Search Results (733)

Quicker identifying abilities are required due to the rising frequency and severity of wildfires. Although polar-orbiting satellites with medium and high resolution can accurately identify wildfires, the majority of available fire detection images originate from such platforms. However, their low temporal revisit rates restrict the potential for early warning. Geostationary satellites provide minute-level, continuous monitoring that corresponds with the quick onset of wildfires; however, their dependence on conventional threshold methods and coarse spatial resolution result in notable detection errors. This study developed an integrated deep learning framework for accurate wildfire detection in low-resolution geostationary imagery in order to get over these restrictions. A novel dynamic index, the Dynamic Normalized Burn Ratio—Thermal (DNBRT), was proposed to characterize wildfire progression by integrating instantaneous thermal anomalies with dynamic vegetation signals. Based on this, a Fire Spatiotemporal Network (FST-Net) was designed, with an efficient residual backbone, a Convolutional Block Attention Module (CBAM) for feature refinement, and a Bidirectional Long Short-Term Memory (BiLSTM) network to capture temporal evolution. Trained and evaluated on an FY-4B-based fire/non-fire dataset, the proposed framework demonstrated superior performance. FST-Net outperformed benchmark models, improving accuracy and recall by averages of 10.30% and 9.32% respectively while achieving faster inference speed. An ablation experiment confirmed the critical role of fusing thermal and vegetation features in DNBRT, with 92.7% accuracy and 94.9% recall. Compared to the FY-4B fire product, the proposed framework enables earlier detection, maintains more complete tracking of fire progression, and exhibits greater robustness under complex burning conditions while achieving sub-hectare (0.36 ha) detection sensitivity at the 2 km resolution. By synergizing a discriminative dynamic index with an efficient spatiotemporal architecture, this work provides an effective solution for operational, real-time monitoring of small and early-stage wildfires from geostationary satellites. Full article

Short-term wind power forecasting is considered a critical challenge in smart grid management due to the nonlinear, unstable, and multi-scale noise characteristics of wind signals. Although recent advances in hybrid deep learning have improved the accuracy of short-term wind power forecasting, many state-of-the-art models usually consider signal denoising, residual decomposition, and attention mechanisms as independent modules without providing a unified solution. This paper proposes an end-to-end solution, W-HiTS-Attention (Wavelet Transform, N-stacked Hierarchical Interpolation for Time Series, Attention), which coherently integrates wavelet denoising, hierarchical residual learning from N-HiTS (Neural Hierarchical Interpolation), and an in-block self-attention mechanism. The proposed solution outperforms 21 benchmarks in accuracy, including state-of-the-art baselines such as N-BEATS, N-HiTS, TCN, Informer, Autoformer, LSTM, BiLSTM, GRU, and Prophet, achieving an RMSE of 55.56 W and an R² of 0.9918. Furthermore, the results show that the proposed solution is efficient in terms of parameter count (0.033M), latency (0.0036 ms/sample), and training time, making it promising for low-latency inference in resource-constrained environments. The results show that the coherent integration of frequency preprocessing, hierarchical residual forecasting, and attention-based temporal refinement provides a robust, explainable, and deployable solution for short-term wind power forecasting. Full article

Online and blended classrooms widen access but remove the in-person cues instructors use to gauge attention. Prior work typically relies on heavy, cloud-bound or multimodal models that are hard to deploy on commodity laptops, treats attention as an unordered label without calibrated probabilities, and evaluates on subject-overlapping splits with limited robustness analysis. This creates a gap in Tiny, deployable, calibration-aware methods validated under realistic protocols. We address this gap with a TinyML, vision-only pipeline that estimates four attention levels: (Very Low, low, high, Very High ) from short webcam clips under strict on-device budgets. Each clip of $T=30$ frames at $224\times 224$ is processed by a compact hybrid encoder: a CNN extracts per frame spatial features, a BiLSTM models temporal context, and a lightweight GRU refines dynamics; three parallel branches with staggered widths encourage feature diversity before fusion. We apply structured pruning of convolutional channels and recurrent units, post-training INT8 quantization, and temperature scaling for calibrated probabilities; models are exported as ONNX. On DAiSEE with subject-independent splits, the baseline attains $99.86\%$ accuracy and $0.998$ macro-F1, with strong ordinal agreement (QWK = 0.998, ordinal MAE = 0.03). The compressed model preserves reliability (macro-F1 = 0.995, QWK = 0.995), remains robust to low light, partial occlusion, and head yaw, and yields ∼4× smaller size and ∼2.3× CPU speedups. These results indicate a deployable, privacy-preserving approach to fine-grained, on-device attention analytics. Full article

Instance segmentation of plant point clouds is challenging due to intricate structures, non-uniform density, and large intra-class variation. Conventional methods often suffer from blurred boundaries, instance adhesion, and insufficient coupling of semantic and instance features. To address these issues, this paper proposes MPRSF-CSA, a novel network integrating recursive slice-based feature extraction with an attention-embedding mechanism. The method first transforms disordered point clouds into ordered sequences via a multi-directional recursive slicing strategy and models inter-slice dependencies using BiLSTM. Parallel decoding branches for semantic and instance segmentation are constructed, and a core attention-embedding module facilitates bidirectional fusion of semantic and instance features. Instance segmentation is achieved via clustering and semantic-aware optimization. Experiments on two public datasets demonstrate that MPRSF-CSA outperforms existing approaches in segmentation accuracy, boundary preservation, and adaptability to complex plant scenes. Full article

Traditional sleep staging studies typically rely on signals collected using contact-based sensors, which may interfere with the natural sleep state of subjects and thus affect the authenticity and reliability of the recorded data. To address this limitation, this study proposes an automatic sleep staging method based on non-contact single-channel ballistocardiogram (BCG) signals. First, band-pass filtering is applied to the raw BCG signals to separate the heart rate and respiratory components. Heart rate variability (HRV) and respiratory rate variability (RRV) features are then extracted, and mutual information is used to select key feature subsets that exhibit strong correlations with different sleep stages. Considering the complexity and prominent temporal characteristics of real-world sleep data, a temporal modeling network named TLA-SleepNet is constructed to enhance the model’s capability in capturing complex sequential features and improving robustness. Experiments conducted on 10 independent sleep recordings containing a total of 10,614 sleep epochs demonstrate that, under subject non-independent testing conditions with five-fold cross-validation, the proposed method achieves an accuracy of 87.1% in the sleep staging task, with precision, kappa coefficient, and F1-score reaching 92.4%, 81.9%, and 88.7%, respectively. The results indicate that the proposed method can achieve a reliable sleep staging performance without direct contact between sensors and the human body, providing a feasible solution for non-contact sleep monitoring in home-based and mobile healthcare applications. Full article

Aiming at the issues of low recognition accuracy and high model computational complexity for power quality disturbances (PQDs) in strong-noise environments, this paper proposes a novel lightweight PQD-recognition method that integrates a hybrid architecture of variational mode decomposition (VMD), convolutional neural network (CNN), and transformer. Firstly, a hybrid optimization algorithm named the monkey–genetic hybrid optimization algorithm (MGHOA) is proposed to optimize VMD parameters for denoising disturbance signals, thereby enhancing recognition accuracy in noisy environments. Secondly, to fully extract disturbance signal features and reduce the computational complexity of the model, a lightweight CNN–transformer model is designed. Depthwise separable convolution (DSC) is employed to extract local features and the multi-head attention mechanism of transformer is utilized to mine the long-distance dependence and global features, thereby enhancing the feature representation. Thirdly, a multitask joint-learning method is proposed to collaboratively optimize classification accuracy and temporal localization tasks, enhancing the discrimination of similar disturbances. Additionally, a dual-pooling global feature fusion strategy is designed to further enhance the model’s ability to discriminate complex disturbances. Comparative experiments on 16 typical PQD types demonstrate that the proposed method achieves excellent performance in recognition accuracy, model robustness, and computational efficiency. The integration of the MGHOA–VMD module improves recognition accuracy by 1.08%, while the multitask joint-learning method contributes an additional 0.55% improvement. When achieving recognition accuracy comparable to complex models, the training time of the proposed method is 36.51% of that required by DeepCNN and merely 5.90% of that required by bidirectional long short-term memory (BiLSTM), with a 31.22% reduction in parameter scale. This work provides a novel solution for intelligent power quality disturbance recognition. Full article

Monolithic zirconia restorations are frequently affected by the unnoticed growth of subcritical cracks, a failure process that is not captured by traditional imaging methods like radiographs and ultrasounds in sophisticated dental architectures. To address this evaluative inadequacy, this research introduces a hierarchical deep learning framework for microcrack detection and spatial localization. We promote a hierarchical deep learning system that integrates Acoustic Emission (AE) detection alongside signal processing. Raw AE signals utilized during dynamic loading are enhanced via Kalman filtering and Continuous Wavelet Transform (CWT) to construct high-fidelity time–frequency scalograms. The diagnostic pipeline operates in two stages: first, a hybrid CNN–BiGRU network with temporal attention fulfills zirconia component-level classification; second, a ResNet-18 backbone integrated with Bidirectional LSTM and Multi-Head Attention precisely localizes defects across five anatomical crown regions. This hierarchical design effectively captures the non-stationary, transient nature of fracture-induced stress waves. The framework achieved an F1-score of 99.00% and an AUC of 0.994, significantly outperforming conventional convolutional networks. By enabling predictive maintenance through early, non-invasive damage localization, this study demonstrates a promising laboratory framework for AE-based crack detection in zirconia dental structures and prosthetics and toward enhanced clinical reliability in digital dentistry. Full article

Transparent clinical decision-making remains a critical barrier to deploying deep learning in medical diagnosis. Post hoc explanation methods approximate model behaviour after training but cannot guarantee that explanations faithfully reflect the underlying reasoning. This study proposes a Self-Explaining Neural Network (SENN) for Parkinson’s Disease (PD) screening via Ground Reaction Force (GRF) gait analysis, enforcing intrinsic interpretability through learnable basis concepts and input-dependent relevance scores computed jointly with the prediction. The architecture combines a four-block residual CNN backbone with stochastic depth regularisation, a 16-concept encoder with diversity and stability constraints, and temperature-scaled probability calibration for reliable clinical operating points. Evaluated on the PhysioNet Gait in Parkinson’s Disease dataset (306 subjects, 16 GRF sensors per foot), SENN achieves a subject-level ROC-AUC of 0.916 [95% CI: 0.867–0.964], sensitivity of 0.913 [0.862–0.963], specificity of 0.671 [0.485–0.858], and Average Precision of 0.942 [0.918–0.967], reported across five independent random seeds. Comparative evaluation against four deep learning baselines—CNN-Residual, BiLSTM, CNN-LSTM, and CNN-Attention—confirms that the interpretability constraints impose no statistically significant reduction in discriminative performance, with all pairwise ROC-AUC confidence intervals overlapping. Concept-level analysis reveals that the three most discriminative concepts correspond to disrupted midfoot loading patterns, increased step-length variability, and bilateral cadence asymmetry—all established biomechanical hallmarks of parkinsonian gait—providing clinically grounded, patient-specific explanations without post hoc approximation. These findings demonstrate that rigorous intrinsic interpretability and competitive predictive accuracy are simultaneously achievable in deep gait analysis, supporting the clinical adoption of transparent diagnostic AI. Full article

To improve ship AIS trajectory prediction under pronounced spatiotemporal coupling and dynamic maneuvering conditions, this study proposes a Spatio-Temporal-Hybrid-Multi-head Attention model (STHA) integrating multiscale convolution, bidirectional long short-term memory, and multi-head attention. Historical AIS data from the Zhoushan waters in 2024 were preprocessed through screening, cleaning, outlier removal, resampling, and cubic spline interpolation to construct trajectory samples. Comparative experiments were conducted against BP, BiLSTM, and BiGRU using MAPE, RMSE, and R² as evaluation metrics. The results show that STHA achieves the best overall predictive performance, more accurately follows trajectory variations across different vessel types, and exhibits better robustness in scenarios involving turning and speed changes. These findings indicate that the proposed model is effective for high-precision ship trajectory prediction and can provide useful support for subsequent collision risk assessment and navigation safety assistance. Full article

The rapid growth of e-commerce has highlighted the critical need for efficient customer review sentiment analysis, yet natural language complexities like sarcasm and mixed sentiments remain challenging. To address these ambiguities, this study proposes a novel sentiment analysis architecture. The methodology integrates a bidirectional Long Short-Term Memory (Bi-LSTM) network with a Luong Attention mechanism. The Bi-LSTM component models the sequential and bidirectional context of the text, while the Luong Attention mechanism isolates and emphasizes the most significant parts of the reviews for precise sentiment detection. The proposed hybrid model demonstrates exceptional performance compared to traditional methods, achieving an accuracy of 96.67%, a precision of 96.83%, and a recall of 96.67%, alongside relatively low overfitting. Ultimately, the findings confirm that this architecture effectively manages ambiguous language and is highly capable of large-scale, real-time sentiment analysis, offering robust analytical tools for shaping e-commerce marketing strategies. Full article

The widespread dissemination of fake news across different domains exerts a negative impact on social order. Current fake news detection models face two major challenges. First, the issue of domain shift constrains the generalization capability of models in cross-domain scenarios. Second, general neural networks struggle to extract features between distant words in text, resulting in poor quality of original features and adversely affecting the final detection outcomes. In response to the aforementioned issues, this paper proposes a multi-domain fake news detection framework based on multi-view hybrid attention enhancement. Firstly, superior original feature extraction is achieved through Recurrent Convolutional Neural Networks (RCNN) and Bidirectional Long Short-Term Memory (BiLSTM). Secondly, a hybrid attention mechanism is established between features and domains across multiple views—including news semantics, sentiment, and style—thereby forming domain-specific memory. This enables the model to achieve more precise classification of news within specific, subdivided domains. Finally, experiments conducted on the public dataset Weibo21 demonstrate that the proposed method attains F1 scores of 93.26% and 85.31% on Chinese and English datasets. Full article

Accurate vessel speed prediction is essential for maritime traffic supervision, navigational safety, and intelligent coastal management. However, due to the nonlinear, time-varying, and context-dependent characteristics of vessel motion in nearshore waters, conventional single-model approaches often fail to provide sufficiently accurate forecasts. To address this issue, this study proposes a hybrid deep learning framework for Automatic Identification System (AIS)-based nearshore vessel speed prediction and risk warning, integrating a temporal convolutional network (TCN), an attention mechanism, and a bidirectional long short-term memory network (BiLSTM) into a unified architecture. The core novelty of this framework is its task-oriented sequential design, in which TCN extracts local temporal patterns and multi-scale sequence features from historical AIS observations, the attention mechanism adaptively emphasizes informative representations, and BiLSTM models bidirectional contextual dependencies in vessel motion sequences; on this basis, a speed-risk warning process is constructed by combining the predicted speed with electronic-fence threshold constraints. Experiments conducted on real AIS data from coastal waters show that the proposed method obtains lower mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) as well as a higher coefficient of determination (R²) than several benchmark models. The results illustrate that the proposed framework effectively improves vessel speed prediction accuracy within the studied coastal area and provides practical support for proactive maritime supervision and nearshore safety management. Full article

Incipient faults in power cables are difficult to diagnose because their transient signatures are weak, non-stationary, and easily masked by background noise, while labeled real-world samples are often scarce. To address these challenges, this paper proposes an offline diagnosis framework that integrates Whale Optimization Algorithm (WOA)-guided CEEMDAN with a TCN-BiLSTM-Multi-HeadAttention network. The proposed method has three main features. First, WOA is explicitly mapped to the CEEMDAN parameter optimization problem and is used to adaptively optimize the noise amplitude and ensemble number, thereby improving decomposition quality and enhancing weak fault-related components. Second, the optimized intrinsic mode functions are reconstructed into a multi-channel representation that preserves complementary fault information across different frequency bands. Third, a hybrid deep architecture combining Temporal Convolutional Networks, Bidirectional Long Short-Term Memory, and multi-HeadAttention is designed to jointly capture local transient characteristics, bidirectional temporal dependencies, and fault-sensitive feature interactions. Experimental results on both PSCAD/EMTDC simulation data and real-world measured data show that the optimized WOA-CEEMDAN achieves superior decomposition performance, with an RMSE of 0.097 and an SNR of 8.42 dB. On the real-world test dataset, the proposed framework achieves 96.00% accuracy, 97.25% precision, 96.84% recall, an F1-score of 0.970, and an AUC of 0.97, outperforming several representative baseline models. Additional ablation, noise-robustness, small-sample, confusion-matrix, and cross-cable validation results further demonstrate the effectiveness and robustness of the proposed framework for incipient cable fault diagnosis. Full article

Classifying power quality disturbances (PQDs) under strong noise conditions remains challenging for existing deep learning models. These models typically separate denoising from feature extraction, often rely on attention mechanisms that operate along only a single dimension, and tend to achieve high accuracy at the cost of high complexity, which limits their performance under low signal-to-noise ratio conditions and hinders practical deployment. To address these limitations, this paper proposes AD-PDAF-Net, which organically integrates three key mechanisms through a co-design strategy. Unlike conventional methods that depend on preprocessing, an adaptive soft thresholding denoising layer is embedded into a lightweight residual network to progressively suppress noise during feature extraction, thereby unifying denoising with feature learning. A parallel dual attention module independently refines features along the channel and temporal dimensions, then adaptively fuses them using learnable weights to capture both frequency domain and temporal characteristics of disturbances. The lightweight network entry replaces aggressive downsampling with small convolutions to preserve transient details, and a bidirectional long short-term memory network (BiLSTM) efficiently captures temporal dependencies. Evaluated on a dataset of 25 disturbance categories defined in IEEE Std 1159-2019, the model achieves a classification accuracy of 97.26% and a Kappa coefficient of 97.02% under 20 dB white Gaussian noise, along with an accuracy of 98.78% under mixed noise conditions. The model has only 0.36 million parameters and a computational cost of just 1.50 GFLOPS. Through this co-design, AD-PDAF-Net achieves both high noise robustness and high classification accuracy with minimal computational overhead, offering an effective solution for time series signal recognition in resource constrained environments. Full article

With increasing port automation and operational intensity, the gearboxes of gantry cranes widely used in bulk cargo terminals are prone to bearing and gear failures under prolonged heavy loads, intense vibrations, and complex operating conditions. Since fault samples often exhibit imbalanced distributions, this imposes two higher requirements on diagnostic methods—first, the ability to effectively address sample imbalance and, second, the capability to simultaneously identify multiple fault categories. To address these challenges, this paper proposes a joint diagnostic method integrating an improved Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) and Multi-Task Learning (MTL). First, the modified CWGAN-GP performs conditional augmentation for minority fault classes, evaluating synthetic sample authenticity and diversity through multiple metrics. Subsequently, a multi-channel diagnostic network is constructed, in which vibration signals are fed into two parallel sub-networks: time–frequency features are extracted from the Short-Time Fourier Transform (STFT)-based time–frequency representations via a residual-block Convolutional Neural Network (CNN), while temporal features are captured from the raw time-domain signal using a Bidirectional Long Short-Term Memory (Bi-LSTM) with an attention mechanism. An attention fusion layer then integrates these two feature types, enabling joint classification of bearings and gears within a multi-task learning framework. Experimental validation on public gearbox datasets and port gantry crane gearbox datasets demonstrates that this method achieves an average diagnostic accuracy exceeding 97%. The proposed method reduces the impact of class imbalance, thereby improving the accuracy and stability of multi-task fault identification. Full article