Search Results (418)

Accurate quality assessment of solar Extreme Ultraviolet (EUV) remote sensing imagery is critical for data reliability in space science and weather forecasting. This study introduces a hybrid framework that fuses deep semantic features from a HyperNet-based model with 22 handcrafted physical and statistical quality indicators to create a robust 24-dimensional feature vector. We used a dataset of top-quality images, i.e., quality class “Excellent”, and generated a dataset of 47,950 degraded, lower-quality images by simulating seven types of degradation including defocus, blur and noise. Experimental results show that an XGBoost classifier, when trained on these fused features, achieved superior performance with 97.91% accuracy and an AUC of 0.9992. This approach demonstrates that combining deep and handcrafted features significantly enhances the classification’s robustness and offers a scalable solution for automated quality control in solar EUV observation pipelines. Full article

BioHealing Optimization (BHO) is a bio-inspired metaheuristic that operationalizes the injury–recovery paradigm through an iterative loop of recombination, stochastic injury, and guided healing. The algorithm is further enhanced by adaptive mechanisms, including scar map, hot-dimension focusing, RAGE/hyper-RAGE bursts (Rapid Aggressive Global Exploration), and healing-rate modulation, enabling a dynamic balance between exploration and exploitation. Across 17 benchmark problems with 30 runs, each under a fixed budget of $1.5·{10}^5$ function evaluations, BHO achieves the lowest overall rank in both the “best-of-runs” (47) and the “mean-of-runs” (48), giving an overall rank sum of 95 and an average rank of 2.794. Representative first-place results include Frequency-Modulated Sound Waves, the Lennard–Jones potential, and Electricity Transmission Pricing. In contrast to prior healing-inspired optimizers such as Wound Healing Optimization (WHO) and Synergistic Fibroblast Optimization (SFO), BHO uniquely integrates (i) an explicit tri-phasic architecture (DE/best/1/bin recombination → Gaussian/Lévy injury → guided healing), (ii) per-dimension stateful adaptation (scar map, hot-dims), and (iii) stagnation-triggered bursts (RAGE/hyper-RAGE). These features provide a principled exploration–exploitation separation that is absent in WHO/SFO. Full article

In order to ensure the earthquake safety of existing buildings, retrofitting applications come to the fore in terms of being fast and cost-effective. Among these applications, fiber-reinforced polymer (FRP) composites are widely preferred thanks to their advantages such as high strength, corrosion resistance, applicability without changing the cross-section and easy assembly. This study presents a data augmentation, modeling, and comparison-based approach to predict the fire resistance (FR) of FRP-strengthened reinforced concrete beams. The aim of this study was to explore the role of data augmentation in enhancing prediction accuracy and to find out which augmentation method provides the best prediction performance. The study utilizes an experimental dataset taken from the existing literature. The dataset contains inputs such as varying geometric dimensions and FRP-strengthening levels. Since the original dataset used in the study consisted of 49 rows, the data size was increased using augmentation methods to enhance accuracy in model training. In this study, Gaussian noise, Regression Mixup, SMOGN, Residual-based, Polynomial + Noise, PCA-based, Adversarial-like, Quantile-based, Feature Mixup, and Conditional Sampling data augmentation methods were applied to the original dataset. Using each of them, individual augmented datasets were generated. Each augmented dataset was firstly trained using eXtreme Gradient Boosting (XGBoost) with 10-fold cross-validation. After selecting the best-performing augmentation method (Adversarial-like) based on XGBoost results, the best-performing augmented dataset was later evaluated in HyperNetExplorer, a more advanced NAS tool that can find the best performing hyperparameter optimized ANN for the dataset. ANNs achieving R² = 0.99, MSE = 22.6 on the holdout set were discovered in this stage. This whole process is unique for the FR prediction of structural elements in terms of the data augmentation and training pipeline introduced in this study. Full article

Gulf cities have embarked on ambitious public transport infrastructure initiatives in recent decades to foster more livable and sustainable cities. This investigation explores the interpretations and implementation of Transit-Oriented Development (TOD) principles in two prototypical urban districts: Doha’s West Bay, Qatar, and Riyadh’s King Abdullah Financial District (KAFD), Saudi Arabia. By following a comparative case study approach, the study explores how retrofitted (West Bay) and purpose-built (KAFD) TOD configurations fare regarding land use mix, density, connectivity, transit access, and environmental responsiveness. The comparative methodology was selected to specifically capture the spatial, climatic, and socio-economic complexities of TOD implementation in hyper-arid urban environments. Based on qualitative evidence from stakeholder interviews, spatial assessments, and geospatial indicators—such as metro access buffers, building shape compactness, and TOD proximity classification—the investigation reflects both common challenges and localized adaptations in hot-desert Urbanism. It emerges that, while benefiting from integrated planning and multimodal connectivity, KAFD’s pedestrian realm is delimited by climatic constraints and inactive active transport networks. West Bay, on the other hand, features fragmented public spaces and low TOD cohesion because of automotive planning heritages. However, it holds potential for retrofit through infill development and tactical Urbanism. The results provide transferable insights that can inform TOD strategies in other Gulf and international contexts facing similar sustainability and mobility challenges. By finalizing strategic recommendations for urban livability improvement through context-adaptive TOD approaches in Gulf cities, the study contributes to the wider discussion of sustainable Urbanism in rapidly changing environments and supplies a reproducible assessment frame for future TOD planning. This study contributes new knowledge by advancing a context-adaptive TOD framework tailored to the unique conditions of hyper-arid Gulf cities. Full article

In recent years, the research trend of HSI classification has focused on the innovative integration of deep learning and Transformer architecture to enhance classification performance through multi-scale feature extraction, attention mechanism optimization, and spectral–spatial collaborative modeling. However, due to the excessive computational complexity and the large number of parameters of the Transformer, there is an expansion bottleneck in long sequence tasks, and the collaborative optimization of the algorithm and hardware is required. To better handle this issue, our paper proposes a method which integrates RWKV linear attention with Transformer through a novel TC-Former framework, combining TimeMixFormer and HyperMixFormer architectures. Specifically, TimeMixFormer has optimized the computational complexity through time decay weights and gating design, significantly improving the processing efficiency of long sequences and reducing the computational complexity. HyperMixFormer employs a gated WKV mechanism and dynamic channel weighting, combined with Mish activation and time-shift operations, to optimize computational overhead while achieving efficient cross-channel interaction, significantly enhancing the discriminative representation of spectral features. The pivotal characteristic of the proposed method lies in its innovative integration of linear attention mechanisms, which enhance HSI classification accuracy while achieving lower computational complexity. Evaluation experiments on three public hyperspectral datasets confirm that this framework outperforms the previous state-of-the-art algorithms in classification accuracy. Full article

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by heterogeneous behavioral and neurological patterns, complicating timely and accurate diagnosis. Behavioral datasets are commonly used to diagnose ASD. In clinical practice, it is difficult to identify ASD because of the complexity of the behavioral symptoms, overlap of neurological disorders, and individual heterogeneity. Correct and timely identification is dependent on the presence of skilled professionals to perform thorough neurological examinations. Nevertheless, with developments in deep learning techniques, the diagnostic process can be significantly improved by automatically identifying and automatically classifying patterns of ASD-related behaviors and neuroimaging features. Method: This study introduces a novel multimodal diagnostic paradigm that combines structured behavioral phenotypes and structural magnetic resonance imaging (sMRI) into an interpretable and personalized framework. A Generalized Additive Model with Interactions (GAMI-Net) is used to process behavioral data for transparent embedding of clinical phenotypes. Structural brain characteristics are extracted via a hybrid CNN–GNN model, which retains voxel-level patterns and region-based connectivity through the Harvard–Oxford atlas. The embeddings are then fused using an Autoencoder, compressing cross-modal data into a common latent space. A Hyper Network-based MLP classifier produces subject-specific weights to make the final classification. Results: On the held-out test set drawn from the ABIDE-I dataset, a 20% split with about 247 subjects, the constructed system achieved an accuracy of 99.40%, precision of 100%, recall of 98.84%, an F1-score of 99.42%, and an ROC-AUC of 99.99%. For another test of generalizability, five-fold stratified cross-validation on the entire dataset yielded a mean accuracy of 98.56%, an F1-score of 98.61%, precision of 98.13%, recall of 99.12%, and an ROC-AUC of 99.62%. Conclusions: These results suggest that interpretable and personalized multimodal fusion can be useful in aiding practitioners in performing effective and accurate ASD diagnosis. Nevertheless, as the test was performed on stratified cross-validation and a single held-out split, future research should seek to validate the framework on larger, multi-site datasets and different partitioning schemes to guarantee robustness over heterogeneous populations. Full article

Crop distribution represents crucial information in agriculture, playing a key role in ensuring food security and promoting sustainable agricultural development. However, existing methods for crop distribution primarily focus on modeling temporal dependencies while overlooking the interactions and dependencies among different remote sensing features, thus failing to fully exploit the rich information contained in multisource satellite imagery. To address this issue, we propose a deep learning-based method named HyperVTCN, which comprises two key components: the ModernTCN block and the TiVDA attention mechanism. HyperVTCN effectively captures temporal dependencies and uncovers intrinsic correlations among features, thereby enabling more comprehensive data utilization. Compared to other state-of-the-art models, it shows improved performance, with overall accuracy (OA) improving by approximately 2–3%, Kappa improving by 3–4.5%, and Macro-F1 improving by about 2–3%. Additionally, ablation experiments suggest that both the attention mechanism(Time-Feature Dual Attention, TiVDA) and the targeted loss optimization strategy contribute to performance improvements. Finally, experiments were conducted to investigate HyperVTCN’s cross-feature and cross-temporal modeling. The results indicate that this joint modeling strategy is effective. This approach has shown potential in enhancing model performance and offers a viable solution for crop classification tasks. Full article

Ground-based LiDAR sensing offers a promising approach for delivering short-range landing feedback to aerial vehicles operating near vertiports and in GNSS-degraded environments. This work introduces a detection system capable of classifying aerial vehicles and estimating their 3D positions with sub-meter accuracy. Using a simulated Gazebo environment, multiple LiDAR sensors and five vehicle classes, ranging from hobbyist drones to air taxis, were modeled to evaluate detection performance. RGB-encoded point clouds were processed using a modified YOLOv6 neural network with Slicing-Aided Hyper Inference (SAHI) to preserve high-resolution object features. Classification accuracy and position error were analyzed using mean Average Precision (mAP) and Mean Absolute Error (MAE) across varied sensor parameters, vehicle sizes, and distances. Within 40 m, the system consistently achieved over 95% classification accuracy and average position errors below 0.5 m. Results support the viability of high-density LiDAR as a complementary method for precision landing guidance in advanced air mobility applications. Full article

In this paper, we propose an ensemble approach for the intelligent fault diagnosis of machinery, which consists of six feature selection methods and classifiers. In the proposed approach, six filters, based on distinct metrics, are utilized. Each filter is combined with an improved sparse representation classifier (ISRC) to form a base model, in which the ISRC is an improved version of a sparse representation classifier and has the advantages of high classification accuracy and being less time consuming than the unimproved version. For each base model, the filter selects a feature subset that is used to train and test the ISRC, where the two hyper-parameters involved in the filter and ISRC are optimized by the binary particle swarm optimization algorithm. The outputs of six base models are aggregated through the cumulative reconstruction residual (CRR), where the CRR is devised to replace the commonly used voting strategy. The effectiveness of the proposed method is verified on six mechanical datasets involving information about bearings and gears. In particular, we conduct a detailed comparison between CRR and voting and carry out an intensive exploration into the question of why CRR is superior to voting in the ensemble model. Full article

Cardiovascular Disease (CVD) remains the number one cause of morbidity and mortality, accounting for 17.9 million deaths every year. Precise and early diagnosis is therefore critical to the betterment of the patient’s outcomes and the many burdens that weigh on the healthcare systems. This work presents for the first time an innovative approach using the DenseNet architecture that allows for the automatic recognition of CVD from clinical data. The data is preprocessed and augmented, with a heterogeneous dataset of cardiovascular-related images like angiograms, echocardiograms, and magnetic resonance images used. Optimizing the deep features for robust model performance is conducted through fine-tuning a custom DenseNet architecture along with rigorous hyper parameter tuning and sophisticated strategies to handle class imbalance. The DenseNet model, after training, shows high accuracy, sensitivity, and specificity in the identification of CVD compared to baseline approaches. Apart from the quantitative measures, detailed visualizations are conducted to show that the model is able to localize and classify pathological areas within an image. The accuracy of the model was found to be 0.92, precision 0.91, and recall 0.95 for class 1, and an overall weighted average F1-score of 0.93, which establishes the efficacy of the model. There is great clinical applicability in this research in terms of accurate detection of CVD to provide time-interventional personalized treatments. This DenseNet-based approach advances the improvement on the diagnosis of CVD through state-of-the-art technology to be used by radiologists and clinicians. Future work, therefore, would probably focus on improving the model’s interpretability towards a broader population of patients and its generalization towards it, revolutionizing the diagnosis and management of CVD. Full article

As face forgery techniques, particularly the DeepFake method, progress, the imperative for effective detection of manipulations that enable hyper-realistic facial representations to mitigate security threats is emphasized. Current spatial domain approaches commonly encounter difficulties in generalizing across various forgery methods and compression artifacts, whereas frequency-based analyses exhibit promise in identifying nuanced local cues; however, the absence of global contexts impedes the capacity of detection methods to improve generalization. This study introduces a hybrid architecture that integrates Efficient-ViT and multi-level wavelet transform to dynamically merge spatial and frequency features through a dynamic adaptive multi-branch attention (DAMA) mechanism, thereby improving the deep interaction between the two modalities. We innovatively devise a joint loss function and a training strategy to address the imbalanced data issue and improve the training process. Experimental results on the FaceForensics++ and Celeb-DF (V2) have validated the effectiveness of our approach, attaining 97.07% accuracy in intra-dataset evaluations and a 74.7% AUC score in cross-dataset assessments, surpassing our baseline Efficient-ViT by 14.1% and 7.7%, respectively. The findings indicate that our approach excels in generalization across various datasets and methodologies, while also effectively minimizing feature redundancy through an innovative orthogonal loss that regularizes the feature space, as evidenced by the ablation study and parameter analysis. Full article

Segmenting cell nuclei in histopathological images is an extremely important process for computational pathology, affecting not only the accuracy of a disease diagnosis but also the analysis of biomarkers and the assessment of cells performed on a large scale. Although many deep learning models can take out global and local features, it is still difficult to find a good balance between semantic context and fine boundary precision, especially when nuclei are overlapping or have changed shapes. In this paper, we put forward a novel deep learning model named Dual-Stream HyperFusionNet (DS-HFN), which is capable of explicitly representing the global contextual and boundary-sensitive features for the robust nuclei segmentation task by first decoupling and then fusing them. The dual-stream encoder in DS-HFN can simultaneously acquire the semantic and edge-focused features, which can be later combined with the help of the attention-driven HyperFeature Embedding Module (HFEM). Additionally, the dual-decoder concept, together with the Gradient-Aligned Loss Function, facilitates structural precision by making the segmentation gradients that are predicted consistent with the ground-truth contours. On various benchmark datasets like TNBC and MoNuSeg, DS-HFN not only achieves better results than other 30 state-of-the-art models in all evaluation metrics but also is less computationally expensive. These findings indicate that DS-HFN provides a capability for accurate nuclei segmentation, which is essential for clinical diagnosis and biomarker analysis, across a wide range of tissues in digital pathology. Full article

Background and Clinical Significance: As a common inflammatory skin disorder, acne vulgaris is classically associated with sebum overproduction, follicular hyper keratinization, and Cutibacterium acnes proliferation. Emerging evidence suggests a link between severe or treatment-resistant acne and metabolic syndrome, characterized by central obesity, insulin resistance, dyslipidemia, and hypertension. This case series aims to explore the clinical overlap between acne and metabolic dysfunction and highlight the relevance of multidisciplinary evaluation. Case Presentation: Three patients with severe acne vulgaris and coexisting metabolic abnormalities were evaluated at a dermatology clinic in Oradea, Romania, between 2023 and 2024. Each patient underwent dermatologic examination, laboratory testing for metabolic and hormonal parameters, and individualized treatment. Management strategies included topical/systemic acne therapies combined with metabolic interventions (lifestyle modifications, metformin (in two cases), and lipid-lowering agents). Case 1 (female, 23) had obesity, insulin resistance, dyslipidemia, and polycystic ovary syndrome (PCOS). Case 2 (male, 19) presented with central obesity and atherogenic dyslipidemia. Case 3 (male, 18) showed insulin resistance, overweight status, and elevated inflammatory markers. All three showed suboptimal response to standard acne treatment. Adjunct metabolic management resulted in partial improvement within 3 months. One patient required isotretinoin after metabolic stabilization. Conclusions: These cases underscore the interplay between acne and metabolic dysfunction. Insulin resistance and systemic inflammation may contribute to therapeutic resistance in acne. Early recognition of metabolic syndrome features in patients with severe acne may improve treatment outcomes. Dermatologists should consider metabolic screening to guide comprehensive, multidisciplinary care. Full article

Background: Gastrointestinal (GI) disorders present significant healthcare challenges, requiring rapid, accurate, and effective diagnostic methods to improve treatment outcomes and prevent complications. Wireless capsule endoscopy (WCE) is an effective tool for diagnosing GI abnormalities; however, precisely identifying diverse lesions with similar visual patterns remains difficult. Methods: Many existing computer-aided diagnostic (CAD) systems rely on manually crafted features or single deep learning (DL) models, which often fail to capture the complex and varied characteristics of GI diseases. In this study, we proposed “EndoNet,” a multi-stage hybrid DL framework for eight-class GI disease classification using WCE images. Features were extracted from two different layers of three pre-trained convolutional neural networks (CNNs) (Inception, Xception, ResNet101), with both inter-layer and inter-model feature fusion performed. Dimensionality reduction was achieved using Non-Negative Matrix Factorization (NNMF), followed by selection of the most informative features via the Minimum Redundancy Maximum Relevance (mRMR) method. Results: Two datasets were used to evaluate the performance of EndoNer, including Kvasir v2 and HyperKvasir. Classification using seven different Machine Learning algorithms achieved a maximum accuracy of 97.8% and 98.4% for Kvasir v2 and HyperKvasir datasets, respectively. Conclusions: By integrating transfer learning with feature engineering, dimensionality reduction, and feature selection, EndoNet provides high accuracy, flexibility, and interpretability. This framework offers a powerful and generalizable artificial intelligence solution suitable for clinical decision support systems. Full article

Mental stress is a psychological or emotional strain that typically occurs because of threatening, challenging, and overwhelming conditions and affects human behavior. Various factors, such as professional, environmental, and personal pressures, often trigger it. In recent years, various deep learning (DL)-based schemes using electroencephalograms (EEGs) have been proposed. However, the effectiveness of DL-based schemes is challenging because of the intricate DL structure, class imbalance problems, poor feature representation, low-frequency resolution problems, and complexity of multi-channel signal processing. This paper presents a novel hybrid DL framework, BDDNet, which combines a deep convolutional neural network (DCNN), bidirectional long short-term memory (BiLSTM), and deep belief network (DBN). BDDNet provides superior spectral–temporal feature depiction and better long-term dependency on the local and global features of EEGs. BDDNet accepts multiple EEG features (MEFs) that provide the spectral and time-domain features of EEGs. A novel improved crow search algorithm (ICSA) was presented for channel selection to minimize the computational complexity of multichannel stress detection. Further, the novel employee optimization algorithm (EOA) is utilized for the hyper-parameter optimization of hybrid BDDNet to enhance the training performance. The outcomes of the novel BDDNet were assessed using a public DEAP dataset. The BDDNet-ICSA offers improved recall of 97.6%, precision of 97.6%, F1-score of 97.6%, selectivity of 96.9%, negative predictive value NPV of 96.9%, and accuracy of 97.3% to traditional techniques. Full article