Search Results (3,614)

In contemporary 5G network environments, intrusion detection systems must balance detection accuracy with operational efficiency, as improvements in one dimension are often achieved at the expense of the other. This study addresses this trade-off by proposing a lightweight two-stage intrusion detection architecture that augments a standard decision-tree classifier with a conditional counter-inspection mechanism. At inference time, a global decision tree produces an initial classification for each traffic record, which is selectively validated by a small set of class-biased expert trees trained under controlled minority exposure. Only experts associated with the opposite class of the initial prediction are activated, and decision revision is governed by a unanimous-dissent rule, ensuring conservative and deterministic correction while avoiding over-correction. Experiments conducted on the 5G-NIDD dataset in a binary benign/malicious setting show that the proposed architecture consistently improves upon the standalone decision tree, reducing false negatives from 51 to 27 (−47.1%) and false positives from 48 to 30 (−37.5%), and achieving an F1-score of 0.99981 on a held-out test set. Ablation and paired statistical tests confirm that these gains arise from selective validation and the unanimous-dissent mechanism rather than from uniform ensembling. The complete pipeline operates in the microsecond inference regime per record, evaluates fewer models on average than flat voting strategies, and preserves full interpretability through deterministic decision paths, making it suitable for practical and resource-constrained 5G intrusion detection deployments. Full article

Accurate and early detection of bone cancer is critical for improving patient outcomes, yet conventional radiographic interpretation remains limited by subjectivity and variability. Conventional AI models often struggle with complex multi-modal noise distributions, non-convex and topologically entangled latent manifolds, extreme class imbalance in rare oncological conditions, and heterogeneous data fusion constraints. To address these challenges, we present a Quantum-Inspired Classical Convolutional Neural Network (QC-CNN) inspired by quantum analogies for automated bone cancer detection in radiographic images. The proposed architecture integrates classical convolutional layers for hierarchical feature extraction with a classical variational layer motivated by high-dimensional Hilbert space analogies for enhanced pattern discrimination. A curated and annotated dataset of bone X-ray images was utilized, partitioned into training, validation, and independent test cohorts. The QC-CNN was optimized using stochastic gradient descent (SGD) with adaptive learning rate scheduling, and regularization strategies were applied to mitigate overfitting. Quantitative evaluation demonstrated superior diagnostic performance, achieving high accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results highlight the ability of classical CNN with quantum-inspired design to capture non-linear correlations and subtle radiographic biomarkers that classical CNNs may overlook. This study establishes QC-CNN as a promising framework for quantum-analogy motivated medical image analysis, providing evidence of its utility in oncology and underscoring its potential for translation into clinical decision-support systems for early bone cancer diagnosis. All computations in the present study are performed using classical algorithms, with quantum-inspired concepts serving as a conceptual framework for model design and motivating future extensions. Full article

Rain, snow, fog, and dust can degrade road-scene images, blur fine details, and consequently reduce the reliability of perception systems for autonomous driving. To address this problem, this paper proposes PF-ConvNeXt, an adverse weather recognition model built upon the ConvNeXt architecture. First, a lightweight pyramid split attention (PSA) module is introduced to enable multi-scale feature fusion, so that both global degradation patterns and local texture details can be captured simultaneously. Second, a feature enhancement channel and spatial attention module (FECS) is designed. It adaptively recalibrates features along the channel and spatial paths, thereby suppressing interference from complex backgrounds and noise. Third, during training, Focal Loss is adopted to strengthen learning for hard samples and minority weather categories, alleviating recognition bias caused by class imbalance. Experiments are conducted on a dataset of 5000 images constructed by integrating RTTS, DAWN, and a self-collected rainy-weather dataset. The results show that PF-ConvNeXt achieves 90.16% accuracy, 95.24% mean average precision, and a 92.18% F1-score. It outperforms the ConvNeXt baseline by 4.74%, 5.46%, and 5.95%, respectively, and surpasses multiple mainstream classification models. This study provides an effective recognition framework for robust environmental perception under challenging weather conditions and demonstrates promising potential for practical deployment. Full article

Because of the substantial class disparity and the intricate interactions between academic, behavioral, and socioeconomic characteristics, anticipating student academic performance and dropout rates continues to be a major issue for institutions of higher learning. To improve the dependability and credibility of multiclass student outcome prediction, this study suggests a strong, multi-objective, and uncertainty-aware predictive framework that combines the Random Forest (RF) classifier with Holistic Swarm Optimization (HSO). The suggested method creates a multi-objective optimization problem that simultaneously maximizes macro F1-score, controls model complexity, and lessens inter-class performance disparity. Thereby, the model promotes fairness across student outcome categories, in contrast to traditional optimization strategies that only concentrate on predictive accuracy. Furthermore, by utilizing ensemble-based probability dispersion, the framework integrates uncertainty-aware prediction, making it possible to identify high-risk students with different degrees of confidence to assist practical academic interventions. According to the results of experiments, the suggested HSO-RF framework greatly reduces the performance gap between outcome classes while achieving the best overall predictive performance, reaching an accuracy of 77.74%, a macro F1-score of 0.69, and a weighted F1-score of 0.76. The analysis shows that academic, socioeconomic, and administrative characteristics serve as significant markers of student motivation, stability, and vulnerability in addition to computational benefits. The suggested architecture advances appropriate and trustworthy educational data mining and offers a dependable decision-support tool for early warning systems. Full article

Background: The Callista Roy Adaptation Model posits that adaptation in later life emerges from the interaction among physical, psychological, and social dimensions. However, empirical evidence integrating these domains through predictive approaches remains limited. The aim of this study was to identify the main predictors of adaptive classification in older adult women using functional and subjective well-being measures. Methods: A predictive study was conducted in older adult women enrolled in community-based exercise programs. Assessments included the Senior Fitness Test and the SF-12 and WHO-5 questionnaires. Multiclass classification models were trained, with Random Forest selected due to superior performance. Model evaluation incorporated oversampling strategies and robustness analyses without oversampling, using metrics resilient to class imbalance (macro-F1 and balanced accuracy). Model interpretability was examined through variable importance analysis, partial dependence, and ICE plots. Results: Under the oversampling framework, the Random Forest model achieved an overall accuracy of 74% and a macro-F1 score of 0.73, with reduced performance observed in robustness analyses, particularly for the minority “High” class. The most influential predictors were the physical component of the SF-12, the 2 min step test, the mental component of the SF-12, and the chair sit-and-reach test. Conclusions: The findings highlight the joint contribution of physical and psychosocial factors to adaptive processes, in alignment with the Roy Adaptation Model. This study provides exploratory evidence supporting the integrated use of the SFT, SF-12, and WHO-5; however, external validation and longitudinal evaluation are required prior to clinical implementation. Full article

Background: Accurately estimating resting energy expenditure (REE) in critically ill obese patients remains a significant clinical challenge, as predictive equations are consistently inadequate. Metabolic heterogeneity across obesity classes and the role of substrate utilization are insufficiently characterized. Objective: To evaluate the impact of different weight-normalization methods on the interpretation of REE and to identify independent metabolic determinants of weight-adjusted energy expenditure in critically ill patients with obesity. Methods: Bicentric cross-sectional study of 148 critically ill adults with obesity undergoing indirect calorimetry. REE normalized by actual body weight (REE/kg), ideal body weight (REE/IBW), and adjusted body weight (REE/AdjBW) was calculated. Multivariable models with robust standard errors (HC3), stratified analyses by obesity class (I–III) with a Chow test, and internal validation were performed using 10-fold cross-validation and bootstrap resampling (1000 iterations). Results: Absolute REE did not differ significantly between BMI categories (p = 0.679), while REE/kg progressively decreased from normal weight (27.8 kcal/kg/day) to class III obesity (16.9 kcal/kg/day; p < 0.001). The respiratory quotient (RQ) emerged as the most robust independent correlate of adjusted REE (β = −13 to −15 kcal·kg⁻¹·day⁻¹; p < 0.001), whereas clinical severity scores (SOFA, APACHE II) and comorbidity (Charlson) did not show significant associations. Stratified analyses revealed significant structural heterogeneity between obesity classes (F = 4.545, p = 0.0001), with no significant predictors identified in class III obesity, likely reflecting limited statistical power in this subgroup. Conclusions: Normalizing REE using different weight indices fundamentally alters its metabolic interpretation. RQ surpasses traditional clinical scores as a correlate of adjusted REE, consistent with a phenotype of metabolic inflexibility. The heterogeneity between obesity classes underscores the need for individualized indirect calorimetry rather than reliance on predictive equations. Full article

Water utilities continue to lose a lot of Non-Revenue Water (NRW) because of leaks that go undetected. This makes it necessary to find accurate but easy-to-use monitoring solutions. This paper presents FiT-WST+, a wavelet-guided Frequency-Informed Transformer (FiT) designed for the classification of five distinct leak types utilising accelerometer measurements. The proposed architecture combines the spectral modelling ability of a FIT with the stable translation-invariant representation of the Wavelet Scattering Transform (WST). The model uses a guided attention mechanism to combine spectral and scattering cues that work well together to make classes more distinct, especially for fault types that are similar. On the held-out test set, FiT-WST+ achieves 99.6% accuracy, 99.6% balanced accuracy, and a 99.6% macro-averaged F1-score. Comparative benchmarking against recent methods tested on the same dataset shows that this method works at a low sampling rate (1 kHz), which greatly lowers bandwidth needs and allows for scalable deployment on edge devices with limited resources for real-time monitoring of important water infrastructure. Full article

With the rapid growth of large-scale networks, traditional rule-based and supervised anomaly detection methods struggle with heavy reliance on labeled data, slow response to rapidly changing patterns, and difficulty in capturing complex temporal anomalies. At the same time, real-world traffic exhibits strong class imbalance, where normal samples overwhelmingly dominate, causing many existing models to miss subtle but critical abnormal behaviors. To address these challenges, this paper proposes an unsupervised temporal anomaly detection framework for network traffic based on a Transformer-autoencoder bidirectional prediction and reconstruction model. The framework combines the advantages of autoencoders and regression models, using multi-head self-attention and positional encoding to capture long-range temporal dependencies in traffic sequences. A masked decoding mechanism is further employed to prevent information leakage from future time steps. The model jointly generates forward and backward predictions as well as reconstructed sequences, and designs multiple anomaly scoring strategies that integrate prediction and reconstruction errors to enhance the sensitivity to point, contextual, and collective anomalies under highly imbalanced data. Experiments on three public benchmark datasets demonstrate that the proposed method significantly improves detection performance, achieving up to an F1 score of 0.960 and a precision of 0.949, with recall approaching 1.0, while reducing false alarms, thereby showing strong applicability to practical network security scenarios. Full article

Regional epidemic risk prediction requires holistic modeling of heterogeneous data sources such as demographic structure, health capacity, geographical features and human mobility. In this study, a unique and multi-modal epidemiological data set integrating demographic, health, geographic and mobility indicators of Türkiye and its neighboring countries was collected. Türkiye’s neighboring countries are Greece, Bulgaria, Georgia, Armenia, Iran, and Iraq. This dataset, created by combining raw data from these neighboring countries, provides a comprehensive regional representation that allows for both quantitative classification and spatial mapping of epidemiological risk. To address the class imbalance problem, Conditional GAN (CGAN), a class-conditional synthetic example generation approach that enhances high-risk category representation was used. In this study, we proposed a multi-view deep learning model named MV-RiskNet, which effectively models the multi-dimensional data structure by processing each view into independent subnetworks and integrating the representations with an attention-based fusion mechanism for regional epidemic risk prediction. Experimental studies were compared using Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Autoencoder classifier, and Graph Convolutional Network (GCN) models. The proposed MV-RiskNet with CGAN model achieved better results compared to other models, with 97.22% accuracy and 97.40% F1-score. The generated risk maps reveal regional clustering patterns in a spatially consistent manner, while attention analyses show that demographic and geographic features are the dominant determinants, while mobility plays a complementary role, especially in high-risk regions. Full article

The proliferation of unsolicited short messages (SMS spam) poses persistent challenges to mobile communication security and user privacy. This study presents a systematic benchmarking and analytical investigation of classical machine learning approaches for SMS spam detection, focusing on the impact of text feature representation under imbalanced short-text conditions.In practical SMS filtering systems, minimizing false positives (i.e., incorrectly blocking legitimate messages) is a critical operational constraint. Therefore, beyond overall accuracy, precision and specificity are emphasized to ensure reliable preservation of legitimate communication. Using the SMSSpamCollection dataset (5574 messages: 747 spam and 4827 ham), seven feature representation techniques were evaluated in combination with six widely adopted classifiers, resulting in 42 configurations assessed under 10-fold cross-validation. The results demonstrate that feature representation plays a more critical role than classifier complexity. Character-level 3-grams combined with Logistic Regression achieved the best overall performance, reaching 98.55% accuracy, with 98.55% precision and 90.50% recall for the spam class (F1-score = 94.32%), and 0.9893 AUC. Linear SVM produced comparable results, highlighting the effectiveness of linear models when paired with expressive representations. Beyond reporting performance metrics, this study analyzes feature–classifier interaction patterns and clarifies practical trade-offs between precision, recall, and computational efficiency. The findings provide reproducible baselines and structured guidance for designing efficient SMS spam filtering systems. Full article

Tracking structural behavior is critically important to reduce maintenance and repair costs. Structural Health Monitoring (SHM) aims to evaluate the structural integrity, detect damage or abnormalities, and estimate overall safety. The integration of Machine Learning techniques has significantly advanced SHM by enabling the identification of deterioration patterns through sensor data analysis. This study focuses on classifying different vibration patterns recorded under various excitation scenarios (ambient, transient, and forced) using sensors installed directly on a 3-DoF structure. The proposed approach used a two-dimensional convolutional neural network (2D-CNN) trained on vibration image patterns generated from vibration signal scalogram images. To address dataset imbalance, stratified 5 × 3 Nested cross-validation and multiple performance metrics were computed to ensure robust evaluation. The proposed method was compared with single-sensor scalogram approaches and baseline models, including Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), One-Dimensional Convolutional Neural Network (1D-CNN), and Long Short-Term Memory (LSTM) models, incorporating class-weighting strategies. Additionally, the contribution of the Total Energy Delivered by Sensor (TES) feature was evaluated for SVM, RF, and XGBoost models. The 2D-CNN model achieved superior performance in identifying excitation types associated with structural dynamic behavior, highlighting its effectiveness for structural vibration pattern recognition in SHM applications. Full article

The rise of toxic content, particularly in the form of hateful memes, poses a significant challenge to social media platforms. This paper presents an empirical comparative study of unimodal and multimodal architectures for toxic content detection. Rather than proposing a novel architecture, the study evaluates the efficacy of a modular Late Fusion framework (RoBERViT) against specialized unimodal baselines (RoBERTa and ViT) and a generalist Large Multimodal (LLaVA). Both unimodal and multimodal configurations across two distinct benchmarks—the imbalanced Innopolis Hateful Memes dataset and the confounder-driven Facebook Hateful Meme dataset—were explored. Beyond quantitative metrics, this study conducts a qualitative analysis using Explainable AI (LIME) and a Large Multimodal Model (LLaVA) to investigate model reasoning. Results demonstrate that the multimodal fusion model consistently outperformed its unimodal counterparts on the Innopolis Hateful Meme dataset, achieving a toxic class F1-score of 0.6439 compared to the text-only score of 0.5794. However, on the Facebook Hateful Meme dataset, text-only models remain competitive, highlighting the “benign confounder” challenge. The qualitative analysis reveals that text remains the dominant modality, with models often relying on surface-level keywords. Notably, the Vision Transformer frequently uses text overlays as a visual proxy for hate, while the LLaVA model struggles with hallucinated toxicity in benign confounder contexts. These findings underscore the persistent challenge of achieving true multimodal understanding in hate speech detection. Full article

In modern organizations, IT Service Management (ITSM) relies on the efficient handling of large volumes of unstructured textual data, such as support tickets and incident reports. This study investigates the automated classification of IT support requests as a data-driven decision-support task within a real-world enterprise ITSM context, addressing challenges posed by multilingual content and severe class imbalance. We propose an applied machine-learning and natural language processing (NLP) pipeline combining text cleaning, stratified data splitting, and supervised model training under realistic evaluation conditions. Multiple classification models were evaluated on historical enterprise ticket data, including a Logistic Regression baseline and transformer-based architectures (multilingual BERT and XLM-RoBERTa). Model validation distinguishes between deployment-oriented evaluation on naturally imbalanced data and diagnostic analysis using training-time class balancing to examine minority-class behavior. Results indicate that Logistic Regression performs reliably for high-frequency, well-defined request categories, while transformer-based models achieve consistently higher macro-averaged F1-scores and improved recognition of semantically complex and underrepresented classes. Training-time oversampling increases sensitivity to minority request types without improving overall accuracy on unbalanced test data, highlighting the importance of metric selection in ITSM evaluation. The findings provide an applied empirical comparison of established text-classification models in ITSM, incorporating both predictive performance and computational efficiency considerations, and offer practical guidance for supporting IT support agents during ticket triage and automated request classification. Full article

Silphium perfoliatum is a promising economic plant rich in bioactive secondary metabolites, yet the molecular regulation of phenylpropanoid biosynthesis across development remains unclear. To elucidate the regulatory networks underlying these metabolic processes, we integrated metabolomic and transcriptomic analyses across six developmental stages, from cotyledon to flowering. LC–MS/MS identified 1964 metabolites, with phenylpropanoids representing the largest class (601 compounds). Differential accumulation analysis showed pronounced temporal dynamics in phenylpropanoid levels, especially chlorogenic acid and its derivatives, with many compounds peaking at the flowering stage. In parallel, RNA-seq revealed 31,624 differentially expressed genes (DEGs). Functional enrichment highlighted phenylpropanoid and flavonoid biosynthetic pathways as major metabolic hubs. Correlation analysis indicated that PAL, 4CL, HCT, F3H, FLS, and F3′H expression was tightly coordinated with the accumulation of phenolic acids and flavonoids, suggesting these gene encoded enzymes may represent rate-limiting steps. Furthermore, weighted gene co-expression network analysis (WGCNA) identified a “blue” module strongly associated with phenylpropanoid accumulation and significantly enriched in pathway-related genes. Together, these results provide a comprehensive regulatory framework for phenylpropanoid biosynthesis in S. perfoliatum and offer valuable genetic targets for metabolic engineering and molecular breeding to enhance bioactive compound production. Full article

Wireless sensor networks (WSNs) have become a critical component of modern Internet of Things (IoT) infrastructures; however, their constrained resources and distributed deployment expose them to various cyber threats. In this work, we present a machine learning-driven intrusion detection framework optimized for WSN-based IoT environments. The proposed approach employs the WSN-DS benchmark dataset and integrates adaptive synthetic sampling (ADASYN) to address class imbalance, followed by a hybrid feature selection strategy combining Feature Importance Selection (FIS) and Recursive Feature Elimination (RFE) to reduce dimensionality and improve learning efficiency. An XGBoost classifier is then trained using five-fold cross-validation to ensure robust generalization. The experimental results demonstrate that the proposed framework significantly outperforms baseline methods, achieving an overall accuracy of 99.87%, with substantial gains in terms of F1-score, precision, and recall. Comparative analysis against recent WSN-DS studies confirms the effectiveness of combining imbalance correction, optimized feature selection, and ensemble learning. These findings highlight the potential of the proposed model as a lightweight and highly accurate intrusion detection solution for emerging WSN-IoT deployments. Full article