Search Results (234)

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with diverse behavioral, genetic, and structural characteristics. Due to its heterogeneous nature, early diagnosis of ASD is challenging, and conventional unimodal approaches often fail to capture cross-modal dependencies. To address this, this study introduces an adaptive multimodal fusion framework that integrates behavioral, genetic, and structural MRI (sMRI) data, addressing the limitations of unimodal approaches. Each modality undergoes a dedicated preprocessing and feature optimization phase. For behavioral data, an ensemble of classifiers using a stacking technique and attention mechanism is applied for feature extraction, achieving an accuracy of 95.5%. The genetic data is analyzed using Gradient Boosting, which attained a classification accuracy of 86.6%. For the sMRI data, a Hybrid Convolutional Neural Network–Graph Neural Network (Hybrid-CNN-GNN) architecture is proposed, demonstrating a strong performance with an accuracy of 96.32%, surpassing existing methods. To unify these modalities, fused using an adaptive late fusion strategy implemented with a Multilayer Perceptron (MLP), where adaptive weighting adjusts each modality’s contribution based on validation performance. The integrated framework addresses the limitations of unimodal approaches by creating a unified diagnostic model. The transfer learning framework achieves superior diagnostic accuracy (98.7%) compared to unimodal baselines, demonstrating strong generalization across heterogeneous datasets and offering a promising step toward reliable, multimodal ASD diagnosis. Full article

Emerging complex multi-step attacks such as Advanced Persistent Threats (APTs) pose significant risks to national economic development, security, and social stability. Effectively detecting these sophisticated threats is a critical challenge. While deep learning methods show promise in identifying unknown malicious behaviors, they often struggle with fragmented modal information, limited feature representation, and generalization. To address these limitations, we propose GATransformer, a new dual-modal detection method that integrates topological structure analysis with temporal sequence modeling. Its core lies in a cross-attention semantic fusion mechanism, which deeply integrates heterogeneous features and effectively mitigates the constraints of unimodal representations. GATransformer reconstructs network behavior representation via a parallel processing framework in which graph attention captures intricate spatial dependencies, and self-attention focuses on modeling long-range temporal correlations. Experimental results on the CIDDS-001 and CIDDS-002 datasets demonstrate the superior performance of our method compared to baseline methods with detection accuracies of 99.74% (nodes) and 88.28% (edges) on CIDDS-001 and 99.99% and 99.98% on CIDDS-002, respectively. Full article

The globalization of hardware design flows has increased the risk of Hardware Trojan (HT) insertion during the design phase. Graph-based learning methods have shown promise for HT detection at the Register Transfer Level (RTL). However, most existing approaches rely on representing RTL designs through a single graph structure. This single-view modeling paradigm inherently constrains the model’s ability to perceive complex behavioral patterns, consequently limiting detection performance. To address these limitations, we propose GNN-MFF, an innovative multi-view feature fusion model based on Graph Neural Networks (GNNs). Our approach centers on joint multi-view modeling of RTL designs to achieve a more comprehensive representation. Specifically, we construct complementary graph-structural views: the Abstract Syntax Tree (AST) capturing structure information, and the Data Flow Graph (DFG) modeling logical dependency relationships. For each graph structure, customized GNN architectures are designed to effectively extract its features. Furthermore, we develop a feature fusion framework that leverages a multi-head attention mechanism to deeply explore and integrate heterogeneous features from distinct views, thereby enhancing the model’s capacity to structurally perceive anomalous logic patterns. Evaluated on an extended Trust-Hub-based HT benchmark dataset, our model achieves an average F1-score of 97.08% in automated detection of unseen HTs, surpassing current state-of-the-art methods. Full article

This paper surveys the application of machine learning in fiber composite manufacturing, highlighting its role in adaptive process control, defect detection, and real-time quality assurance. First, the need for ML in composite processing is highlighted, followed by a review of data-driven approaches—including predictive modeling, sensor fusion, and adaptive control—that address material heterogeneity and process variability. An in-depth analysis examines six case studies, among which are XPBD-based surrogates for RL-driven robotic draping, hyperspectral imaging (HSI) with U-Net segmentation for adhesion prediction, and CNN-driven surrogate optimization for variable-geometry forming. Building on these insights, a hybrid AI model architecture is proposed for natural-fiber composites, integrating a physics-informed GNN surrogate, a 3D Spectral-UNet for defect segmentation, and a cross-attention controller for closed-loop parameter adjustment. Validation on synthetic data—including visualizations of HSI segmentation, graph topologies, and controller action weights—demonstrates end-to-end operability. The discussion addresses interpretability, domain randomization, and sim-to-real transfer and highlights emerging trends such as physics-informed neural networks and digital twins. This paper concludes by outlining future challenges in small-data regimes and industrial scalability, thereby providing a comprehensive roadmap for ML-enabled composite manufacturing. Full article

Industry 4.0 produces large volumes of sensor and machine data, offering new possibilities for manufacturing analytics but also creating challenges in combining domain knowledge with visual analysis. We present a systematic review of 13 peer-reviewed knowledge-assisted visual analytics (KAVA) systems published between 2014 and 2024, following PRISMA guidelines for the identification, screening, and inclusion processes. The survey is organized around six predefined building blocks, namely, user group, industrial domain, visualization, knowledge, data and machine learning, with a specific emphasis on the integration of knowledge and visualization in the reviewed studies. We find that ontologies, taxonomies, rule sets, and knowledge graphs provide explicit representations of expert understanding, sometimes enriched with annotations and threshold specifications. These structures are stored in RDF or graph databases, relational tables, or flat files, though interoperability is limited, and post-design contributions are not always persisted. Explicit knowledge is visualized through standard and specialized techniques, including thresholds in time-series plots, annotated dashboards, node–link diagrams, customized machine views from ontologies, and 3D digital twins with expert-defined rules. Line graphs, bar charts, and scatterplots are the most frequently used chart types, often augmented with thresholds and annotations derived from explicit knowledge. Recurring challenges include fragmented storage, heterogeneous data and knowledge types, limited automation, inconsistent validation of user input, and scarce long-term evaluations. Addressing these gaps will be essential for developing adaptable, reusable KAVA systems for industrial analytics. Full article

The goal of multi-view graph clustering (MVGC) for remote sensing data is to obtain a consistent partitioning by capturing complementary and consensus information across multiple views. However, numerous ambiguous background samples in multi-view remote sensing data increase structural heterogeneity while simultaneously hindering effective information extraction and fusion. Existing MVGC methods cannot selectively integrate and fully refine both graph structure and node attribute information for consensus representation learning. Furthermore, current methods tend to overlook distant nodes, thus failing to capture the global graph structure. To solve these issues, we propose a novel method called Improved Multi-View Graph Clustering with Global Graph Refinement (IMGCGGR). Specifically, we first design a view-specific fusion network (VSFN) to extract and integrate node attribute and structural information into view-specific representation for each view. VSFN not only utilizes a global self-attention mechanism to enhance the global properties of structural information but also constructs a clustering loss through a self-supervised strategy to guide the view-specific clustering distribution assignment. Moreover, to enhance the capability of view-specific representation, a learnable attention-driven aggregation strategy is introduced to flexibly fuse the attribute and structural feature. Then, we adopt a cross-view fusion module to adaptively merge multiple view-specific representations for generating the final consensus representation. Comprehensive experiments show that IMGCGGR achieves significant clustering performance improvements over baseline methods across various benchmark datasets. Full article

In the era of big-data-driven multi-platform and multimodal health information dissemination, the rapid spread of false and misleading content poses a critical threat to public health awareness and decision making. To address this issue, a dual-stream Transformer-based multimodal health misinformation detection framework is presented, incorporating a symbol drift detection module, a symbol-aware text graph neural network, and a crossmodal alignment fusion module. The framework enables precise identification of implicit misleading health-related symbols, comprehensive modeling of textual dependency structures, and robust detection of crossmodal semantic conflicts. A domain-specific health-symbol-sensitive lexicon is constructed, and contextual drift intensity is quantitatively measured and embedded as explicit features into the text GNN. Bidirectional cross-attention and contrastive learning are further employed to enhance crossmodal semantic alignment. Extensive experiments on a large-scale real-world multimodal health information dataset, encompassing heterogeneous data sources typical of big data environments, demonstrate that the proposed method consistently outperforms state-of-the-art baselines in CTR prediction, multimodal recommendation, and ranking tasks. The results indicate substantial improvements in both accuracy and ranking quality, while ablation studies further verify the contributions of symbol drift modeling, graph-structured representation, and crossmodal fusion. Overall, the proposed approach advances big data analytics for multimodal misinformation detection and provides an interpretable and scalable solution for public health communication governance. Full article

Graph Neural Networks (GNNs) face fundamental algorithmic challenges in real-world applications due to a combination of data heterogeneity, adversarial heterophily, and severe class imbalance. A critical research gap exists for a unified framework that can simultaneously address these issues, limiting the deployment of GNNs in high-stakes domains like financial fraud detection and social network analysis. This paper introduces HAG-CFNet, a novel framework designed to bridge this gap by integrating three key innovations: (1) a heterogeneity-aware message-passing mechanism that uses relation-specific attention to capture rich semantic information; (2) a dual-channel heterophily detection module that explicitly identifies and neutralizes adversarial camouflage through separate aggregation pathways; and (3) a domain-aware counterfactual generator that produces plausible, actionable explanations by co-optimizing feature and structural perturbations. These are supported by a synergistic imbalance correction strategy combining graph-adapted oversampling with cost-sensitive learning. Extensive testing on large-scale financial datasets validates the framework’s impact: HAG-CFNet achieves a 4.2% AUC-PR improvement over state-of-the-art methods, demonstrates superior robustness by reducing performance degradation under structural noise by over 50%, and generates counterfactual explanations with 91.8% validity while requiring minimal perturbations. These advances provide a direct pathway to building more trustworthy and effective AI systems for critical applications ranging from financial risk management to supply chain analysis and social media content moderation. Full article

Urbanization and traffic congestion caused by the surge in car ownership have exacerbated energy consumption and carbon emissions, and dynamic traffic flow prediction and energy-saving route planning have become the key to solving this problem. Dynamic traffic flow prediction accurately captures the spatio-temporal changes of traffic flow through advanced algorithms and models, providing prospective information for traffic management and travel decision-making. Energy-saving route planning optimizes travel routes based on prediction results, reduces the time vehicles spend on congested road sections, thereby reducing fuel consumption and exhaust emissions. However, there are still many shortcomings in the current relevant research, and the existing research is mostly isolated and applies a single model, and there is a lack of systematic comparison of the adaptability, generalization ability and fusion potential of different models in various scenarios, and the advantages of heterogeneous graph neural networks in integrating multi-source heterogeneous data in traffic have not been brought into play. This paper systematically reviews the relevant global studies from 2020 to 2025, focuses on the integration path of dynamic traffic flow prediction methods and energy-saving route planning, and reveals the advantages of LSTM, graph neural network and other models in capturing spatiotemporal features by combing the application of statistical models, machine learning, deep learning and mixed methods in traffic forecasting, and comparing their performance with RMSE, MAPE and other indicators, and points out that the potential of heterogeneous graph neural networks in multi-source heterogeneous data integration has not been fully explored. Aiming at the problem of disconnection between traffic prediction and path planning, an integrated framework is constructed, and the real-time prediction results are integrated into path algorithms such as A* and Dijkstra through multi-objective cost functions to balance distance, time and energy consumption optimization. Finally, the challenges of data quality, algorithm efficiency, and multimodal adaptation are analyzed, and the development direction of standardized evaluation platform and open source toolkit is proposed, providing theoretical support and practical path for the sustainable development of intelligent transportation systems. Full article

Social networks generate vast amounts of data that can reveal patterns of human behaviour, social attachment, and mental states. This paper explores advanced machine learning techniques to detect and model such patterns, focusing on community structures, influential users, and information diffusion pathways. To address the scale, noise, and heterogeneity of social data, we leverage recent advances in graph theory, natural language processing, and anomaly detection. Our framework combines clustering for community detection, sentiment analysis for emotional state inference, and centrality metrics for influence estimation, while integrating multimodal data—including textual and visual content—for richer behavioural insights. Experimental results demonstrate that the proposed approach effectively extracts actionable knowledge, supporting mental well-being and strengthening digital social ties. Furthermore, we emphasise the role of privacy-preserving methods, such as federated learning, to ensure ethical analysis. These findings lay the groundwork for responsible and effective applications of machine learning in social network analysis. Full article

This paper investigates federated learning (FL) for cross-site scripting (XSS) detection under out-of-distribution (OOD) drift. Real-world XSS traffic involves fragmented attacks, heterogeneous benign inputs, and client imbalance, which erode conventional detectors. To simulate this, we construct two structurally divergent datasets: one with obfuscated, mixed-structure samples and another with syntactically regular examples, inducing structural OOD in both classes. We evaluate GloVe, GraphCodeBERT, and CodeT5 in both centralised and federated settings, tracking embedding drift and client variance. FL consistently improves OOD robustness by averaging decision boundaries from cleaner clients. Under FL scenarios, CodeT5 achieves the best aggregated performance (97.6% accuracy, 3.5% FPR), followed by GraphCodeBERT (96.8%, 4.7%), but is more stable on convergence. GloVe reaches a competitive final accuracy (96.2%) but exhibits a high instability across rounds, with a higher false positive rate (5.5%) and pronounced variance under FedProx. These results highlight the value and limits of structure-aware embeddings and support FL as a practical, privacy-preserving defence within OOD XSS scenarios. Full article

The proliferation of fake profiles on social media presents critical cybersecurity and misinformation challenges, necessitating robust and scalable detection mechanisms. Such profiles weaken consumer trust, reduce user engagement, and ultimately harm brand reputation and platform credibility. As adversarial tactics and synthetic identity generation evolve, traditional rule-based and machine learning approaches struggle to detect evolving and deceptive behavioral patterns embedded in dynamic user-generated content. This study aims to develop an AI-driven, multi-modal deep learning-based detection system for identifying fake profiles that fuses textual, visual, and social network features to enhance detection accuracy. It also seeks to ensure scalability, adversarial robustness, and real-time threat detection capabilities suitable for practical deployment in industrial cybersecurity environments. To achieve these objectives, the current study proposes an integrated AI system that combines the Robustly Optimized BERT Pretraining Approach (RoBERTa) for deep semantic textual analysis, ConvNeXt for high-resolution profile image verification, and Heterogeneous Graph Attention Networks (Hetero-GAT) for modeling complex social interactions. The extracted features from all three modalities are fused through an attention-based late fusion strategy, enhancing interpretability, robustness, and cross-modal learning. Experimental evaluations on large-scale social media datasets demonstrate that the proposed RoBERTa-ConvNeXt-HeteroGAT model significantly outperforms baseline models, including Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM). Performance achieves 98.9% accuracy, 98.4% precision, and a 98.6% F1-score, with a per-profile speed of 15.7 milliseconds, enabling real-time applicability. Moreover, the model proves to be resilient against various types of attacks on text, images, and network activity. This study advances the application of AI in cybersecurity by introducing a highly interpretable, multi-modal detection system that strengthens digital trust, supports identity verification, and enhances the security of social media platforms. This alignment of technical robustness with brand trust highlights the system’s value not only in cybersecurity but also in sustaining platform credibility and consumer confidence. This system provides practical value to a wide range of stakeholders, including platform providers, AI researchers, cybersecurity professionals, and public sector regulators, by enabling real-time detection, improving operational efficiency, and safeguarding online ecosystems. Full article

The rapid and accurate identification of power system transient stability status is a fundamental prerequisite for ensuring the secure and reliable operation of large-scale power grids. With the increasing complexity and heterogeneity of modern power system components, system nonlinearity has grown significantly, rendering traditional time-domain simulation and direct methods unable to meet accuracy and efficiency requirements simultaneously. To further improve the prediction accuracy of power system transient stability and provide more refined assessment results, this paper integrates deep learning with power system transient stability and proposes a transient stability assessment of power systems built upon a deep spatio-temporal feature extraction network method. First, a spatio-temporal feature extraction module is constructed by combining an improved graph attention network with a residual bidirectional temporal convolutional network, aiming to capture the spatial and bidirectional temporal characteristics of transient stability data. Second, a classification module is developed using the Kolmogorov–Arnold network to establish the mapping relationship between spatio-temporal features and transient stability states. This enables the accurate determination of the system’s transient stability status within a short time after fault occurrence. Finally, a weighted cross-entropy loss function is employed to address the issue of low prediction accuracy caused by the imbalanced sample distribution in the evaluation model. The feasibility, effectiveness, and superiority of the proposed method are validated through tests on the New England 10-machine 39-bus system and the NPCC 48-machine 140-bus system. Full article

Prefabricated construction represents an advanced approach to sustainable development, and safety issues in prefabricated construction projects have drawn widespread attention. Safety accident case reports contain a wealth of safety knowledge, and extracting and learning from such historical reports can significantly enhance safety management capabilities. However, these texts are often semantically complex and lengthy, posing challenges for traditional Information Extraction (IE) methods. This study focuses on the challenge of Named Entity Recognition (NER) in long texts under complex engineering contexts and proposes a novel model that integrates Modern Bidirectional Encoder Representations from Transformers (ModernBERT),Bidirectional Long Short-Term Memory (BiLSTM), andConditional Random Field (CRF). A comparative analysis with current mainstream methods is conducted. The results show that the proposed model achieves an F1 score of 0.6234, outperforming mainstream baseline methods. Notably, it attains F1 scores of 0.95 and 0.92 for the critical entity categories “Consequence” and “Type,” respectively. The model maintains stable performance even under semantic noise interference, demonstrating strong robustness in processing unstructured and highly heterogeneous engineering texts. Compared with existing long-text NER models, the proposed method exhibits superior semantic parsing ability in engineering contexts. This study enhances information extraction methods and provides solid technical support for constructing safety knowledge graphs in prefabricated construction, thereby advancing the level of intelligence in the construction industry. Full article

In recommender systems research, the data sparsity problem has driven the development of hybrid recommendation algorithms integrating multimodal information and the application of graph neural networks (GNNs). However, conventional GNNs relying on homogeneous Euclidean embeddings fail to effectively model the non-Euclidean geometric manifold structures prevalent in real-world scenarios, consequently constraining the representation capacity for heterogeneous interaction patterns and compromising recommendation accuracy. As a consequence, the representation capability for heterogeneous interaction patterns is restricted, thereby affecting the overall representational power and recommendation accuracy of the models. In this paper, we propose a hyperbolic graph neural network model with contrastive learning for rating–review recommendation, implementing a dual-graph construction strategy. First, it constructs a review-aware graph to integrate rich semantic information from reviews, thus enhancing the recommendation system’s context awareness. Second, it builds a user–item interaction graph to capture user preferences and item characteristics. The hyperbolic graph neural network architecture enables joint learning of high-order features from these two graphs, effectively avoiding the embedding distortion problem commonly associated with high-order feature learning. Furthermore, through contrastive learning in hyperbolic space, the model effectively leverages review information and user–item interaction data to enhance recommendation system performance. Experimental results demonstrate that the proposed algorithm achieves excellent performance on multiple real-world datasets, significantly improving recommendation accuracy. Full article