Search Results (1,070)

Accurate multi-horizon Significant Wave Height (SWH) forecasting is vital for offshore safety and efficiency. Beyond scheduling maintenance windows, reliable lead-time predictions provide critical early warnings to protect personnel and high-value assets from hazardous high-wave conditions. However, the non-stationary and multi-scale nature of sea states poses challenges for consistent long-term accuracy. To address this challenge, we propose a robust three-stage framework for decomposition, feature selection, and multi-horizon forecasting. Specifically, Optimal Variational Mode Decomposition (OVMD) is adopted to construct multiscale and multi-view representations of nonlinear SWH sequences, while a Triangulated Maximally Filtered Graph (TMFG) constructs a sparse dependency network to select informative and non-redundant predictors from decomposed components and environmental variables. A hybrid prediction model then combines a Temporal Convolutional Network (TCN) for local multi-scale patterns with a Bidirectional Gated Recurrent Unit (BiGRU) for long-range dependencies. Experiments on real-world buoy observations show that the proposed approach improves accuracy and robustness over commonly used statistical and deep-learning baselines across short-, medium-, and long-term horizons. Ablation studies confirm that integrating modal decomposition with sparse feature selection enhances model robustness, offering reliable decision support for offshore window planning and high-wave condition monitoring. Full article

With the rapid aging of the global population, constipation has become a major gastrointestinal concern among elderly individuals. Bowel sounds provide a non-invasive acoustic signal for assessing gastrointestinal function, but their automatic analysis remains challenging due to sparsity and non-stationarity. This study proposes a two-stage bowel sound analysis framework based on continuous abdominal recordings. First, a Convolutional Neural Network-MAMBA (CNN-MAMBA) model was used for salient bowel sound detection. Second, a patient-level constipation classification model was developed using multi-view spectral representations and a Convolutional Neural Network-Conformer-Multiple Instance Learning (CNN-Conformer-MIL) architecture. On a held-out test set, the detection model achieved an accuracy of 0.87, an F1-score of 0.78, and a ROC-AUC of 0.93. For patient-level classification under binary Bristol Stool Form Scale (BSFS) grouping, five-fold cross-validation yielded a mean accuracy of 0.665 and an F1-score of 0.755. All BSFS labels were annotated by clinical physicians and temporally aligned with bowel sound recording. Given the modest improvement and cross-validation variability, the patient-level results should be interpreted as preliminary feasibility evidence. These findings suggest that bowel sound analysis may serve as an auxiliary screening or longitudinal monitoring tool rather than a stand-alone diagnostic system. Full article

High dynamic range (HDR) image reconstruction from a single low dynamic range (LDR) input remains an important problem for computational photography, particularly when practical deployment on consumer-grade hardware is considered. With the increasing availability of hardware supporting HDR, public demand for capturing and viewing HDR images has grown significantly. Recent research has explored deep learning-based approaches to reconstruct HDR images from low dynamic range (LDR) inputs by extracting regional pixel features or leveraging the camera response function (CRF) for model training. Many of these approaches employ Convolutional Neural Network (CNN) architectures and utilize skip connections to preserve learned information. Nevertheless, the configuration-level effects of data augmentation in HDR reconstruction remain insufficiently discussed. Existing CNN-based approaches, such as HDRCNN, HDRUNet, and ExpandNet, have demonstrated promising reconstruction ability, but they may involve a heavy backbone architecture, a long training time, or a limited discussion of how preprocessing configurations affect reconstruction performance. This study presents an engineering-oriented HDR reconstruction framework derived from HDRCNN, focusing on practical efficiency, structural fidelity, and training feasibility. The proposed framework introduces three modifications: (1) a configuration-level comparison of composite data augmentation settings, including unsharp masking, denoising, Gaussian blur, and brightness–contrast adjustment; (2) the replacement of the original VGG16 backbone with a ResNet50-based encoder enhanced with attention blocks and squeeze-and-excitation (SE) blocks for improved multi-scale feature extraction and channel-wise recalibration; and (3) the integration of mixed-precision training with cosine annealing learning-rate scheduling to reduce computational cost. Experimental results on the SI-HDR dataset show that the best composite augmentation configuration improves PSNR from 19.05 dB to 22.10 dB and SSIM from 0.6444 to 0.7714 without increasing the training time. Compared with the original VGG16-based HDRCNN setting, the ResNet50-based model reduces training time while improving SSIM from 0.2705 to 0.8512. Under the adopted comparison protocol, the proposed model achieves the shortest training time and slightly higher PSNR than HDRUNet, while HDRUNet retains a higher SSIM. This indicates a trade-off among pixel-wise fidelity, structural similarity, and computational efficiency. The current evaluation is limited by a small test setting, composite rather than operation-level augmentation analysis, and the use of PSNR and SSIM only; therefore, future work should include full benchmark evaluation, additional perceptual/HDR-specific metrics, and controlled component-level ablation studies. Full article

Accurate traffic flow prediction is fundamental to Intelligent Transportation Systems (ITSs), critical for transportation management and logistics. Despite advances in spatio-temporal prediction methods, existing approaches suffer from two key limitations: (i) multi-scale fusion methods inadequately capture hierarchical constraints between cross-scale features, and (ii) models rely on single spatio-temporal views, neglecting multi-source relationship complementarity. To address these issues, we propose ST-MAFNet, a spatio-temporal multi-scale adaptive fusion network comprising three key components, specifically, a Cross-Scale Hierarchical Anchoring strategy (CSHA) that anchors short-term predictions with multi-scale temporal patterns to mitigate noise; a Dual Spatial Perception Module (DSPM) that learns node heterogeneity and dynamic correlations through node embeddings and adaptive graph attention; and a Spatio-Temporal Adaptive Fusion Module (STAFM) that captures time-varying connectivity by integrating multi-scale temporal features with multi-source spatial relationships. Experiments on four real-world datasets demonstrate that ST-MAFNet is particularly effective for short-term traffic forecasting. Compared with the best previously reported MAE results, ST-MAFNet reduces MAE by 2.95%, 1.43%, 1.25%, and 0.37% on PEMS03, PEMS04, PEMS07, and PEMS08, respectively, and achieves the best or second-best performance on most evaluation metrics. Full article

On current social media platforms, multimodal fake news has permeated various fields. Multi-domain fake news detection has garnered significant attention in the academic community. Existing multi-domain methods primarily employ feature fusion techniques based on text–image alignment, neglecting the extraction of conflicting information across modalities and failing to address the domain-dependent nature of cross-modal feature conflicts. To address this, we propose a Multi-Granularity Conflict and Domain-Guided Calibration for Multimodal Fake News Detection model (GC²MFND). This model captures conflicting features through the domain-aware multi-granularity conflict extraction module and mitigates feature suppression using the domain-guided multimodal feature calibration module. Finally, it combines domain-adaptive aggregation with multi-view evidence integration to achieve robust decision-making under supervised contrastive learning constraints. Under known domain conditions, the experimental results demonstrate that GC²MFND outperforms existing multi-domain baseline methods, achieving accuracy rates of 95.3%, 95.7%, and 81.2% on the Weibo, Weibo21, and FineFake datasets, respectively, representing improvements of 1.1%, 1.2%, and 1.4% over the corresponding multi-domain baselines. Full article

Background: Artificial intelligence (AI) is increasingly being proposed for postharvest food-safety control of animal-source foods, but its practical value depends on whether models can support real decisions rather than only report high accuracy. Methods: This narrative review used a structured literature mapping of peer-reviewed work, mainly from 2020 to 2025, identified through database searches and citation tracking using combined terms for artificial intelligence, machine learning, animal-source foods, postharvest food safety, slaughterhouse inspection, cold-chain monitoring, traceability, authenticity, HACCP, validation, and regulatory applicability. Results: The most implementation-proximate applications are computer vision prescreening in slaughterhouses and processing plants, sensor- and IoT-based cold-chain surveillance, freshness and adulteration screening, and digital traceability systems. Across these areas, stronger evidence is associated with clearly defined control points, transparent reference methods, external or temporal validation, auditable data flows, and documented human oversight. The main weaknesses are single-site datasets, retrospective designs, incomplete reporting of reference methods, limited workflow testing, and insufficient attention to false alerts, fallback procedures, and governance. Conclusions: AI should be viewed as targeted decision support, not as a replacement for established food-safety control. Future studies should prioritize prospective, multi-site, workflow-embedded validation and show how alerts lead to documented corrective or verification actions. Full article

Shortwave infrared (SWIR) imagery plays an important role in land–water boundary delineation, coastal monitoring, and complex aquatic environment observation. However, the spatial resolution of SWIR bands is usually lower than that of visible bands, which limits their capability to represent fine-scale targets and boundary structures. To address this problem, this study proposes MLE-ResUNet, a SWIR image super-resolution method that integrates along-track oversampling with visible-light-guided deep learning. The proposed method first exploits dual-view SWIR observations with sub-pixel displacement generated by increasing the sampling line rate in the push-broom imaging process. A maximum likelihood estimation (MLE)-based physical prior module is then introduced to transform multi-view degraded observations into a physically consistent latent high-resolution prior. Finally, high-resolution visible images are used to provide edge, texture, and structural guidance, and a ResUNet-based network is employed for multi-source feature fusion and residual reconstruction. Based on multi-region measured data acquired by the LHRSI (Lightweight High-Resolution Spectral Imager) payload onboard the BlueCarbon-1A satellite, a SWIR super-resolution dataset covering typical urban, farmland, and coastal scenarios was constructed. Comparative experiments were conducted against PCA, BDSD, PanNet, GPPNN, and two additional lightweight-guided deep learning baselines, namely LGPConv and a CANConv-style visible-guided baseline. The results show that MLE-ResUNet achieves the best performance across different scenarios and consistently outperforms the comparison methods in terms of SSIM, SAM, ERGAS, and Q-index. The proposed method effectively enhances spatial detail recovery while maintaining favorable spectral consistency. Ablation experiments further demonstrate that both along-track oversampling information and the MLE-based physical prior contribute to improved reconstruction quality and more stable training convergence. These findings indicate that the proposed method can enhance fine-scale SWIR observation capability without substantially increasing hardware complexity, providing an effective technical solution for shoreline identification, land–water boundary extraction, and complex surface target monitoring. Full article

Intelligent autonomous driving systems must not only predict the appropriate driving manoeuvre but also provide human-interpretable evidence that justifies the decision. However, existing methods typically address these objectives separately, leading to three practical limitations: multi-stage perception-to-language pipelines can propagate upstream perception errors into downstream explanations; post hoc saliency methods often produce pixel-level highlights that are difficult to interpret semantically; and decoupled decision and explanation modules cannot guarantee that the explanation reflects the same scene evidence used for behaviour prediction. In this paper, we propose a unified framework that jointly performs vehicle behaviour prediction and human-centric interpretation from a shared visual backbone. Specifically, a hierarchical Swin Transformer encodes the driving scene into a sequence of spatial tokens, which are processed by two complementary branches. The first branch, termed the Object Selection Module (OSM), learns a compact scene-level semantic representation through query-guided cross-attention, while the second branch extracts a small set of class-agnostic object-centric tokens without requiring bounding-box or segmentation supervision. These two representations are subsequently integrated by a Semantic–Object Fusion (SOF) module based on scaled dot-product attention, residual connections, and a feed-forward network. The behaviour prediction head operates on the fused representation, whereas the interpretation head leverages the semantic representation through a skip connection to preserve decision-relevant context. For surround-view perception, learnable per-camera embeddings are introduced to maintain viewpoint identity with negligible additional parameter cost. Furthermore, a compact language model fine-tuned via Low-Rank Adaptation (LoRA) generates fluent, label-conditioned natural-language justifications. Extensive experiments on two public benchmarks, BDD-OIA and nu-AD, demonstrate that the proposed framework consistently delivers superior performance and provides effective, human-readable interpretations of driving decisions. Full article

Soil moisture (SM) is a critical variable in land–atmosphere water and energy exchange, and synthetic aperture radar (SAR) observations offer an effective means for large-scale and fine-resolution SM monitoring. Sentinel-1A, with its all-time and all-weather capability, has become an indispensable data source for SM retrieval, while comprehensive comparisons of machine learning and deep learning methods for regional and global scale SM retrieval remain insufficient. In this study, four widely used machine learning (ML) algorithms, including random forest (RF), eXtreme gradient boosting (XGBoost), convolutional neural network (CNN), and long short-term memory (LSTM), are evaluated for SM retrieval from Sentinel-1A observations across the International Soil Moisture Network (ISMN) at global and regional scales. Multiple-source dynamic parameters, including Sentinel-1A observations, MODIS vegetation parameters, ERA5-Land meteorological and soil variables, are used as inputs, as well as static geospatial parameters. Validation results demonstrate that tree-based ensemble methods (RF and XGBoost) consistently outperform deep learning methods across all scales. Specifically, XGBoost achieves the best performance with satisfactory SM retrieval results. Moreover, XGBoost is insensitive to Sentinel-1A viewing geometry, allowing fusion of multi-orbit observations to improve temporal resolution without accuracy loss. These findings demonstrate the effectiveness of tree-based ML for global/regional SM retrieval from Sentinel-1A. In addition, this study performs a comprehensive evaluation of spatial generalization ability and orbit robustness of different retrieval models under global heterogeneous environments, and proposes a reliable scheme for generating high-spatiotemporal-resolution SM products. Full article

Continuous Integration pipelines rely on large-scale automated testing to support rapid releases. However, flaky tests exhibit non-deterministic outcomes under an identical code and configuration, substantially increasing rerun costs and hindering fault localization. Existing approaches struggle to uniformly model heterogeneous runtime evidence and its multi-relational structure in CI environments, which limits cross-project generalization and interpretability. To address this gap, this paper presents HgtFlaky, a runtime-evidence-centered multi-view heterogeneous graph learning framework. A Unified Event Model is introduced to normalize heterogeneous CI artifacts into semantically consistent event quadruples, and a heterogeneous execution graph is then constructed to capture testing entities and multiple relation types. Based on the HEG, three complementary views are derived to characterize run-level, test-level, and thread-level flaky behaviors. A heterogeneous graph Transformer is further adopted to jointly encode the multi-view graph instances and learn transferable test-level representations for flaky/non-flaky prediction. Experiments on two benchmark datasets, FlakeFlagger and IDoFT, show that HgtFlaky achieves strong and stable performance. Under 10-fold cross-validation, it obtains an F1-score of 83% on FlakeFlagger and 98% on IDoFT. Under per-project validation on FlakeFlagger, HgtFlaky achieves 78% Precision, 89% Recall, and 81% F1-score, outperforming Flakify by 8 percentage points and FlakeFlagger by 74 percentage points in F1-score. Full article

Three-dimensional (3D) shape recognition is a fundamental task in computer vision, where view-based methods have recently achieved state-of-the-art performance. However, effectively capturing and exploiting the rich geometric correspondences between different views remains a key challenge, as such information is crucial for accurate shape representation. Existing methods often fall short in explicitly modeling these structured correlations, which limits their ability to fully leverage discriminative shape information. To address this limitation, we propose a novel View-based Graph Convolution and Sampling Fusion Network (View-GFN). View-GFN employs a hierarchical architecture that progressively coarsens the view-graph to learn multi-scale features. In this structure, views are treated as graph nodes, and a predefined-value strategy is introduced to initialize the adjacency matrix (AM) for constructing initial node correlations. For effective graph coarsening, we develop a novel view down-sampling method based on a cluster assignment matrix. Furthermore, a Graph Convolution and Sampling Fusion (CSF) module is designed to seamlessly integrate deep feature embeddings with the topological information derived from view down-sampling. Extensive experiments on benchmark datasets, including ModelNet40 and RGB-D, demonstrate that View-GFN achieves strong performance, performing on par with established baseline methods while reducing the number of model parameters by nearly 50% compared to the baseline View-GCN. These results validate the effectiveness of our hierarchical fusion strategy in capturing multi-view geometric information both efficiently and robustly. Full article

Anomaly detection on attributed graphs is essential for scientific integrity, cybersecurity, and financial oversight, where abnormal patterns often manifest as breaks in structure or attributes. However, existing unsupervised methods are difficult to combine both global and local perspectives to detect anomalies. To address this issue, we propose DCGAD, a unified unsupervised framework that captures anomalies by fusing global reconstruction error and local view inconsistency. Our model leverages diffusion reconstruction to strengthen global semantic information, employing two parallel autoencoders to reconstruct the graph structure based on the original features and diffusion-enhanced features, respectively, to capture global structural differences. Complementarily, the model samples two local subgraph views per target node and uses multi-view contrastive learning to evaluate local contextual inconsistencies. By jointly optimizing these two complementary objectives, our proposed model achieves collaborative use of local and global information. Extensive experiments on six real-world graph datasets show that DCGAD outperforms other state-of-the-art approaches, achieving excellent scores on citation networks and significant gains on social and collaborative platforms. Full article

Accurate and reliable prediction of lettuce fresh weight is essential for optimising protected cultivation management and improving the yield and quality. Multimodal data combined with machine learning models have been widely used for monitoring crop growth. However, existing approaches often fail to capture dynamic physiological changes during crop growth, whereas conventional machine learning models are frequently limited by their black-box nature and thus cannot reveal the intrinsic relationships between features and targets. To address the above issues, this study developed a stationary, multi-sensor integrated data acquisition platform under controlled greenhouse conditions. By fusing multi-view morphological structure, color and texture, and multispectral features, the study constructed interpretable machine learning models for predicting the fresh weight of lettuce. Based on the data collected by the platform, 66 initial features covering morphology, color texture, and vegetation indices were extracted from the data. A two-stage feature-selection strategy combining Pearson correlation screening and variance inflation factor (VIF)-based multicollinearity elimination was used to select nine optimal input variables for the model. To achieve an accurate estimation of the fresh weight of lettuce, the system compared six models: Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosted Decision Tree Regression (GBDT), K-nearest neighbour regression (KNN), XGBoost, and Backpropagation Neural Network (BPNN). The results indicate that the SVR model based on multimodal data fusion performed best, with an R² of 0.93, an RMSE of 3.23 g, an RMSEn of 5.60%, and an MAE of 2.31 g, demonstrating a significantly higher prediction accuracy than the other models. Furthermore, the SHAP interpretation method was used to reveal the contributions of key features to fresh weight estimation and their interaction mechanisms. This study provides a feasible approach and technical guidance for non-destructive estimation of fresh weight in lettuce under controlled conditions, and offers a preliminary basis for the development of phenotypic monitoring models for protected cultivation. Full article

Fraud detection on multi-relational graphs is challenging because real-world fraud-related data often contains heterogeneous relations, noisy structures, and imbalanced labels. Existing GNN-based methods usually rely on predefined graph structures, but real-world financial graphs often contain noisy, redundant, or missing relations, which undermine neighborhood aggregation and message passing. In addition, single-view learning is insufficient to capture both local structural patterns and high-order semantic dependencies, limiting performance in complex fraud scenarios. To address this issue, we propose HGSO-DVCL, a heterogeneous graph structure optimization framework with dual-view contrastive learning. The framework performs type-aware structure optimization for each relation subgraph and integrates optimized graphs with the original structure through channel attention. A dual-view encoder then learns complementary representations from the network schema view and the meta-path view, while contrastive learning promotes consistency and complementarity between the two perspectives. An end-to-end objective jointly optimizes fraud classification, structure regularization, and contrastive alignment. Experiments on public multi-relational fraud detection benchmarks show that HGSO-DVCL achieves strong and competitive performance, while ablation and sensitivity studies support the effectiveness and stability of the proposed framework under the evaluated benchmark settings. Full article

The widespread use of mobile Internet- and location-based services has generated large-scale check-in data in location-based social networks, creating opportunities for intelligent urban-mobility analysis and personalized mobility services. Making the next point-of-interest (POI) recommendation is an important task in this setting because it supports context-aware destination suggestion, travel assistance, and smart mobility services. However, existing methods still face challenges in jointly modeling higher-order mobility patterns, uneven time intervals, geographic reachability, and fine-grained intra-day temporal regularities. To address these issues, this paper proposes ASTHN, an Adaptive Spatio-Temporal Hypergraph Network for next POI recommendation. ASTHN constructs three fine-grained spatio-temporal context hypergraphs from minimum time interval, spatial proximity, and hourly preference, and uses hypergraph neural networks to learn view-specific POI representations. A context-adaptive fusion module then aligns and integrates multi-source spatio-temporal signals, while an ST-GRU with spatio-temporal gates captures dynamic trajectory evolution. Temperature scaling is further applied at the output layer to alleviate overly concentrated score distributions. Experiments on Foursquare-NYC and Foursquare-TKY show that ASTHN consistently outperforms representative baselines. With results reported as mean ± std over three random seeds, ASTHN improves over the strongest baseline by 3.79%, 14.62%, 2.28%, and 1.24% on NYC in Recall@5, Recall@10, NDCG@5, and NDCG@10, respectively. On TKY, the corresponding improvements are 5.83%, 37.20%, 13.86%, and 20.49%. Ablation, parameter, complexity, and application-oriented case analyses further demonstrate the effectiveness, stability, and practical usability of ASTHN for next POI recommendation in urban-mobility scenarios. Full article