Search Results (111)

Gait analysis is increasingly used as a dynamic functional biomarker for computer-aided diagnosis (CADx), although strong internal performance alone does not establish clinical utility. This targeted narrative review examines Parkinson’s disease (PD) and knee osteoarthritis (KOA) as its primary clinical contexts while treating fall risk and other mobility disorders as contextual extensions. A structured literature search and source-verification process covered studies available through 31 July 2026. The review corpus comprised 118 sources spanning clinical evidence, measurement validation, datasets, algorithmic architectures, and methodological guidance. This review critically compares sensing modalities, public and proprietary datasets, feature-based models, CNN/RNN architectures, graph neural networks, Transformers, state-space models, and trust-supporting approaches, including explainable artificial intelligence, automated machine learning, federated learning, and multimodal fusion. Using an explicit coverage rule, a common validation audit was applied to 15 empirical or measurement-validation studies. The audited evidence did not demonstrate mature independent multisite validation for disease-focused gait CADx. Formal probability calibration and quantitative testing of explanation stability were also absent, while publicly available KOA-specific multimodal benchmarks remained scarce. Based on these findings, this review proposes a six-level validation-readiness ladder in which independent external evidence at Level 3 represents the minimum threshold for initiating a supervised clinical pilot. The framework prioritizes subject-level separation, leakage control, calibration, clinically meaningful reference standards, and prospective workflow evaluation. Full article

Environmental, Social, and Governance (ESG) risks increasingly propagate across interconnected supply chains, yet conventional ESG assessment methods remain largely reliant on firm-level disclosures and static ESG ratings that often overlook indirect risk transmission among trading partners. This study develops a network-aware artificial intelligence (AI) framework for forecasting ESG risk by integrating Graph Neural Networks (GNNs), transformer-based natural language processing (NLP), explainable AI, and conventional machine-learning techniques. The proposed framework combines supply-chain network structures, shipment-level trade information, ESG controversy records, governance indicators, and transformer-derived ESG sentiment extracted using FinBERT and RoBERTa. Using a dataset of 11,386 firms across 27 industries from 2015 to 2025, the proposed GNN achieved the highest predictive performance, outperforming conventional machine-learning models with an ROC-AUC of 0.913. The results further demonstrate that supply-chain network centrality and transformer-derived ESG sentiment substantially improve the early identification of firms exposed to future ESG controversies. By integrating network relationships with textual ESG intelligence, the proposed framework advances FinTech-enabled ESG analytics and provides a scalable approach for proactive risk monitoring, sustainable investment decision-making, and supply-chain risk management. Full article

Unmanned aerial vehicles (UAVs) have become essential cyber-physical platforms for applications such as surveillance, infrastructure inspection, emergency response, and intelligent transportation. However, their tight coupling among sensing, communication, control, actuation, and swarm coordination also exposes them to sophisticated cyber-physical attacks that are difficult to detect using conventional intrusion detection systems. Existing machine learning, deep learning, graph-based, and federated intrusion detection approaches generally rely on statistical feature representations or temporal patterns, providing limited capability to model causal dependencies among interacting UAV subsystems and to generalize across heterogeneous operating environments. To address these limitations, this paper proposes Fed-CGIDS-UAV, a federated causal graph learning framework for cross-domain intrusion detection in cyber-physical UAV networks. The proposed framework models each telemetry window as a typed causal graph in which nodes represent navigation, sensing, communication, control, actuation, and swarm states, while directed edges capture stable operational dependencies. Intrusions are detected by identifying violations of these learned causal relationships, and the framework provides interpretable node-edge explanations to support root-cause analysis. Furthermore, federated learning enables collaborative model training across distributed UAV clients without sharing raw telemetry, thereby preserving data privacy while improving robustness under heterogeneous operating conditions. The proposed framework was implemented and experimentally evaluated in a controlled simulation environment covering four UAV operating domains and six representative attack classes. All experiments were repeated over five independent runs using different random seeds, and the reported results correspond to the measured average performance. The proposed framework was implemented using Python 3.12 (Python Software Foundation, Wilmington, DE, USA) and PyTorch 2.3 (Meta Platforms, Menlo Park, CA, USA). UAV flight data were generated using Microsoft AirSim 1.9.1 (Microsoft Corporation, Redmond, WA, USA), integrated with PX4 Autopilot v1.14 (Dronecode Foundation, San Francisco, CA, USA) and Gazebo Sim 11 (Open Source Robotics Foundation, Mountain View, CA, USA). Within this simulation-based evaluation, Fed-CGIDS-UAV achieved an accuracy of 0.968, an F1-score of 0.956, and an internal–external stability gap (IESG) of 0.028, outperforming conventional machine learning, deep learning, graph-based, and centralized causal baselines while maintaining competitive computational latency. Although these results demonstrate the effectiveness of the proposed framework under controlled simulation conditions, validation using real-flight UAV telemetry remains an important direction for future research. These results demonstrate that integrating causal graph learning with federated optimization provides an effective and interpretable solution for privacy-preserving intrusion detection in heterogeneous cyber-physical UAV environments. Full article

In biomedical research and clinical practices, Graph Neural Networks (GNNs) are playing an increasingly important role and have been applied to the problems of disease pathway detection, gene–disease relation prediction, etc. They show great potential for biomedical predictions; however, there are interpretability issues when used on complex datasets like gene expression data. Current explainability methods such as GNNExplainer are designed to explain individual instances, not the whole network. The absence of transparency hinders trust and limits the clinical/biomedical implementation of GNNs. Additionally, more interpretable models like GNN-SubNet and XGDAG do not fulfill the expectation of a clear picture for the entire network. This research addresses the limitation of the network-wide explainability of GNNs by introducing a GNN-based BioGraphEX model that incorporates interpretable methods at two levels, instance-level and network-wide level, such as gradient-based methods and SHAP (Shapley Additive Explanations). Using the GSE25097 biomedical dataset, the model achieves an accuracy of 85% and an F1 Score of 0.82, surpassing baseline methods in both predictive performance and interpretability. These results address the limitations of existing models like GNN-SubNet and XGDAG by providing both instance-level and network-wide insights. Metrics like Explanation Fidelity (83%) further validated the robustness of the explanations. Full article

Graph neural networks (GNNs) for crystal property prediction are typically evaluated by a single aggregate error, which can mask where, and for which classes of materials, these models fail. In this study, we present a space-group-, centering-type-, and band gap-resolved error analysis of GNN band gap prediction on the Materials Project dataset. As a channel for this analysis we use Crystal-X, a deliberately simple model: a standard graph convolutional backbone with two minor architectural modifications, an asymmetric edge convolution and a neighbor-feature transformation, that supplement bond information often treated as secondary in node-centric models. Crystal-X is not a state-of-the-art model: it reaches a band gap MAE of 0.256 eV on the MP 2018.6 dataset, behind ALIGNN (0.22 eV) and PotNet (0.20 eV), though ahead of older baselines such as CGCNN (0.39 eV), SchNet (0.415 eV), and MEGNet (0.33 eV) while using only the nine-property CGCNN atomic feature set. Its value here is as a controlled, low-complexity testbed for the error analysis. That analysis reveals systematic patterns that aggregate MAE conceals: errors concentrate in underrepresented band gap ranges and in low-symmetry and non-centrosymmetric space groups; per-group errors for sparsely populated space groups are dominated by sampling noise; and modest, as-yet-unverified gains from edge-aware convolutions appear in monoclinic and non-primitive-centered systems. We argue that this kind of granular, symmetry-resolved evaluation should accompany aggregate benchmarks when assessing crystal GNNs. Full article

In smart cities, crowding in transportation hubs, large event venues, and commercial/tourist districts can rapidly escalate from service congestion to public-safety incidents. Real-world operations are constrained by heterogeneous sensing coverage, delayed statistics, privacy requirements, and the need for accountable multi-agency decisions. Following a rigorous PRISMA protocol, we synthesized 107 primary empirical studies (2020–2026) to systematically review context-aware crowd technologies. Moving beyond isolated algorithmic benchmarks, we organized these advances into a mathematically formalized closed-loop framework (Sensing–Prediction–Intervention–Feedback). Crowd sensing has evolved toward edge-based computer vision, passive mobile signaling, and multimodal fusion to balance operational trade-offs among density applicability, environmental robustness, privacy burdens, and end-to-end latency. Prediction architectures—converging on Spatiotemporal Graph Neural Networks (ST-GNNs) and simulation-augmented digital twins—are critically evaluated against constraints in predictive horizon, computational overhead, and explainability. To bridge theory and practical deployment, we deduce a multidimensional evaluation taxonomy and a hierarchical trigger-and-escalation matrix, tailoring control philosophies (e.g., spatiotemporal capacity synchronization and dynamic demand reshaping) to the three heterogeneous scenarios. Finally, we propose four strategic directions to chart a deployment-oriented roadmap for the integrated Urban Brain: edge-based privacy-preserving fusion, cross-scenario generalization, accountable Explainable Artificial Intelligence (XAI) with human-in-the-loop synergy, and end-to-end outcome-oriented empirical validation. Full article

Urban infrastructure systems increasingly depend on textual reports generated by citizens, inspection teams, maintenance units, emergency platforms, and smart city services. Accurate identification of critical risks in these reports is essential for enhancing urban resilience and enabling timely decision-making. Nevertheless, urban infrastructure risk classification is challenging due to the brevity, noise, domain specificity, and context dependence of these reports. This study introduces RAFE-XAI, a retrieval-augmented feature engineering and explainable natural language processing framework for urban infrastructure risk classification. The term retrieval-augmented is used here in a classification-oriented sense: retrieved reports are used to construct additional features and evidence, not to generate output text as in Retrieval-Augmented Generation systems. The proposed framework incorporates semantic sentence embeddings, retrieval-based evidence, neighborhood-derived label distributions, domain-specific risk indicators, infrastructure asset cues, location indicators, and evidence-based explainability. The framework does not construct an explicit graph, adjacency matrix, graph neural network, or message-passing mechanism. Instead, retrieval is used to derive neighbor label-distribution features, which are combined with semantic embeddings and interpretable keyword, asset, and location indicators. To assess the effectiveness of this approach, UIR-Text, a semi-synthetic urban infrastructure risk dataset with scenario-level group splitting to mitigate data leakage, was constructed. Experimental results on UIR-Text show that fine-tuned DistilBERT achieves the strongest predictive performance, with Macro-F1 scores of 0.8278 for category classification, 0.9120 for binary critical-risk detection, and 0.3379 for four-level severity classification. Among the explainable feature-engineering models, RAFE-XAI with Random Forest achieves the strongest category classification performance, with Accuracy 0.8400, Macro-F1 0.8043, Weighted-F1 0.8444, and MCC 0.8062. These results suggest that fine-tuned transformers provide the highest predictive performance on this benchmark, while RAFE-XAI offers a transparent retrieval-augmented alternative that exposes retrieved evidence, neighbor label distributions, and domain cues. Four-level severity classification remains challenging, even with fine-tuned DistilBERT, indicating the need for richer impact-aware variables. Full article

Graph structures can be used to represent and explain data about connections and interactions between certain objects that form network systems. Graphs are applied in various scientific areas, and this paper explores their potential to support the design process of electronic circuits. Experimentations for predicting links in a heterogeneous graph structure are performed, which are built on the basis of schematic variants of electronic circuits and their elements. A graph neural network approach and the PyG library are used. The predictive model is evaluated, and the obtained results are promising. Full article

Non-Revenue Water (NRW) from leakage represents a major global economic and environmental challenge for urban utilities. While Deep Learning (DL) offers transformative potential for leak detection in Water Distribution Systems (WDSs) and existing reviews provide critical assessments, a consolidated, quantitative evaluation of real-world applicability and performance consistency that is actionable for engineering practice remains absent. This systematic review critically evaluates DL applications for WDS leak detection and localisation, with a focused analysis of model accuracy in relation to data types, methodological rigour, and the validation gap between controlled experiments and operational deployment. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework, a systematic literature search was performed using Scopus, Web of Science, Google Scholar, ScienceDirect, Taylor & Francis, and MDPI databases for publications spanning the period from 2015 to 2025. From an initial 5265 records, 72 studies met the inclusion criteria for qualitative synthesis. Analysis revealed a specialisation of DL architectures by data modality: Convolutional Neural Networks (CNNs) applied to acoustic or vibration data yield the highest reported accuracy for direct leak identification; Long Short-Term Memory (LSTM) and Transformer models are predominant for temporal hydraulic data (pressure and flow); and Graph Neural Networks (GNNs) excel with topological data for state estimation. While reported accuracy is often high, performance is highly contingent on data quality and pre-processing. A significant disparity exists between results on synthetic versus real-world validation datasets, ranging from a decline of approximately 3 to 30 percentage points, with reported real-world accuracy spanning 70 to 79.7 percent. Moreover, DL demonstrates a paradigm shift in technical capability for leak management. However, transitioning to reliable field applications requires overcoming key challenges: standardising benchmarks and performance reporting, improving model generalisability and explainability, and fostering integration within practical Digital Twin (DT) frameworks to enable proactive infrastructure management. Full article

Over the past two decades, air-quality prediction has moved from a mainly single-model paradigm toward ensemble systems that make explicit use of diversity across models, observations, and data streams. This review connects developments that are often treated separately: chemical transport model (CTM) ensembles, tree-based and hybrid machine learning ensembles, deep learning architectures, physics-informed neural networks, and distributed approaches such as federated learning. Evidence summarized from recent systematic reviews and coordinated modeling initiatives indicates that, within comparable validation settings, ensembles often outperform individual models for PM_2.5, PM₁₀, O₃, NO₂, CO, and SO₂ across a broad range of spatial scales and standard error metrics, including RMSE, MAE, and correlation. Operational CTM ensembles, such as the Copernicus Atmosphere Monitoring Service (CAMS) European system with eleven regional models, improve both forecast skill and uncertainty characterization for ozone and particulate matter. In data-driven applications, tree-based ensembles (Random Forest, gradient boosting, XGBoost, LightGBM) and hybrid deep architectures (CNN–LSTM models, attention-based multi-branch networks, graph neural networks) now form a core part of the state of the art for AQI (Air Quality Index) and particulate-matter estimation from structured and multi-source data. Reported performance can be very high on well-structured tabular datasets, with $R^2$ values above 0.99 in selected benchmarks and RMSE reductions of 23–45% relative to classical statistical baselines in multi-modal studies; however, these values are not directly interchangeable because pollutant type, prediction horizon, monitoring density, and validation design differ among studies. This review proposes a practical taxonomy of ensemble strategies and uses it to explain why diversity, rather than model count alone, is central to reliable air-quality prediction. Drawing on coordinated European and North American model-evaluation initiatives (AQMEII, HTAP) and on case studies in topographically and meteorologically complex Italian regions (the Po Valley, the Naples metropolitan area, and Campania), we show that effective ensemble design requires a balance among diversity, redundancy, computational feasibility, and interpretability. On the basis of a structured narrative synthesis, the main research gaps concern physics-informed and explainable ensemble frameworks, transferable and adaptive models, standardized benchmarks, severe-pollution-episode forecasting, and scalable distributed architectures. Open questions include how to design compact non-redundant CTM sub-ensembles and how to couple deep learning with chemical-transport physics in next-generation operational systems. Full article

Inverse kinematics (IK) is fundamental to robot motion planning. Classical analytical solvers require complete Denavit–Hartenberg (DH) parameters that are often proprietary or degraded by mechanical wear, and numerical solvers based on damped least squares (DLS) are sensitive to initialization, particularly near singularities. We propose XGNN, an explainable graph neural network positioned as a model-free, interpretable warm-start initializer for downstream numerical IK refinement rather than as a standalone replacement for analytical solvers. Each IK query is encoded as a 12-node graph in which six pose nodes and six joint nodes are connected through bipartite pose-to-joint attention edges and chain edges along the kinematic structure. GATv2 message passing aggregates information at each joint node; two ablation-validated design contributions (a learnable node-type embedding and an angle-aware composite loss) enable training to convergence. Evaluated on 300,000 trajectory-style samples generated from the ABB IRB 2400 kinematic model, XGNN achieves ${3.66}^{\circ }$ joint mean absolute error (MAE), comparable to a multilayer perceptron baseline (${3.09}^{\circ }$) and a bidirectional LSTM (${3.14}^{\circ }$) under identical training. The standalone joint accuracy of all learned models is too coarse for direct industrial use, but XGNN provides the strongest warm start for DLS refinement: the convergence rate improves from $98.4\%$ to $100\%$, mean iterations drop from 14.6 to 3.2, and wall-clock time per pose drops $5.0\times$ on the IRB 2400. The benefit transfers cross-platform to the Universal Robots UR5 collaborative manipulator (convergence rate $82.2\%$ to $100\%$, $10.0\times$ speedup) and survives DH parameter perturbation of up to $\pm 10\%$, simulating calibration drift or mechanical wear. The GATv2 attention coefficients additionally provide an interpretability signal at zero inference cost. XGNN therefore complements analytical and numerical IK methods as an interpretable, calibration-robust warm start when DH parameters are unavailable, proprietary, or degraded. Full article

Recent advances in wearable, vision-based, trajectory, physiological, and multimodal sensing technologies, together with deep learning, have enabled continuous, objective, and individualized assessment of sport performance and athlete health. Unlike prior reviews that primarily focus on a single sensing modality, sport, or algorithmic series, this review integrates wearable, vision-based, trajectory, physiological, and multimodal sensing streams with deep learning models across both performance analysis and athlete health monitoring, thereby clarifying modality-task-model relationships and translational limitations. This review synthesizes recent progress in sensor-based sports intelligence, focusing on how heterogeneous data streams are transformed into performance- and health-related decision support. The reviewed applications include athlete and ball perception, multi-object tracking, pose estimation, action recognition, trajectory and tactical analysis, training-load and fatigue monitoring, injury-risk prediction, rehabilitation monitoring, and return-to-play support. Deep learning architectures, including CNNs, LSTMs, GRUs, TCNs, Transformers, attention mechanisms, graph neural networks, and multimodal fusion models, are discussed in relation to their suitability for visual, temporal, spatial, physiological, and multisource data. This review further identifies key challenges, including data heterogeneity, annotation scarcity, limited cross-sport and cross-device generalization, real-time deployment constraints, model interpretability, privacy protection, and ethical governance. Moving forward, research efforts should focus on the development of standardized datasets, reliable multimodal data fusion strategies, self-supervised and transfer learning approaches, and deployment on edge or cloud computing platforms. Additionally, enhancing interpretability through explainable AI and implementing closed-loop, individualized monitoring systems are critical. By synthesizing advances in sensing technologies, deep learning methodologies, and real-world applications, this review aims to provide a practical reference for optimizing athletic performance, preventing injuries, guiding rehabilitation, and supporting long-term health management of athletes. Full article

This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, we conducted empirical evaluations across three distinct benchmark domains (AIDS, MUTAG, and PROTEINS) using a Graph Isomorphism Network (GIN) backbone. Under a baseline 5% backdoor subgraph trigger injection ratio, ECSMT achieves excellent utility retention, securing a Clean Accuracy (CA) of 97.33% (±0.62%) while reducing the Attack Success Rate (ASR) from 97.00% down to 69.45% on the primary AIDS benchmark. Cross-domain testing reveals that defensive efficacy is strongly constrained by dataset characteristics: small-scale datasets such as MUTAG suffer from persistent trigger concentration, while complex graph manifolds such as PROTEINS exhibit high levels of topological noise. Furthermore, mapping these technical outcomes into an enterprise asset framework yields a 61% expenditure compression at critical technological feeder locations and a 98.93% reduction in total systemic loss. This study indicates that the proposed triangular mitigation strategy offers a valuable, scalable blueprint for enhancing the technical resilience and prognostic economic modeling of critical infrastructure networks. Full article

Background: The convergence of machine learning (ML), gamification, and critical thinking assessment within adaptive educational platforms has accelerated since 2020, driven by large language models (LLMs) and graph neural networks (GNNs). No prior systematic review has jointly addressed all three dimensions, and Central Asian educational contexts remain underrepresented. Methods: Following PRISMA 2020 guidelines, we searched Scopus (n  =  4396) and OpenAlex (n  =  4152) for publications from 2016 to 2026. Quality assessment used the Mixed Methods Appraisal Tool (MMAT 2018; threshold ≥  2), yielding 82 papers. Five research questions addressed ML personalization (RQ1), gamification and engagement (RQ2), critical thinking assessment tools (RQ3), recommendation algorithms (RQ4), and regional applicability in Kazakhstan and Central Asia (RQ5). Results: Transformer-based and GNN models dominate the recent literature (52% of corpus from 2025), with an accuracy of 91–97% for dropout prediction and learning path recommendation under single-institution conditions. Gamification studies report up to 90% student satisfaction; LLM-based critical thinking assessment shows promise but faces validity concerns. Thirteen papers address Central Asian contexts. Conclusions: Significant gaps persist: no integrated gamification–critical thinking framework exists, recommendation systems lack explainability, and Kazakh-language datasets are severely underrepresented. Future research should prioritize multilingual adaptive systems, explainable algorithms, and privacy-preserving federated learning for low-resource contexts. Full article

Combinatorial optimization is a key component in critical decision problems such as routing, scheduling, network design, and graph optimization. Although combinatorial optimization methods, including exact algorithms, approximation methods, constraint programming, mixed integer programming, and metaheuristics, are widely available, they often face obstacles, such as limited scalability and adaptability in various applications. In this study, a systematic critical review of machine learning for combinatorial optimization is provided to characterize the usage and evaluation of learning-based approaches. A detailed analysis is used to infer and determine findings and limitations. The paper emphasizes how machine learning for computational optimization has changed over time, moving from end-to-end neural solvers to hybrid systems. Learning components are essential for directing, speeding up, or enhancing traditional solver backbones such as constraint programming and metaheuristics in hybrid systems. The review also critically examines current limits that impact performance in general, including scalability, deployment readiness, generalization, and benchmark consistency. Even though using large language models for problem formulation and heuristic synthesis has potential, more work needs to be done to ensure reliable validation. As a conclusion, this article examines recent studies’ findings, emphasizes the growing trend toward hybrid learning-driven optimization frameworks, and underlines important methodological limits and unresolved issues. Full article