstrate that UFS-FMI consistently outperforms several representative methods in terms of classification accuracy, clustering accuracy, and normalized mutual information (NMI). In particular, on datasets such as Movement_libras, Ionosphere, and Control, the proposed method achieves significantly improved classification performance, confirming its effectiveness and robustness. Full article

In the era of information explosion, the effective configuration of labels on maps is crucial for the rapid comprehension of information. The point-feature label placement problem, particularly in large-scale and high-density scenarios with spatial mutual-exclusion constraints, is a classic NP-hard discrete optimization challenge. Existing metaheuristic algorithms (e.g., Simulated Annealing and Genetic Algorithm) often struggle to achieve high-quality global layouts due to their propensity to become trapped in local optima, inefficient random point-selection processes, and inadequate modeling of the spatial mutual-exclusion and blocking constraints between labels. To address these limitations, this paper proposes a Progressive Reinforcement Learning (PRL) algorithm specifically tailored for the point-feature label placement problem. The algorithm models the label placement process as a sequential decision-making problem within the Reinforcement Learning framework, optimized through agent–environment interaction. Its core design comprises the following: (1) a staircase-like policy learning mechanism that shifts from “broad exploration in the early stage to precise exploitation in the later stage” to balance global search and local optimization; (2) a data mining-based Intelligent Action Screening (IAS) mechanism, which dynamically identifies and prioritizes “high-value action points” critical for improving layout quality by constructing the “Contribution Decline Degree” and “Contribution Support Degree” metrics. Experiments on large-scale real-world POI datasets (10,000, 20,000, and 32,312 points) demonstrate that the proposed algorithm significantly outperforms 13 state-of-the-art comparative algorithms, including Simulated Annealing, Genetic Algorithm, Differential Evolution, POPMUSIC, and DBSCAN, in terms of both placement quality and the number of successfully placed labels. It exhibits remarkable adaptability and competitiveness in handling high-density and complex scenarios. Full article

With the proliferation of edge computing in IoT and smart security, there is a growing demand for large-scale encrypted traffic anomaly detection. However, the opaque nature of encrypted traffic makes it difficult for traditional detection methods to balance efficiency and accuracy. To address this challenge, this paper proposes FMTF, a Multi-Scale Feature Fusion method based on Federated Learning for encrypted traffic anomaly detection. FMTF constructs graph structures at three scales—spatial, statistical, and content—to comprehensively characterize traffic features. At the spatial scale, communication graphs are constructed based on host-to-host IP interactions, where each node represents the IP address of a host and edges capture the communication relationships between them. The statistical scale builds traffic statistic graphs based on interactions between port numbers, with nodes representing individual ports and edge weights corresponding to the lengths of transmitted packets. At the content scale, byte-level traffic graphs are generated, where nodes represent pairs of bytes extracted from the traffic data, and edges are weighted using pointwise mutual information (PMI) to reflect the statistical association between byte occurrences. To extract and fuse these multi-scale features, FMTF employs the Graph Attention Network (GAT), enhancing the model’s traffic representation capability. Furthermore, to reduce raw-data exposure in distributed edge environments, FMTF integrates a federated learning framework. In this framework, edge devices train models locally based on their multi-scale traffic features and periodically share model parameters with a central server for aggregation, thereby optimizing the global model without exposing raw data. Experimental results demonstrate that FMTF maintains efficient and accurate anomaly detection performance even under limited computing resources, offering a practical and effective solution for encrypted traffic identification and network security protection in edge computing environments. Full article