Search Results (5,852)

Facial emotion recognition (FER) in real-world settings is limited by the semantic mismatch between discrete emotion categories and continuous Valence–Arousal–Dominance (V-A-D) dimensions and the lack of reliable uncertainty estimates under incomplete facial evidence. Existing uncertainty-aware FER studies mainly address annotation ambiguity or training-time reliability, leaving the behavior of predictive uncertainty under progressive input degradation insufficiently examined. This paper proposes BGDC (Bayesian Gaussian-mixture Distributional Consistency), a multi-task FER framework that integrates a GMM-based soft consistency module with a context-conditioned Bayesian regression head and explicitly models aleatoric and epistemic uncertainty. To evaluate predictive reliability, a controlled masking protocol is introduced to remove facial information under different spatial configurations. On FER2013-VAD, BGDC attains the highest classification accuracy of 0.6943 and the highest mean V-A-D CCC of 0.6079 among the compared configurations, and it yields a stronger epistemic uncertainty-error correspondence than MC Dropout in a single-model setting. Controlled masking further shows that the epistemic uncertainty of BGDC tracks task-relevant facial information loss rather than masking ratio alone: it rises with regression error when diagnostically important regions are removed, and it contracts when the masked region is largely task-irrelevant. Combining Bayesian uncertainty with the GMM-based distributional prior thus enables reliability-aware multi-task FER, in which controlled masking serves as a diagnostic intervention rather than as a benchmark of accuracy degradation alone. Full article

Using the Hierarchical Taxonomy of Psychopathology (HiTOP) as our framework, the current study examines how 13 common psychological disorders can be grouped into different spectra in two groups of adolescents: a community sample (N = 951), and a clinic-referred sample (N = 173). Scores for the disorders were obtained using the parent version of the Child and Adolescent PsychProfiler. Taken together, the findings across the two samples for factor structure, reliability, and discriminant and concurrent validity indicate the most support for a three-factor CFA oblique model with primary factors for neurodevelopment disorders (that include Specific Learning Disorder, Autism Spectrum Disorder, Language Disorder, and Speech Sound Disorder), internalizing disorder problems (that include Generalized Anxiety Disorder, Persistent Depressive Disorder, Separation Anxiety Disorder, Obsessive–Compulsive Disorder, Posttraumatic Stress Disorder, Anorexia Nervosa, and Bulimia Nervosa), and externalizing disorder problems [(that include Attention Deficit/Hyperactivity Disorder (ADHD), and Oppositional Defiant Disorder/Conduct Disorder (ODD/CD)], with a covariance for the error variance for Anorexia Nervosa and Bulimia Nervosa. Additionally, the analysis for Sample 2 supports the concurrent validity of the factors in this model. A modification of this model, with ADHD cross-loading on the neurodevelopment disorders factor, did not produce an admissible solution. The findings indicate support for a neurodevelopmental spectrum in the HiTOP model, with ADHD and ODD/CD showing stronger statistical association with the externalizing factor than with the neurodevelopmental factor in the models tested. This finding pertains to dimensional structure and does not invalidate the neurodevelopmental classification of ADHD in DSM-5-TR. Full article

Deep learning models for power quality (PQ) disturbance classification remain critically vulnerable to adversarial perturbations, with classification performance degrading severely under white-box attacks. Existing defenses address individual models in isolation and provide no mechanism for operators to assess whether the system is under attack or which classifier remains trustworthy. This paper proposes a two-stage framework that combines adversarial training with large language model (LLM) reasoning to improve both robustness and interpretability. In the first stage, four architecturally diverse classifiers, including a proposed Multi-Scale Temporal Attention Network (MSTAN), are evaluated under four adversarial attacks (FGSM, PGD, C&W, and UAP), and their failure patterns are recorded as structured vulnerability fingerprints. The ensemble is then retrained via adversarial training on mixed clean and perturbed signals. In the second stage, an LLM analyzes the ensemble predictions alongside the fingerprint knowledge base to perform attack detection, fingerprint-guided meta-classification, and operator-facing threat report generation. On a 17-class, 255,000-signal synthetic benchmark, adversarial training recovers FGSM and PGD accuracy from below 25% to the 53–78% range, with MSTAN achieving the highest post-training robustness (78.26% under FGSM, 65.41% under PGD). The LLM reasoning layer provides an additional 3.5–6.2 percentage point improvement over majority voting by selecting the most reliable ensemble member based on the inferred attack condition, and detects adversarial attacks with 87.6% overall accuracy. To our knowledge, this is the first integration of LLM-based ensemble reasoning into the PQ adversarial robustness pipeline and the first application of the C&W optimization attack to power quality signals. Full article

Microplastics (MPs) are widely recognized as emerging contaminants in freshwater environments. Their identification often relies on extensive sample preparation and chemical treatments, which increase analysis time, reagent use, and overall resource consumption. Consequently, there is a growing need for sustainable analytical approaches enabling reliable MP detection while minimizing sample handling. This study proposes an analytical workflow based on hyperspectral imaging (HSI) as a proof-of-concept approach for direct identification of MPs in freshwater samples. Water samples collected from three different rivers, containing heterogeneous natural materials, were spiked with MPs (250–1000 μm) of three common polymers, namely high-density polyethylene (HDPE), polystyrene (PS), and polypropylene (PP), to simulate realistic contamination scenarios. HSI acquisitions were performed in the short-wave infrared range (SWIR: 1000–2500 nm). Spectral preprocessing and principal component analysis (PCA) were applied for data exploration, while a hierarchical partial least squares-discriminant analysis (Hi-PLS-DA) model was developed to classify five target classes: natural materials, water, HDPE, PS, and PP. Despite sample complexity, the proposed workflow achieved satisfactory classification results, as demonstrated by the predicted class map and the corresponding statistical metrics (sensitivity, specificity, precision, and F1-score: 0.900–0.999). These results highlight the potential of the SWIR-HSI-based approach as a rapid and sustainable method for direct MP identification in freshwater samples and provide methodological insights for rapid MP screening strategies requiring minimal sample preparation. Full article

Background/Objectives: Oral cancer is one of the most common types of cancer, with high mortality rates if not detected early. Traditional diagnostic methods based on clinical examination rely on experience, leading to delays in early and reliable diagnosis. In recent years, medical imaging and AI-based computer-aided diagnostic systems have shown promising results in the automated identification of oral cancer. In particular, the efficient management of high-dimensional feature spaces in machine learning and deep learning approaches directly impacts classification performance. In this context, metaheuristic-based feature selection technics is a critical component because of eliminating redundant and irrelevant features. To address these challenges, this study proposes a metaheuristic-based feature selection method to reduce feature dimensionality and enhance the classification performance of oral cancer detection. Methods: This study proposes an improved Honey Badger Algorithm-based feature selection approach for the automated detection of oral cancer. In the proposed method, the distance vector used in the HBA method has been redefined to improve the balance between exploration and exploitation. Additionally, a new Cauchy mutation-based migration strategy was integrated into the proposed method to increase diversity in the search space and avoid getting stuck in local minima. The continuous-valued iHBA method was discretized with a modified sin–cos transfer function for feature selection. Oral cancer images were filtered using the CLAHE method, and after extracting deep features with the ResNet50 architecture, the proposed metaheuristic-based method was used to select discriminative features. Results: The proposed method was first tested for reliability and limitations through repeated runs on problems with different characteristics, such as unimodal and multimodal classical test functions. Then, the method was applied to extract significant features for oral cancer detection using a Histopathological Imaging Database containing 1224 histopathological oral tissue images at 100× and 400× magnification levels from 230 patients. The proposed approach was assessed in terms of accuracy, precision, recall, F1-score, and convergence curves in comparison with various classical feature selection techniques, such as wrapper-based, filter-based, and embedded-based methods, as well as other metaheuristic-based methods. The experimental results demonstrated that the suggested strategy outperformed both traditional feature selection techniques and alternative metaheuristic approaches. Conclusions: The effectiveness of the proposed method in improving diagnostic accuracy was evaluated through comprehensive experimental analyses. The obtained findings show that the proposed iHBA-based feature selection approach can reduce feature dimensionality, eliminate redundant and irrelevant features, and improve the classification performance of oral cancer detection. Therefore, the proposed method provides an effective and competitive computer-aided diagnostic framework for the automated classification of histopathological oral cancer images. Full article

Background: Dedicated artificial intelligence (AI) systems for fracture detection already exist, yet general-purpose multimodal models are increasingly accessible to clinicians despite not being developed or formally validated as medical devices. Their behavior in focused orthopedic imaging tasks remains insufficiently characterized. Purpose: To characterize how two accessible general-purpose multimodal models interpret AP pelvis radiographs with hip fractures, focusing on context dependence, overconfidence, and complementary error patterns within a surgically confirmed positive-only cohort. This was a behavioral characterization study of a fracture-positive cohort, not a diagnostic accuracy evaluation. Methods: In April 2026, we retrospectively studied 214 surgically confirmed hip fractures on AP pelvis radiographs using two general-purpose multimodal models under six prompting conditions. In runs A–D, the models were explicitly told that a hip fracture was present and were asked to classify it; in runs E–F, they were not told whether a hip fracture was present. Each image was rerun de novo in a separate chat session through vendor APIs using a fixed base prompt and no image preprocessing. We recorded hip-fracture detection, correct laterality, coarse fracture pattern, intracapsular displacement, AO/OTA grading, subtrochanteric identification, and self-reported confidence. Because the cohort contained hip fractures only, we report fracture-detection rates and classification performance within a positive-only cohort rather than full diagnostic-accuracy metrics. Results: Using the more conservative endpoint of hip-fracture detection with correct laterality, GPT-5.4 was correct in 79.0% and 86.4% of cases in runs E and F, whereas Gemini was correct in 80.4% and 93.5%, respectively. When outputs from both models were combined, this endpoint reached 89.7% in run E and 96.7% in run F, indicating complementary rather than redundant error patterns. Incorrect laterality cues markedly degraded performance, from 90.7% to 66.4% in GPT-5.4 and from 97.7% to 57.0% in Gemini. Performance remained limited for treatment-relevant subtyping, particularly AO/OTA grading and subtrochanteric identification. Both models frequently remained highly confident when wrong, and self-reported confidence did not reliably distinguish correct from incorrect outputs. Conclusions: Accessible general-purpose multimodal models showed partial capability for coarse hip-fracture interpretation, but they remained context-sensitive, unreliable for treatment-relevant subtyping, and highly confident even when incorrect. Their complementary error patterns are hypothesis-generating rather than evidence of clinical readiness. On the basis of these findings, we do not support unvalidated or uncontrolled clinical use of such models. As access to these tools expands, explicit usage boundaries, minimum performance expectations, repeated local revalidation, and sustained human oversight become increasingly necessary. Full article

High-dynamic amusement ride conditions involving impacts, rapid rotations, and abrupt posture changes introduce severe motion artifacts that degrade vital sign quality and destabilize physiological state recognition. This study aims to develop an engineering-ready closed-loop framework for robust passenger monitoring and intelligent diagnosis. A multimodal sensing and modeling pipeline was designed to jointly leverage physiological signals such as heart rate and SpO₂ and kinematic measurements, including acceleration, angular rate, velocity, and attitude. Inertial and PPG signals were preprocessed into supervised samples through wavelet multiresolution denoising and coordinate frame unification, while a strapdown inertial navigation system was used to propagate a 12-channel physical quantity sequence. To ensure interpretability and standards compliance, constraints from GB 8408-2018 were translated into executable threshold rules, enabling standards-driven auto-labeling and rule-based early warning. Building on this foundation, three learning modules were developed: a fusion model for high-dynamic heart rate estimation, a CNN–LSTM dynamic-threshold-enhanced network TAPNet for rapid kinematic anomaly screening, and an attention-augmented hybrid model HS-BANet integrating one-dimensional residual blocks, bidirectional LSTM, and multi-head attention for fine-grained arrhythmia classification. Experimental results demonstrated accurate and consistent heart rate estimation with RMSE of 1.18 bpm on HSSH-I and 1.24 bpm on the independent HSSH-II set, strong agreement with training and testing correlations of 0.9928 and 0.9865, and near-zero bias in Bland–Altman analysis. TAPNet achieved 96.9% validation accuracy and 98.2% test accuracy for kinematic anomaly recognition, maintaining robust generalization under class imbalance. HS-BANet enabled multi-class identification of PVC, PAC, VT, SVT, and AF, achieving an accuracy of 92.37%, an F1-score of 86.87%, a precision of 88.45%, a sensitivity of 88.14%, and a specificity of 89.42%. Overall, the proposed two-stage multimodal closed-loop—fast, interpretable early warning based on physical quantity thresholds followed by fine-grained diagnosis from physiological signals—supports stable feature extraction and reliable decision-making under strong motion artifacts and non-stationary dynamics, balancing responsiveness and diagnostic credibility, while showing potential for practical safety early warning and future deployment-oriented operational support in amusement ride scenarios. Full article

Background: Childhood oral hygiene behaviors are crucial to preventing oral diseases and can influence the development and progression of malocclusions. The World Health Organization (WHO) Oral Health Questionnaire is a standardized tool for assessing oral hygiene behaviors, oral health-related behaviors, and preventive dental awareness in children. Aim: This study aimed to assess oral hygiene behaviours and examine associations between WHO Oral Health Questionnaire variables and Angle malocclusion classes among children aged 6–9 years. Materials and Methods: This cross-sectional study included 90 children aged 6–9 years from the Pristina region, Kosovo. Data were collected using the WHO Oral Health Questionnaire for Children, which assessed oral hygiene habits, toothbrushing frequency, fluoride awareness, dental attendance, dietary behaviors, oral symptoms, and oral-health-related quality of life. Malocclusion was classified according to Angle classification into Class I, II, and III malocclusions with 3D intraoral scanners, Aerolscan 3. Descriptive statistical analysis, Chi-square (χ²) test, Spearman correlation analysis, and reliability analysis using Cronbach’s Alpha were performed using SPSS Statistics 23.0 (IBM Corp., Armonk, NY, USA) and Statistica 7.1 (StatSoft Inc., Tusla, OK, USA). Results: Most participants reported regular oral hygiene practices, with 46.7% brushing their teeth two or more times daily. However, limited awareness regarding fluoride-containing toothpaste was observed, as most children answered “don’t know” regarding fluoride use. Occasional toothache or oral discomfort was reported by 33.3% of participants, while 23.3% reported dissatisfaction with dental appearance. Difficulty biting hard foods was present in 34.4% of children. Reliability analysis of the Q10 section demonstrated moderate internal consistency (Cronbach’s Alpha = 0.500). Chi-square analysis demonstrated no statistically significant association between Angle malocclusion classes and WHO questionnaire variables (p > 0.05). The highest χ² value was observed for tooth-cleaning frequency (Q7) (χ² = 11.97; p = 0.152), although the association remained statistically non-significant. Psychosocial impact questions and oral health-related quality of life questions also demonstrated no statistically significant association with malocclusion classes. Conclusions: oral hygiene practices, preventative oral health practices, and oral health-related experiences were comparatively similar among children in different Angle malocclusion classes. Although there were no statistically significant correlations found between malocclusion classes and WHO questionnaire variables, the results show that some children have psychosocial concerns about their dental appearance and insufficient awareness of preventive oral health. The WHO Oral Health Questionnaire is a useful epidemiological tool for evaluating pediatric oral health behaviors and may help build youth orthodontic and preventive oral health policies. Full article

Optical performance monitoring is essential for adaptive and intelligent coherent optical communication systems. In this paper, a Transformer-based multi-task meta-learning framework is proposed for joint modulation format identification and electrical signal-to-noise ratio (ESNR) estimation from original received waveforms. A simulated coherent optical communication system is established to generate QPSK, 16QAM, and 32QAM signals under different launch-power conditions. The received I/Q waveforms are directly used as model inputs, avoiding handcrafted feature extraction or constellation-image conversion. The proposed model employs a shared one-dimensional Transformer encoder to extract temporal waveform representations. A prototypical classification branch is used for few-shot modulation format identification, while an ESNR regression branch is introduced for continuous signal-quality estimation. The two tasks are jointly optimized under an episodic support-query training mechanism. Experimental results show that the proposed method achieves 99.99% modulation identification accuracy on the test episodes. For ESNR estimation, the model obtains an MAE of 0.1194 dB, an RMSE of 0.1738 dB, and an R² value of 99.83%. These results demonstrate that the proposed framework can simultaneously provide accurate modulation decisions and reliable ESNR estimation, showing its potential for waveform-based optical performance monitoring. Full article

Background: Hydatid disease, caused by Echinococcus granulosus, remains a significant public health concern in endemic regions. This study aimed to evaluate the role of ultrasound in the diagnosis, staging, and clinical management of liver hydatid cysts in the eastern city of Jalalabad, Afghanistan. Method: A cross-sectional study was conducted between February and November 2024 among 159 patients diagnosed with liver hydatid cysts. Demographic, clinical, laboratory, and imaging data were collected. Cysts were classified according to the WHO Informal Working Group on Echinococcosis (WHO-IWGE) and Gharbi systems. Ultrasound findings were compared with computed tomography (CT), and their association with treatment decisions was assessed. Result: A total of 159 patients with liver hydatid cysts were included in the study. Among them, 91 (57.2%) were female, 80 (50.3%) were aged 20–39 years, and 128 (80.5%) resided in rural areas. Most patients presented with a single cyst (144/159, 90.6%), while multiple cysts were observed in 15 (9.4%). The majority of cysts measured 5–9.9 cm in diameter (43.4%), followed by 1–4.9 cm (42.1%) and ≥10 cm (14.5%). According to the WHO-IWGE classification, CE1 (25.8%) and CE4 (24.5%) were the most common stages, followed by CE2 (17.6%), CE3a (13.8%), CE3b (11.3%), and CE5 (7.0%). Common exposure-related factors included dog ownership, poor hygiene practices, and consumption of raw vegetables. Ultrasound accurately identified cyst stages and demonstrated a significant association between WHO-IWGE staging and treatment modality (χ² = 63.56, p < 0.001). Almost perfect agreement was observed between ultrasound and CT for cyst classification (Cohen’s κ > 0.90), although CT provided additional anatomical information in selected complex cases. Conclusions: Ultrasound is an accessible, accurate, and reliable imaging modality for the diagnosis, staging, and management of liver hydatid cysts. In resource-limited settings, it serves as the primary imaging modality for guiding clinical decision-making, with CT reserved for complex or uncertain cases. Full article

The present study investigates the application of conventional and ensemble machine learning models for slope stability prediction, which is essential for landslide risk reduction and sustainable infrastructure management. A database containing 627 slope cases was used, including six input variables: unit weight, cohesion, friction angle, slope angle, slope height, and pore pressure ratio. Six machine learning models, namely Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Classification and Regression Tree (CART), and Boosted Tree, were developed and evaluated. The models were assessed using ROC analysis, confusion-matrix-derived metrics, precision–recall analysis, feature importance assessment, and unseen testing cases. The results showed that ensemble-based models provided superior predictive performance compared with conventional machine learning models. Based on ROC analysis, RF achieved the highest ROC-AUC value of 0.93, followed by Boosted Tree and XGBoost with ROC-AUC values of 0.92 and 0.90, respectively. Based on confusion-matrix-derived metrics, Boosted Tree achieved the highest accuracy of 0.862 and F1-score of 0.874, while RF showed comparable performance with an accuracy of 0.857 and F1-score of 0.868. Feature importance analysis indicated that cohesion and unit weight were among the most influential variables affecting slope stability prediction. In addition, the unseen testing cases confirmed the practical generalization capability of the ensemble models, with Boosted Tree and RF achieving accuracies of 0.920 and 0.880, respectively. Overall, the findings demonstrate that ensemble learning models, particularly Boosted Tree and RF, can provide reliable and interpretable decision-support tools for preliminary slope stability assessment and landslide hazard management. Full article

Background: Accurate assessment of neonatal sleep is critical for monitoring brain development and identifying potential neurological disorders, yet manual scoring of multi-channel EEG recordings is labor-intensive and prone to variability. Methods: To address this, we propose a lightweight temporal–spatial feature fusion network for automatic neonatal sleep staging. The model employs a dual-branch architecture to separately capture temporal dependencies and spatial correlations in EEG signals, which are then integrated through feature concatenation and a compact classifier to obtain comprehensive feature representations while maintaining low computational complexity. Results: The framework was evaluated on a clinical neonatal dataset (CHFD) for tasks including sleep–wake classification, quiet sleep detection, and three-stage sleep staging, achieving superior performance compared with several state-of-the-art methods. Additional evaluation on the MASS-S3 adult dataset demonstrate that the model retains competitive accuracy and F1-score, indicating strong generalization across populations. Conclusions: These results suggest that jointly modeling temporal and spatial features enables robust and efficient automatic sleep staging. The proposed approach offers a practical solution for clinical applications and edge deployment, providing reliable, multi-dimensional assessment of neonatal brain activity and laying the groundwork for future studies integrating larger datasets or multimodal physiological signals. Full article

This paper summarizes an analysis of the Joint Clinical Assessment (JCA) subgroup’s recommendations for systematic literature reviews (SLRs). While the JCA offers clear guidance on study classification, exclusion criteria reporting, and PRISMA diagram use, several of its recommendations diverge from established best practices in evidence-based medicine (EBM). A comparison with recognized guidelines, such as those from Cochrane and EUnetHTA, reveals that the JCA guidance may lack reliability, comprehensiveness, and reproducibility. Aligning JCA recommendations with gold standards in SLR methodology would address these shortcomings and enhance methodological rigor. Full article

Early fault diagnosis of ball bearings is essential for maintaining the reliability of rotating machinery and preventing unexpected downtime. This study proposes a fault diagnosis framework that combines non-contact ultrasonic visualization with a lightweight convolutional neural network (Light CNN). Seven bearing conditions, including ferrous particle contamination and grease starvation, were investigated using ultrasonic, vibration, and acoustic emission (AE) sensors under identical experimental conditions. Sa-liency-map extraction and two-dimensional histogram analysis were applied to ultrasonic RGB images to generate compact feature representations, which were compressed into 20 × 20 feature maps and used as inputs to a three-layer Light CNN. The proposed method achieved an average classification accuracy of 99.98% and an F1-score of 99.98%. In addition, an average inference throughput of 11.47 IPS was obtained, representing approximately ten times higher computational efficiency than vibration- and AE-based approach-es. Stable diagnostic performance was also maintained under a low-speed operating condition of 500 rpm. These results demonstrate the effectiveness of combining ultrasonic visualization and a lightweight CNN for accurate and computationally efficient bearing fault diagnosis. Full article

Joint detection of strawberry growth stages and defective fruit is needed for harvest planning and quality screening, but field images make this task difficult because stage-related visual differences are subtle, flowers and early fruits are often small and densely distributed, and occlusion weakens localization reliability. This study develops Multi-Scale Refined Detection and Classification YOLO (MRDC-YOLO), a lightweight detector based on the YOLO11s framework, for this fine-grained detection scenario. The backbone, neck, and detection head are redesigned with three modules: a Multi-Scale Adaptive Edge Enhancement Module (MAEM), a Reparameterized Progressive Feature Aggregation (RPFA) module, and a Decoupled Cross-Scan Head (DCSH). MAEM strengthens boundary and texture responses for visually similar categories, RPFA reduces redundant multi-scale fusion while maintaining features for dense small targets, and DCSH introduces task-aware classification and regression branches with cross-scan-inspired spatial modeling for occlusion-sensitive localization. Experiments on a five-class strawberry dataset containing 5114 images show that MRDC-YOLO achieves 95.63% mAP@0.5 and 82.39% mAP@0.5:0.95. Over YOLO11s, the model yields a 2.06-percentage-point gain in precision and 1.34- and 1.53-percentage-point gains in mAP@0.5 and mAP@0.5:0.95, together with 10.7% fewer parameters and 8.9% lower GFLOPs. These results suggest that MRDC-YOLO improves fine-grained category discrimination and localization while retaining a smaller model size than the YOLO11s baseline. Full article