Search Results (367)

Currently, maritime navigation safety risks—particularly those related to ship navigation—are primarily assessed through traditional rule-based methods and expert experience. However, such approaches often suffer from limited accuracy and lack real-time responsiveness. As maritime environments and operational conditions become increasingly complex, traditional techniques struggle to cope with the diversity and uncertainty of navigation scenarios. Therefore, there is an urgent need for a more intelligent and precise risk prediction method. This study proposes a ship risk prediction framework that integrates a deep learning model based on Long Short-Term Memory (LSTM) networks with Bayesian risk evaluation. The model first leverages deep neural networks to process time-series trajectory data, enabling accurate prediction of a vessel’s future positions and navigational status. Then, Bayesian inference is applied to quantitatively assess potential risks of collision and grounding by incorporating vessel motion data, environmental conditions, surrounding obstacles, and water depth information. The proposed framework combines the advantages of deep learning and Bayesian reasoning to improve the accuracy and timeliness of risk prediction. By providing real-time warnings and decision-making support, this model offers a novel solution for maritime safety management. Accurate risk forecasts enable ship crews to take precautionary measures in advance, effectively reducing the occurrence of maritime accidents. Full article

Posture recognition is critical in modern educational and office environments for preventing musculoskeletal disorders and maintaining cognitive performance. Existing methods based on human keypoint detection typically rely on convolutional neural networks (CNNs) and single-scale features, which limit representation capacity and suffer from overfitting under small-sample conditions. To address these issues, we propose MSBN-SPose, a Multi-Scale Bayesian Neuro-Symbolic Posture Recognition framework that integrates geometric features at multiple levels—including global body structure, local regions, facial landmarks, distances, and angles—extracted from OpenPose keypoints. These features are processed by a multi-branch Bayesian neural architecture that models epistemic uncertainty, enabling improved generalization and robustness. Furthermore, a lightweight neuro-symbolic reasoning module incorporates human-understandable rules into the inference process, enhancing transparency and interpretability. To support real-world evaluation, we construct the USSP dataset, a diverse, classroom-representative collection of student postures under varying conditions. Experimental results show that MSBN-SPose achieves 96.01% accuracy on USSP, outperforming baseline and traditional methods under data-limited scenarios. Full article

Accurate prediction of food delivery time is crucial for enhancing operational efficiency and customer satisfaction in real-world logistics and intelligent dispatch systems. To address this challenge, this study proposes a novel symmetric optimization prediction framework, termed SymOpt-CNSVR. The framework is designed to leverage the strengths of both deep learning and statistical learning models in a complementary architecture. It employs a Convolutional Neural Network (CNN) to extract and assess the importance of multi-feature data. An Enhanced Superb Fairy-Wren Optimization Algorithm (ESFOA) is utilized to optimize the diverse hyperparameters of the CNN, forming an optimal adaptive feature extraction structure. The significant features identified by the CNN are then fed into a Support Vector Regression (SVR) model, whose hyperparameters are optimized using Bayesian optimization, for final prediction. This combination reduces the overall parameter search time and incorporates probabilistic reasoning. Extensive experimental evaluations demonstrate the superior performance of the proposed SymOpt-CNSVR model. It achieves outstanding results with an R² of 0.9269, MAE of 3.0582, RMSE of 4.1947, and MSLE of 0.1114, outperforming a range of benchmark and state-of-the-art models. Specifically, the MAE was reduced from 4.713 (KNN) and 5.2676 (BiLSTM) to 3.0582, and the RMSE decreased from 6.9073 (KNN) and 6.9194 (BiLSTM) to 4.1947. The results confirm the framework’s powerful capability and robustness in handling high-dimensional delivery time prediction tasks. Full article

This paper addresses a central contradiction in dual-process theories of reasoning: identical tasks produce different outcomes under within-subjects and between-subjects experimental designs. Drawing on two prior studies that exemplify this divergence, we synthesize the empirical patterns into a unified theoretical account. We propose a conceptual framework in which the research design itself serves as a cognitive moderator, influencing the dominance of System 1 (intuitive) or System 2 (analytical) processing. To formalize this synthesis, we introduce a mathematical model that captures the functional relationship between methodological framing, cognitive system engagement, and decision accuracy. The model supports both forward prediction and Bayesian inference, offering a scalable foundation for future empirical calibration. This integration of experimental design and cognitive processing contributes to resolving theoretical ambiguity in dual-process research and opens avenues for predictive modeling of reasoning performance. By formalizing dual-process cognition through dynamic system analogies, this study contributes a continuous modeling approach to performance fluctuations under methodological asymmetry. Full article

This paper proposes a hybrid predictive maintenance framework that combines the discriminative power of XGBoost with the interpretability of a Bayesian Network automatically learned from sensor data. Targeted at textile manufacturing equipment operating under Industry 4.0 conditions, the system addresses the trade-off between early fault detection and decision transparency. Sensor data, including vibration, temperature, and electric current, were collected from a multi-needle quilting machine using a custom IoT-based platform. A degradation-aware labeling scheme was implemented using historical maintenance logs to assign semantic labels to sensor readings. A Bayesian Network structure was learned from this data via a Hill Climbing algorithm optimized with the Bayesian Information Criterion, capturing interpretable causal dependencies. In parallel, an XGBoost model was trained to improve classification accuracy for incipient faults. Experimental results demonstrate that XGBoost achieved an F1-score of 0.967 on the high-degradation class, outperforming the Bayesian model in raw accuracy. However, the Bayesian Network provided transparent probabilistic reasoning and root cause explanation capabilities—essential for operator trust and human-in-the-loop diagnostics. The integration of both models yields a robust and interpretable solution for predictive maintenance, enabling early alerts, visual diagnostics, and scalable deployment. The proposed architecture is validated in a real production line and demonstrates the practical value of hybrid AI systems in bridging performance and interpretability for predictive maintenance in Industry 4.0 environments. Full article

Background/Objectives: Sepsis remains one of the leading causes of mortality worldwide, characterized by a complex and heterogeneous clinical presentation. Despite advances in patient monitoring and biomarkers, early detection of sepsis in the intensive care unit (ICU) is often hampered by incomplete data and diagnostic uncertainty. In recent years, machine learning models have been proposed as predictive tools, but many function as opaque “black boxes”, meaning that humans are unable to understand algorithmic reasoning, poorly suited to the uncertainty-laden clinical environment of critical care. Even when post-hoc interpretability methods are available for these algorithms, their explanations often remain difficult for non-expert clinicians to understand. Methods: In this clinical perspective, we explore the specific advantages of probabilistic graphical models, particularly Bayesian Networks (BNs) and their dynamic counterparts (DBNs), for sepsis prediction. Results: Recent applications of AI models in sepsis prediction have demonstrated encouraging results, such as DBNs achieving an AUROC of 0.94 in early detection, or causal probabilistic models in hospital admissions (AUROC 0.95). These models explicitly represent clinical reasoning under uncertainty, handle missing data natively, and offer interpretable, transparent decision paths. Drawing on recent studies, including real-time sepsis alert systems and treatment-effect modeling, we highlight concrete clinical applications and their current limitations. Conclusions: We argue that BNs present a great opportunity to bridge the gap between artificial intelligence and bedside care through human-in-the-loop collaboration, transparent inference, and integration into clinical information systems. As critical care continues to move toward data-driven decision-making, Bayesian models may offer not only technical performance but also the epistemic humility needed to support clinicians facing uncertain, high-stakes decisions. Full article

Clean water security demands autonomous systems that sense, reason, and act at scale. We introduce AquaSurveil, a unified multi-agent platform coupling mobile robots, fixed IoT nodes, and privacy-preserving machine learning for continent-scale water health surveillance. The architecture blends Gaussian-process mapping with distributed particle filtering, multi-agent deep-reinforcement Voronoi coverage, GAN/LSTM anomaly detection, and sheaf-theoretic data fusion; components are tuned by Bayesian optimization and governed by Age-of-Information-aware power control. Evaluated on a 2.82-million-record dataset (1940–2023; five countries), AquaSurveil achieves up to 96% spatial-coverage efficiency, an ROC-AUC of 0.96 for anomaly detection, ≈95% state-estimation accuracy, and reduced energy consumption versus randomized patrols. These results demonstrate scalable, robust, and energy-aware water quality surveillance that unifies robotics, the IoT, and modern AI. Full article

Knowledge tracing, the computational modeling of student learning progression through sequential educational interactions, represents a critical component for adaptive learning systems and personalized education platforms. However, existing approaches face a fundamental trade-off between predictive accuracy and interpretability: deep sequence models excel at capturing complex temporal dependencies in student interaction data but lack transparency in their decision-making processes, while probabilistic graphical models provide interpretable causal relationships but struggle with the complexity of real-world educational sequences. We propose a hybrid architecture that integrates transformer-based sequence modeling with structured Bayesian causal networks to overcome this limitation. Our dual-pathway design employs a transformer encoder to capture complex temporal patterns in student interaction sequences, while a differentiable Bayesian network explicitly models prerequisite relationships between knowledge components. These pathways are unified through a cross-attention mechanism that enables bidirectional information flow between temporal representations and causal structures. We introduce a joint training objective that simultaneously optimizes sequence prediction accuracy and causal graph consistency, ensuring learned temporal patterns align with interpretable domain knowledge. The model undergoes pre-training on 3.2 million student–problem interactions from diverse MOOCs to establish foundational representations, followed by domain-specific fine-tuning. Comprehensive experiments across mathematics, computer science, and language learning demonstrate substantial improvements: 8.7% increase in AUC over state-of-the-art knowledge tracing models (0.847 vs. 0.779), 12.3% reduction in RMSE for performance prediction, and 89.2% accuracy in discovering expert-validated prerequisite relationships. The model achieves a 0.763 F1-score for early at-risk student identification, outperforming baselines by 15.4%. This work demonstrates that sophisticated temporal modeling and interpretable causal reasoning can be effectively unified for educational applications. Full article

Background: Millimetric ST-segment elevation (STEMI) rules miss more than half of angiographic coronary occlusions. Re-casting acute infarction as Occlusion MI (OMI) versus Non-Occlusion MI (NOMI) and embedding that paradigm in Bayesian reasoning could shorten time to reperfusion while limiting unnecessary activations. Methods: We derived age- and sex-specific baseline prevalences of OMI from national emergency-department surveillance data and contemporary angiographic series. Pre-test probabilities were adjusted with published likelihood ratios (LRs) for chest-pain descriptors and clinical risk factors, then updated again with either (1) the stand-alone accuracy of ST-elevation or (2) the pooled accuracy of a broader OMI ECG spectrum. Two decision thresholds were prespecified: post-test probability >10% to trigger catheterization and >75% to justify fibrinolysis when angiography was unavailable. The framework was applied to five consecutive real-world cases that had elicited diagnostic disagreement in clinical practice. Results: The Bayesian scaffold re-classified three “NSTEMI” tracings as intermediate or high-probability OMI (post-test 27–65%) and prompted immediate reperfusion; each was confirmed as a totally occluded artery. A fourth patient with crushing pain and a normal ECG retained a 17% post-ECG probability and was later found to have an occluded circumflex. The fifth case, an apparent South-African-Flag pattern, initially rose to 75% but fell after a normal bedside echo and normal troponins. Conclusions: Layering pre-test context with sign-specific LRs transforms ECG interpretation from a binary rule into a transparent probability calculation. The OMI/NOMI Bayesian framework detected occult occlusions that classic STEMI criteria missed. Full article

Background: In recent decades, dermatological diagnostics have undergone a profound transformation, driven by the integration of new technologies alongside traditional methods. Classic techniques such as the Tzanck smear, potassium hydroxide (KOH) preparation, and Wood’s lamp examination remain fundamental in everyday clinical practice due to their simplicity, speed, and accessibility. At the same time, the development of non-invasive imaging technologies and the application of artificial intelligence (AI) have opened new frontiers in the early detection and monitoring of both neoplastic and inflammatory skin diseases. Methods: This review aims to provide a comprehensive overview of how conventional and emerging diagnostic tools can be integrated into dermatologic practice. Results: We examined a broad spectrum of diagnostic methods currently used in dermatology, ranging from traditional techniques to advanced approaches such as digital dermoscopy, reflectance confocal microscopy (RCM), optical coherence tomography (OCT), line-field confocal OCT (LC-OCT), 3D total body imaging systems with AI integration, mobile applications, electrical impedance spectroscopy (EIS), and multispectral imaging. Each method is discussed in terms of diagnostic accuracy, clinical applications, and potential limitations. While traditional methods continue to play a crucial role—especially in resource-limited settings or for immediate bedside decision-making—modern tools significantly enhance diagnostic precision. Dermoscopy and its digital evolution have improved the accuracy of melanoma and basal cell carcinoma detection. RCM and LC-OCT allow near-histological visualization of skin structures, reducing the need for invasive procedures. AI-powered platforms support lesion tracking and risk stratification, though their routine implementation requires further clinical validation and regulatory oversight. Tools like EIS and multispectral imaging may offer additional value in diagnostically challenging cases. An effective diagnostic approach in dermatology should rely on a thoughtful combination of methods, selected based on clinical suspicion and guided by Bayesian reasoning. Conclusions: Rather than replacing traditional tools, advanced technologies should complement them—optimizing diagnostic accuracy, improving patient outcomes, and supporting more individualized, evidence-based care. Full article

Traditional educational assessment systems suffer from inefficient question selection strategies that fail to optimally probe student knowledge while requiring extensive testing time. We present a novel hierarchical probabilistic neural framework that integrates Bayesian inference with symmetric deep neural architectures to enable adaptive, efficient knowledge assessment. Our method models student knowledge as latent representations within a graph-structured concept dependency network, where probabilistic mastery states, updated through variational inference, are encoded by symmetric graph properties and symmetric concept representations that preserve structural equivalences across similar knowledge configurations. The system employs a symmetric dual-network architecture: a concept embedding network that learns scale-invariant hierarchical knowledge representations from assessment data and a question selection network that optimizes symmetric information gain through deep reinforcement learning with symmetric reward structures. We introduce a novel uncertainty-aware objective function that leverages symmetric uncertainty measures to balance exploration of uncertain knowledge regions with exploitation of informative question patterns. The hierarchical structure captures both fine-grained concept mastery and broader domain understanding through multi-scale graph convolutions that preserve local graph symmetries and global structural invariances. Our symmetric information-theoretic method ensures balanced assessment strategies that maintain diagnostic equivalence across isomorphic concept subgraphs. Experimental validation on large-scale educational datasets demonstrates that our method achieves 76.3% diagnostic accuracy while reducing the question count by 35.1% compared to traditional assessments. The learned concept embeddings reveal interpretable knowledge structures with symmetric dependency patterns that align with pedagogical theory. Our work generalizes across domains and student populations through symmetric transfer learning mechanisms, providing a principled framework for intelligent tutoring systems and adaptive testing platforms. The integration of probabilistic reasoning with symmetric neural pattern recognition offers a robust solution to the fundamental trade-off between assessment efficiency and diagnostic precision in educational technology. Full article

Based on the Copula theory, a multiple correlation analysis model for the operation safety risks of long-distance water diversion projects was established. Combined with Bayesian network reasoning, a polynomial regression analysis, and other techniques, a dynamic analysis method for the operation safety of long-distance water diversion projects based on a Copula Bayesian network model was proposed, providing decision support for the operation safety risk management of long-distance water diversion projects. We took the Middle Route Project of the South-to-North Water Diversion Project as an example to verify the validity and practicability of the model. The results show that this method can capture the nonlinear mapping relationship when the probability of risk occurrence changes dynamically on the basis of considering the risk correlation, and realize the dynamic analysis of risk correlation. Full article

We investigate the affine term structure models (ATSMs) with unspanned stochastic volatility (USV). Our aim is to test their ability to generate accurate cross-sectional behavior and time-series dynamics of bond yields. Comparing the restricted models and those with USV, we test whether they produce both reasonable estimates for the short rate variance and cross-sectional fit. Essentially, a joint approach from both time series and options data for estimating risk-neutral dynamics in ATSMs should be followed. Due to the scarcity of derivative data in emerging markets, we estimate the model using only time-series of bond yields. A Bayesian estimation approach combining Markov Chain Monte Carlo (MCMC) and the Kalman filter is employed to recover the model parameters and filter out latent state variables. We further incorporate macro-economic indicators and GARCH-based volatility as external validation of the filtered latent volatility process. The $A_1\left(4\right)$USV performs better both in and out of sample, even though the issue of a tension between time series and cross-section remains unresolved. Our findings suggest that even without derivative instruments, it is possible to identify and interpret risk-neutral dynamics and volatility risk using observable time-series data. Full article

Bayesian networks (BNs) are effective and universal tools for addressing uncertain knowledge. BN learning includes structure learning and parameter learning, and structure learning is its core. The topology of a BN can be determined by expert domain knowledge or obtained through data analysis. However, when many variables exist in a BN, relying only on expert knowledge is difficult and infeasible. Therefore, the current research focus is to build a BN via data analysis. However, current data learning methods have certain limitations. In this work, we consider a combination of expert knowledge and data learning methods. In our algorithm, the hard constraints are derived from highly reliable expert knowledge, and some conditional independent information is mined by feature selection as a soft constraint. These structural constraints are reasonably integrated into an exponential Monte Carlo with counter (EMCQ) hyper-heuristic algorithm. A comprehensive experimental study demonstrates that our proposed method exhibits more robustness and accuracy compared to alternative algorithms. Full article

Poisoning and suffocation accidents occurred frequently in the pre-treatment pool workshops of biogas plants, so this paper provided a multi-dimensional risk analysis model: Bow-Tie-Qualitative Comparative Analysis (QCA)-Bayesian Neural Network-Consequence Simulation. First, the reasons for biogas poisoning and suffocation accidents were clarified through Bow-Tie. Then, the QCA method explored the accident cause combination paths in management. Next, the frequency distribution of biogas poisoning and suffocation accidents in the pre-treatment pool workshop was predicted to be 0.61–0.66 using the Bayesian neural network model, and the uncertainty of the forecast outcome was given. Finally, the ANSYS Fluent 16.0 simulation of biogas diffusion in three different ventilation types and a grid-independent solution of the simulation were conducted. The simulation results showed the distribution of methane, carbon dioxide and hydrogen sulfide gases and the hazards of the three gases to workers were analyzed. In addition, according to the results, this paper discussed the importance and necessity of ventilation in pre-treatment pool workshops and specified the hazard factors in biogas poisoning and suffocation accidents in the pre-treatment pool workshops. Some suggestions on gas alarms were also proposed. Full article