Search Results (514)

Background: Differentiating alcohol-related liver disease (ARLD) from chronic liver injury caused by other etiologies remains a clinically relevant challenge in cross-sectional imaging. In particular, alcoholic hepatitis and alcoholic cirrhosis may overlap morphologically with HBV-related cirrhosis on CT imaging. To develop and assess an attention-residual hybrid Convolutional Neural Network (CNN) for multiclass CT image classification of alcoholic hepatitis, alcoholic cirrhosis, HBV-related cirrhosis, and living liver donors. Methods: A four-class liver image dataset comprising 5760 CT images from 144 individuals (36 per group) was constructed using images from alcoholic hepatitis, alcoholic cirrhosis, HBV-related cirrhosis, and living liver donors. The dataset was partitioned into training, validation, and test sets at the patient level. Five pretrained CNN architectures, including DenseNet121, ResNet50, MobileNetV3-Large, EfficientNetB0, and ConvNeXt-Tiny, were first fine-tuned and comparatively evaluated. Based on F1-score ranking, DenseNet121 and ConvNeXt-Tiny were selected as the two backbone networks for the proposed hybrid model. The final architecture integrated Attention Pooling, Feature-wise Linear Modulation (FiLM), Multi-head Attention, Gated Linear Units, Residual Connections, and layer normalization to improve feature fusion and contextual representation. Results: The proposed model demonstrated the best overall performance among all evaluated architectures on the test set. It achieved an accuracy of 99.55%, a weighted F1-score of 99.55%, an MCC of 0.9941, and a Cohen’s kappa coefficient of 0.9940. The model also achieved ROC-AUC and PR-AUC values of 100.00% and 99.99%, respectively, together with an NPV of 99.85%. The proposed model’s performance was also balanced across classes. For Alcoholic Cirrhosis, precision, recall, and F1-score were all 99.64%. For Alcoholic Hepatitis, the corresponding values were 100.00%, 98.93%, and 99.46%, respectively, while HBV-related Cirrhosis achieved 99.29% precision, 99.64% recall, and 99.47% F1-score. Living Liver Donors achieved 99.29% precision, 100.00% recall, and 99.64% F1-score. Conclusions: The findings of this exploratory study suggest that routine CT images may contain image-based differences potentially relevant to etiology-oriented classification of diffuse liver disease. Beyond distinguishing ARLD from HBV-related cirrhosis and images from living liver donors, the model also captured image-based differences between major ARLD subgroups, including alcoholic hepatitis and alcoholic cirrhosis. These findings support further investigation of CT-derived image-based differences in larger independent and multicenter datasets. Full article

Electromagnetic scattering from plunging breaking waves generates strong sea-surface radar returns that degrade radar-based maritime surveillance and target detection performance. This study develops a deep learning framework for automatic classification of simulated plunging-breaker scattering conditions using high-range-resolution profile (HRRP) data. The electromagnetic scattering data are generated using a physics-based Capillary Wave Modification Facet Scattering Model (CWMFSM) combined with ray-tracing techniques. Eight simulated plunging-breaker scattering conditions are constructed by combining two wind speeds, 7 m/s and 10 m/s, with four temporal conditions, Δt1, Δt10, Δt14, and Δt16. A total of 8000 HRRP samples are generated, with 100 normalized range-cell features extracted from each sample. Two deep learning classifiers, an artificial neural network (ANN) and a one-dimensional residual convolutional neural network (1D ResNet CNN), are comparatively evaluated. The ANN achieves an overall classification accuracy of 96%, compared with 91% for the 1D ResNet CNN under the simulated dataset and adopted model configurations. Robustness analysis under controlled additive white Gaussian noise (AWGN) conditions further shows that classification performance decreases as the signal-to-noise ratio is reduced, while noise-augmented training improves the robustness of both classifiers. Overall, the results demonstrate the feasibility of HRRP-based deep learning for distinguishing simulated plunging-breaker scattering conditions from sea-surface radar returns, providing a basis for further investigation of sea-clutter characterization and maritime radar applications. Full article

Efficient parsing of manufacturing process data is a key enabler for the informatization of intelligent manufacturing systems. However, network isolation in aerospace-specific job shops makes large volumes of unstructured shop-floor data, such as handwritten production reports and equipment logs, inaccessible to existing information systems. To tackle this, we propose a comprehensive parsing framework that covers data acquisition, parsing, and structured output. For handwritten report parsing, we devise a collaborative pipeline comprising text detection via the Differentiable Binarization Network (DBNet); text recognition using an enhanced Convolutional Recurrent Neural Network (CRNN) that incorporates Asymmetric Convolution (AC) and a Simple Attention Module (SimAM)-based residual module (SimRes, short for SimAM ResNet), referred to as AC-SimRes-CRNN; and table structure extraction via TableMaster. The predicted table cell coordinates, detected text-region coordinates, and recognized text contents are subsequently aggregated to reconstruct complete tables, which are then exported as Excel files. Experiments on the CASIA-HWDB2x and IAM datasets show that AC-SimRes-CRNN achieves an Accurate Rate (AR) of 91.36% and a Correct Rate (CR) of 93.17% on Chinese handwritten text recognition and a Character Error Rate (CER) of 7.85% and a Word Error Rate (WER) of 26.83% on English handwritten text recognition, demonstrating competitive performance against representative methods. Ablation studies validate the contributions of both AC and SimRes. A case study on an aerospace equipment maintenance report further illustrates the component-level feasibility of the proposed workflow. Full article

Bone marrow lesion (BML) volume is an essential biomarker for understanding knee osteoarthritis (KOA). However, automatic BML segmentation remains challenging due to the irregular shapes and indistinct boundaries of these lesions in knee magnetic resonance images (MRI). To improve BML segmentation, this study investigated two established strategies in the specific context of BML segmentation: (1) integrating bone segmentation as an additional output channel in deep neural networks to facilitate BML segmentation, and (2) incorporating test-time augmentation (TTA) to reduce uncertainty during testing. The added bone segmentation channel provides auxiliary anatomical information that may facilitate BML localization. TTA was used to improve boundary alignment and reduce false positives by generating more robust predictions. Multiple State-of-the-Art deep neural networks for segmentation were employed as the baseline models to compare performance before and after implementing the proposed strategies. A 10-fold cross-validation was conducted on a dataset of knee MR scans from 300 participants. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) for overlap accuracy and the 95% Hausdorff Distance (HD95) for boundary alignment. Paired t-tests were used to assess the significance of improvements from the proposed strategies. Both strategies produced improvements in segmentation performance, although the magnitude and statistical significance of the improvements varied across architectures. The DSC improved from 63.1% to 64.8% for Residual U-Net, 64.2% to 65.8% for Swin UNETR, 61.5% to 66.5% for Attention U-Net, and 66.6% to 69.0% for UNet++. These gains were accompanied by improvements in boundary accuracy and reductions in false positives, reflected in lower HD95 values. Comparison with additional medical image segmentation models under the same evaluation framework showed that the dual-channel UNet++ with TTA achieved the highest BML DSC of 69.0%, followed by U-Mamba at 68.6% and nnU-Net at 65.2%, while U-Net + InceptionResNet-v2 achieved 57.7%. These findings support the potential value of dual-channel and TTA strategies for automated BML analysis, while further validation on independent datasets is needed to assess their broader generalizability and clinical utility. Full article

Granulation is a major physiological disorder that compromises sweet orange quality. Conventional destructive detection methods lead to food waste and cannot be used for batch inspection. This study proposes a nondestructive approach for detecting granulation in sweet oranges using air-jet transient excitation and a laser Doppler vibrometer (LDV). Acoustic vibration spectra were acquired from 640 sweet orange samples. Using both competitive adaptive reweighted sampling (CARS)-extracted feature parameters and raw acoustic vibration spectra as inputs, an ISNet-1D model integrating a multi-scale Inception module and a squeeze-and-excitation (SE) attention mechanism was developed, and its performance was compared against those of partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), K-nearest neighbor (KNN), random forest (RF), and baseline deep learning models including one-dimensional convolutional neural network (1D-CNN), Visual Geometry Group network 16 (VGG16), and residual network v1 (ResNet-v1). The results demonstrated that the ISNet-1D model trained on the full raw vibration spectrum achieved the best performance, with an overall test set accuracy, recall, and specificity of 92.97%, 95.00%, and 91.18%, respectively. Ablation experiments revealed that removal of the Inception branches and the SE module reduced the overall test accuracy by 5.47% and 4.69%, respectively, indicating that their combination effectively extracts multi-scale acoustic vibration features and enhances model precision. Gradient-weighted class activation mapping further identified the critical frequency bands primarily relied upon by the model for prediction. Collectively, noncontact acoustic vibration detection combined with ISNet-1D provides a viable method for nondestructive granulation detection in sweet oranges. Full article

Background: Breast cancer brain metastases (BCBMs) exhibit notable receptor discordance between primary tumors and metastases, yet obtaining intracranial biopsies is rarely practical. We created an interpretable deep learning radiogenomic model to predict estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status from MRI scans in BCBM cases. Methods: A total of 241 post-contrast T1-weighted MRIs from 142 patients were analyzed. We developed a mask-free 3D Residual Neural Network (ResNet) ensemble trained directly on cropped, bias-corrected brain images, with comprehensive 3D geometric and intensity augmentations. The model was optimized in a multi-label setting using Asymmetric Focal Loss. Hyperparameters were fine-tuned via Bayesian optimization, and class probabilities were calibrated. Additionally, Integrated Gradients (IG) offered voxel-level saliency maps. Results: Our ResNet ensemble model achieved a micro-averaged AUROC of 0.74 and a macro-averaged AUROC of 0.67. Receptor-specific AUROCs were 0.57 for ER, 0.79 for PR, and 0.64 for HER2. The F1-scores were 0.56, 0.62, and 0.88 for ER, PR, and HER2, respectively. The held-out cohort contained only four HER2-negative patients, so threshold-dependent HER2 metrics are strongly prevalence-driven and AUROC is the more appropriate summary. Qualitatively inspected saliency maps were concentrated on enhancing metastases with limited background attribution. Conclusions: This proof-of-concept study shows that a segmentation-free, interpretable 3D Convolutional Neural Network (CNN) can be trained to capture receptor-associated patterns in post-contrast T1-weighted MRI of BCBMs. Accuracy was modest relative to published radiomics models, and the mask-free, single-sequence design is offered as a methodological contribution rather than a performance gain. These findings are preliminary, do not establish clinical utility, and require validation in larger, multi-center cohorts. Full article

Nitrogen is a major determinant of crop growth, yield formation, and grain quality, making precise nitrogen (N) management essential for sustainable production. This study integrated field canopy spectra acquired with an ASD FieldSpec 4 spectrometer and unmanned aerial vehicle (UAV)-based RGB imagery to estimate leaf nitrogen content in broomcorn millet across four growth stages. Vegetation indices (VIs) and gray-level co-occurrence matrix texture features were derived, and six algorithms—partial least squares (PLS), support vector machine (SVM), random forest (RF), one-dimensional convolutional neural network (CNN1D), one-dimensional residual network (ResNet1D), and one-dimensional U-Net (U-Net)—were evaluated. Among the spectral preprocessing methods, the first-derivative transformation showed the strongest relationship with leaf nitrogen content (r = −0.85). Models based on multi-source feature fusion consistently outperformed those using a single data source. The U-Net model using ASD + RGB + VIs + texture features achieved the highest test-set accuracy (R² = 0.913, RMSE = 1.851, and RPD = 3.382). SHapley Additive exPlanations (SHAP) analysis identified blue-band correlation (B_Correlation) as the most influential individual predictor (mean absolute SHAP value = 0.39), while texture features accounted for 45.170% of the cumulative feature importance. These results demonstrate the potential of multi-source remote sensing and deep learning for rapid, non-destructive assessment of leaf nitrogen content and precision nitrogen management in broomcorn millet. Full article

Reliable scene understanding under open-world conditions requires intelligent perception systems to accurately recognize known semantic categories while remaining robust to out-of-distribution (OOD) inputs, distribution shifts, and uncertain environmental conditions. This challenge becomes increasingly important for resource-constrained edge intelligence, where lightweight models are expected to provide reliable predictions without sacrificing computational efficiency. Although knowledge distillation has achieved remarkable success in compressing deep neural networks, existing methods primarily transfer classification semantics and often neglect the uncertainty representations that are critical for reliable open-set perception. To address this issue, we propose Residual Feature-driven Knowledge Distillation (RFKD), a lightweight uncertainty-aware distillation framework for reliable open-set scene understanding under distribution shift. Instead of directly distilling output confidence or energy scores, RFKD reconstructs uncertainty within the student’s latent feature space through a compact residual uncertainty branch. The proposed framework combines confidence-aware supervision, relational uncertainty distillation, and energy-guided relative ordering to preserve teacher-induced uncertainty geometry while enabling the student to learn discriminative feature-level uncertainty representations. The present study is evaluated on unimodal image data and does not claim empirical validation for multimodal perception. Extensive experiments on CIFAR-100 using multiple OOD benchmarks demonstrate that RFKD consistently improves uncertainty estimation while maintaining high computational efficiency. Compared with the ResNet-50 Teacher (Energy), RFKD increases the average AUROC from 0.7793 to 0.8446 while reducing the model size from 23.71 M to 11.29 M parameters and computational complexity from 1.31 G to 0.56 G FLOPs; the ImageNet-style student baseline obtains an AUROC of 0.7528. A score-specific sensitivity analysis shows that the energy detector is strongest when the auxiliary ordering score uses the residual branch alone, whereas moderate coupling with the student’s log-sum-exp potential improves the standalone OOD head. These results demonstrate that explicitly modeling representation-level uncertainty offers an effective and efficient solution for reliable scene understanding, providing a practical reliability enhancement for future intelligent perception systems operating in open and dynamic environments. Full article

The management of estuarine ecosystem sustainability is a complex problem that requires models that are physically sound, socially meaningful, and interpretable from a mechanistic standpoint. Even though classical AI has demonstrated promise in environmental forecasting, black-box models typically fall short of meeting basic conservation requirements or accounting for anthropogenic stresses that alter water quality. This work introduces a novel framework based on Deep Physics-Informed Neural Networks (Deep PINNs) to predict the dynamics of dissolved oxygen (DO) in the Bouregreg Estuary (Morocco). We advance baseline standards by directly integrating the non-linear advection–diffusion–reaction (ADR) transport equations into the loss function of a deep residual architecture (ResNet with 12–20 layers). This integration ensures that the model takes into account two important aspects of estuarine hydrodynamics: gravitational circulation and the salt wedge effect. The incorporation of a socio–hydro–physical nexus, which uses regional water-pricing indices and urban wastewater discharge volumes from the Rabat-Salé municipal area $(\sim \mathrm{120,000} {\mathrm{m}}^3/\mathrm{d}\mathrm{a}\mathrm{y})$ as proxy variables for anthropogenic pressure, is a unique aspect of this work. The Deep PINN achieves a better coefficient of determination ($R^2=0.998$) and a Nash–Sutcliffe efficiency ($\mathrm{N}\mathrm{S}\mathrm{E}=0.997$), outperforming the traditional ANFIS and ANN baselines by $89.1\%$ in terms of predictive error reduction ($\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}=0.041\pm 0.002 \mathrm{m}\mathrm{g}/\mathrm{L}$). In situations where unconstrained data-driven models fall short, the framework exhibits physical robustness in capturing vertical DO stratification in addition to numerical accuracy. Urban effluent volumes have a significant impact on predictive variance, accounting for 28% of the model internal attribution—more than the relative influence of thermal solubility, according to mechanistic feature attribution analysis using SHAP (Shapley Additive exPlanations). Finally, exploratory management scenarios suggest that summer hypoxia could hypothetically be mitigated through a $20\%$ reduction in discharge volumes. This study bridges the gap between scientific modeling and policy implementation by providing a physics-consistent digital twin framework for environmental stewardship in support of UN SDG 6 and Morocco’s National Water Plan. Full article

Traffic accidents are one of the most significant societal problems worldwide, resulting in loss of life and property. In recent years, powerful solutions for the automatic classification of traffic accidents have been offered by deep learning-based image processing methods. This study comparatively evaluated four convolutional neural network architectures (SqueezeNet, ResNet-18, ResNet-50 and AlexNet) with different depths and levels of complexity using the CADD (Car Accidents and Deformation) dataset, which consists of images of vehicle accidents. The model’s performance was examined in detail using accuracy, precision, recall, the F1 score, a confusion matrix and an ROC–AUC analysis. The ResNet-50 model significantly outperformed all others, achieving 52% validation accuracy and balanced F1-score values (~0.50–0.52). It also achieved particularly high precision (0.70), recall (0.79) and AUC (0.922) values in the Totaled class. In contrast, SqueezeNet exhibited significant class bias and struggled to learn the multi-class structure. The AlexNet and ResNet-18 models showed moderate performance, achieving limited success in terms of discriminability, particularly in the ‘Severe’ class. It should be emphasized that the analysis relies on single-frame static images and a relatively small, predefined dataset; the study is therefore intended as a preliminary comparative benchmark rather than a deployable damage-assessment system. Within this scope, the findings indicate that residual architectures such as ResNet-50 are promising candidates for future deep learning-based accident analysis tools, although further validation on larger and more diverse datasets is required prior to their real-world use in traffic safety, insurance assessment, and intelligent transportation applications. Full article

Visual anomaly detection has achieved very high accuracy on standard benchmarks, yet state-of-the-art synthesis-based detectors such as GLASS rely on heavy backbones (e.g., WideResNet-50) that are ill-suited for deployment on resource-constrained edge platforms. This study investigates whether a lightweight convolutional neural network (CNN) backbone can retain such accuracy at much lower computational cost. To this end, the heavy backbone within the GLASS anomaly detection framework is replaced with the lightweight MobileNetV2 backbone. The experimental findings demonstrate that this particular choice of feature representation, namely the expanded depthwise features of MobileNetV2 rather than compressed bottleneck outputs, recovers the accuracy otherwise lost by a naive lightweight substitution. A lightweight learnable per-stream gating mechanism is further introduced, adaptively weighting feature streams on a per-category basis and yielding a measurable and consistent improvement at negligible parameter cost. On the MVTec AD benchmark, the proposed model attains a 0.992 mean image-level AUROC, matching or exceeding the ResNet-18 configuration reported by the GLASS authors under an identical training budget, while using 3.3× fewer backbone parameters and 4.7× fewer FLOPs. On a Jetson Nano, it runs 2.4× faster per frame than the ResNet-18 baseline (approximately 19 frames per second), confirming that the efficiency gains translate to usable speed on low-cost edge hardware. The findings are further corroborated on the more challenging VisA benchmark, where the proposed model matches the ResNet-18 configuration on image-level detection and improves pixel-level AUROC. Additionally, the proposed approach experimentally generalizes without modification to two further datasets from different domains, i.e., concrete crack and pharmaceutical pill inspection. A systematic negative result is additionally reported, demonstrating that the handcrafted complementary feature streams (PCA reconstruction-residual and wavelet high-frequency descriptors) do not improve accuracy, and the residual performance gap on difficult categories is attributed to the training schedule rather than to feature representation. This study therefore provides experimental evidence that careful backbone-feature selection, rather than architectural augmentation, is the key to efficient edge-deployable anomaly detection at a minimal cost in accuracy. These findings indicate that high-accuracy visual anomaly detection can be brought within reach of low-cost embedded hardware, lowering the barrier to automated inspection in smaller-scale industrial settings where a dedicated computing workstation is impractical. The source code is made publicly available to support this use. Full article

Fritillaria thunbergii Miq. (ZBM) and Fritillaria hupehensis Hsiao et K.C. Hsia (HBBM) are two common medicinal and edible species of the genus Fritillaria, both possessing high economic and pharmacological value. Given their differences in pharmacological effects and economic value, rapid, nondestructive, and accurate identification of these two species is of practical significance. In this study, hyperspectral imaging (HSI) was used for the species identification of sliced Fritillaria samples. Based on the extracted one-dimensional spectra, conventional machine learning models, including Logistic Regression (LR), Support Vector Classification (SVC), and Extreme Gradient Boosting (XGBoost), as well as deep learning models, including one-dimensional Convolutional Neural Network (1D-CNN), one-dimensional Residual Network (1D-ResNet), Transformer, and CNN-Transformer, were first constructed. The one-dimensional spectra were further transformed into two-dimensional Gramian Angular Difference Field (GADF) images, followed by the construction of two-dimensional Convolutional Neural Network (2D-CNN) and two-dimensional Residual Network (2D-ResNet) models. To further explore structural relationships among spectral samples, graph-structured and hypergraph-structured data were constructed from both one-dimensional spectra and GADF images, and the corresponding Graph Convolutional Network (GCN) and Hypergraph Neural Network (HGNN) models were established. On this basis, a Spectral–Structural Decoupled Hypergraph Neural Network (SSD-HGNN) was proposed, in which deep spectral features extracted by 1D-ResNet were used as node attributes, while deep GADF image features extracted by 2D-ResNet were used to construct hyperedges. This design enabled decoupled fusion of one-dimensional spectral information and two-dimensional structural information within a hypergraph learning framework. The results showed that most models achieved satisfactory identification performance, demonstrating the feasibility of HSI for distinguishing ZBM and HBBM slices. SSD-HGNN achieved competitive overall performance, with accuracies of 0.9848, 0.9533, and 0.9441 on the training, validation, and test sets, respectively. Although SSD-HGNN did not achieve the highest test accuracy among all evaluated models, it achieved the highest validation accuracy and effectively integrated discriminative one-dimensional spectral features with two-dimensional GADF-based structural relationships. These results demonstrate that the proposed spectral–structural decoupled hypergraph learning strategy provides a competitive multimodal approach for rapid and nondestructive identification of Fritillaria species. Full article

The rapid miniaturization and increasing complexity of Printed Circuit Boards (PCBs) have rendered traditional automated optical inspection (AOI) systems inadequate for high-precision defect detection. While deep learning approaches have shown promise, they often struggle to balance the need for high-resolution feature extraction with the computational efficiency required for real-time industrial deployment. Furthermore, the “black-box” nature of most convolutional neural networks (CNNs) limits their adoption in stringent quality assurance environments where interpretability is paramount. To address these challenges, this paper proposes PCBVisionNet, a novel, lightweight deep learning architecture specifically engineered for automated multi-class PCB defect classification. The framework integrates a Dual-Domain Attention Mechanism (DDAM) and a Multi-Scale Feature Extractor (MSFE) with residual learning to effectively capture both microscopic anomalies and complex structural defects, enabling robust image-level classification of PCB defect categories. We evaluate the proposed model on three publicly available datasets: DeepPCB, PKU-Market-PCB, and HRIPCB. Experimental results demonstrate that PCBVisionNet achieves superior classification performance with a mean Average Precision (mAP) of 99.4% across defect categories, outperforming state-of-the-art architectures such as ResNet50, EfficientNet-B0, and Vision Transformers, while requiring 45% fewer parameters and reducing inference time by 23 ms per image. The integration of Gradient-weighted Class Activation Mapping (Grad-CAM) provides post hoc visual explainability, highlighting image regions that influence the classification decision to support interpretability and root-cause analysis. The proposed framework offers a highly accurate, efficient, and interpretable solution for modern smart manufacturing systems. Full article

Wildfires are significant ecological and environmental disasters, impacting forests, ecosystems, climate stability and human life. The visible-spectrum imagery-based traditional wildfire monitoring system can fail to perform well in the presence of smoke, haze and low lighting. A number of machine learning and deep learning techniques have been proposed, but most of the studies do not provide an integrated Short-Wave Infrared (SWIR) optical-electronic payload framework along with an intelligent optimization technique. The objective of this research is to design an intelligent SWIR-based optical-electronic payload architecture for accurate detection and remote sensing of wildfire and Earth applications via deep learning and optimization techniques. The proposed framework is based on Sentinel-2 SWIR satellite data layers with wildfire and non-wildfire samples. To enhance the quality of the images and the representation of their spectral domain, the following preprocessing operations are carried out: resizing, image normalization, SWIR band extraction, and data augmentation. The following spectral feature extraction techniques are then used: burn area analysis, vegetation stress analysis, and thermal anomaly detection. The framework also incorporates SWIR optical payload design, electronic subsystem development and SWIR InGaAs sensor modeling. Finally, a Hybrid Convolutional Neural Network (CNN)–Residual Network 50 (ResNet50) model optimized by Grey Wolf Optimization (GWO) is used for wildfire classification and hyperparameter tuning. The proposed framework achieved an accuracy of 91.03%, precision of 91.27%, recall of 91.03%, and F1-score of 91.01%. The wildfire detection capability, classification robustness, and convergence performance were enhanced through the integration of SWIR spectral analysis, hybrid deep learning and GWO. The proposed framework offers an effective and trustworthy solution for intelligent wildfire monitoring and Earth remote sensing applications with enhanced spectral sensing and classification performance. Full article

Accurate fault location is essential for improving the reliability and service restoration capability of distribution networks. With the increasing penetration of distributed generation, power electronic devices, and flexible loads, fault transient signals become increasingly nonlinear and nonstationary, posing challenges to conventional impedance-based, traveling-wave-based, and feature-engineering-based methods. To improve transient fault feature representation, this paper proposes a wavelet-embedded residual attention convolutional neural network (CNN) for distribution network fault location. The task is formulated as a multi-class classification problem, in which each predefined line section is treated as a candidate fault location class. The proposed method embeds discrete wavelet decomposition into the convolutional feature extraction process, enabling low-frequency trend components and high-frequency transient components to be jointly represented and fused by subsequent trainable network modules. Residual connections improve deep feature propagation, and an attention mechanism enhances fault-sensitive representations. Simulation studies on the IEEE 33-bus distribution system show that the proposed method outperforms multi-layer perceptron (MLP), support vector machine (SVM), standard CNN, ResNet, and Attention-CNN, achieving 98.27% accuracy and a 98.33% F1-score. The class-wise results and robustness tests under different transition resistances, noise levels, and fault types further verify the effectiveness and adaptability of the proposed method. Full article