Search Results (447)

Class imbalance in datasets is a common issue across various fields, including banking, medicine, and information security. Data augmentation is a frequently used approach to address this problem by generating additional samples of the minority class to rebalance the dataset. Other studies have employed methods such as Generative Adversarial Networks (GAN) and Synthetic Minority Over-sampling Technique (SMOTE)for this purpose. Therefore, this study aims to compare the differences between the two oversampling techniques, GAN and SMOTE, in handling class imbalance problems. The results of this study show the accuracy of distinguishing between real and generated data to determine which method offers a greater advantage. The method demonstrates better performance in multi-class classification tasks. The GAN model can be effectively applied to both binary classification and the generation of diverse samples from minority and majority classes, even in extreme cases where the number of minority samples is tiny. Moreover, in terms of classification accuracy and the quality of generated samples, GAN outperforms SMOTE in data augmentation and oversampling. It maintains strong performance even when the number of instances in the minority class is limited. Full article

Accurate weather-regime classification is increasingly important for climate-sensitive decision-making in agriculture, aviation, disaster preparedness, and territorial planning, particularly in regions where strong climatic heterogeneity complicates conventional operational workflows. This study proposes a machine learning-based framework for broad-regime weather classification over Morocco using hourly METAR observations collected from 22 meteorological stations between July 2022 and February 2024. The proposed workflow integrates data cleaning, missing-value imputation, feature transformation, categorical encoding, class-imbalance handling, and model optimization under a leakage-safe experimental protocol. To preserve temporal integrity, observations were chronologically split into training, validation, and independent test subsets; SMOTE and random undersampling were applied exclusively to the training subset, whereas the validation and test subsets retained their original class distributions. Seven classifiers were evaluated, including XGBoost, LightGBM, CatBoost, Random Forest, Gradient Boosting, Support Vector Machine, and Logistic Regression, with hyperparameters optimized using Optuna. The results show that optimized boosting models are particularly effective for Moroccan station-based weather classification. XGBoost achieved the highest test-set accuracy of 95.1%, followed by LightGBM at 94.7% and CatBoost at 93.8%, with optimization improving accuracy by approximately 8–12 percentage points compared with baseline configurations. Because the dataset exhibits class imbalance, macro-averaged precision, recall, and F1-score were emphasized alongside accuracy to provide a more reliable assessment across weather classes. Confusion-matrix analysis indicates improved recognition of underrepresented regimes, especially Dust/Sand events, while residual confusion between Fog/Haze and Rain/Storm reflects both physical overlap and the limits of a four-class METAR taxonomy. Overall, the findings demonstrate that optimized ensemble learning can provide a robust, computationally efficient, and operationally relevant classification layer for regional meteorological decision support in Morocco, while future work should extend the framework to longer time series, finer weather taxonomies, and external regional validation. Full article

The deep integration of sci-tech and industrial innovation, rooted in the fusion of exploratory and exploitative ambidextrous capabilities, is a common global challenge. Traditional actors like enterprises and universities struggle due to the inherent imbalance of ambidextrous capability. Developed countries (e.g., Germany’s Fraunhofer, Finland’s VTT) have achieved integration through new-type research organizations, but rely on a “static coordination” model across departments ill-suited for rapidly changing, multi-logic environments. In contrast, China’s new-type R&D institutions (NTRI), emerging as innovative organizations, are naturally equipped to handle such institutional complexity and have become key drivers of deep integration. This study takes NTRI as a longitudinal single-case study object. Based on ambidextrous innovation theory and resource action theory, it constructs an analytical framework of “identifying integration challenges—addressing integration challenges—achieving integrated innovation” to explore how NTRI build dynamic ambidextrous capability through resource actions to drive the internal mechanism of integrating sci-tech innovation and industrial innovation. The results show that: (1) Accurately identifying integration breakpoints, bottlenecks, and hurdles at different development phases and establishing integration goals are key prerequisites for achieving integrated innovation; (2) the process of achieving integrated innovation is essentially a dynamic reconstruction of ambidextrous capability, involving resource bricolage to reconfigure demand-driven ambidextrous linking capability, utilizing resource orchestration to fission context-synchronized ambidextrous integration capability, and executing resource concerto to leapfrog networked symbiotic ambidextrous empowerment capability; and (3) the integrated innovation of NTRI at different phases exhibits a dynamic evolution, evolving from unidirectional spillover-integrated innovation to bidirectional interactive integrated innovation, and ultimately to empowering symbiotic integrated innovation. Full article

The increasing convergence of Operational Technology (OT) and Information Technology (IT) within the Industrial Internet of Things (IIoT) brings about remarkable improvements in monitoring and automation. However, it also exposes industrial systems to large-scale Distributed Denial of Service (DDoS) attacks. Edge-based defences are essential in satisfying low-latency demands and data sovereignty rules, yet they must function under severe resource limitations and adapt to shifting traffic characteristics without cloud assistance. In this work, we introduce a lightweight hybrid deep learning architecture that fuses a Convolutional Neural Network (CNN) with a Convolutional Block Attention Module (CBAM) and a Multi-Layer Perceptron (MLP) in a single detector. A sequential transfer learning scheme is adopted, including a feature projection layer that handles differences in input dimensionality. The model is pre-trained on the CIC-DDoS2019 dataset, then adapted to the more recent CICIoT23 dataset. Evaluations are performed on both datasets while preserving their natural class imbalance. We provide extensive ablation and variance analysis under identical experimental conditions. The proposed method achieves 99.52% accuracy on CICIoT23 while maintaining 99.65% recall, which is a crucial property for critical systems. Real-time measurements on a CPU-only testbed show an average inference latency of 0.013 ms, inference-only throughput exceeding 93,000 packets/s, and end-to-end batch throughput of approximately 38,000 packets/s. The solution demonstrates effective domain adaptation, sub-millisecond latency, and suitability for resource-constrained IIoT edge gateways. Full article

To address the challenges of quantifying expert experience, handling missing data, and managing class imbalance in evaluating the autonomous controllability of power equipment, this paper proposes a quantitative evaluation method that integrates expert prior rules with machine learning. First, building upon a five-dimensional evaluation indicator system, expert decision logic—including dimension-average threshold judgments, multi-dimensional weakness-based cumulative downgrading mechanisms, and key sub-item interaction rules—is formalized into a 15-dimensional rule prior feature vector, which is concatenated with the original 21-dimensional raw indicators to construct a RAW + RULE augmented feature space. Second, a KNN algorithm is employed for missing value imputation, while cost-sensitive learning combined with the SMOTE is adopted in a dual-path parallel scheme to address class imbalance. Six machine learning models are evaluated and compared via 30 repeated stratified cross-validations on a real-world dataset of 97 high-voltage bushing suppliers. Experimental results show that, on complete datasets, the RAW + RULE configuration with the Random Forest model achieves a mean test accuracy of 0.936 and a Kappa of 0.938, substantially outperforming the pure raw-feature model (accuracy 0.769, Kappa 0.766). Under weighted random missingness ranging from 10% to 50%, the RAW + RULE configuration demonstrates superior robustness, with ensemble tree models maintaining mean accuracies of 0.614–0.636 even at a 50% missing rate. This study provides a practically deployable technical solution and methodological reference for the quantitative assessment of autonomous controllability levels and early security warning in the power equipment supply chain. Full article

Power system operators globally face an ongoing challenge of maintaining a balance between electricity supply and load demand. This is a task which has been made increasingly complex by variability inherent in both generation sources and consumer loads. The balancing act is resource intensive, costly, and is critical for preventing frequency deviations that could destabilize the entire network, which can lead to blackouts and equipment damage. The intermittent nature caused by unpredictable wind speeds adds more challenges by introducing rapid fluctuations that system operators may struggle to mitigate. Energy storage systems (ESSs) have shown potential in addressing these challenges by offering flexible buffering capabilities to smooth out imbalances and enhance frequency stability. In this research, the impact of fluctuating wind speeds on power system frequency stability was analyzed. Subsequently, a hybrid energy storage system that integrates batteries for sustained energy discharge and super capacitors for rapid high-power responses was added. This enabled the system to handle mismatches effectively. The results show a 66% reduction in frequency deviations during wind fluctuations compared to baseline scenarios without storage. This improvement facilitates improved integration of renewable energy sources by allowing higher penetration levels without compromising stability. Full article

Colorectal cancer (CRC) is a major global health burden characterized by progressive genetic and metabolic alterations, with iron metabolism being increasingly recognized as a key contributor to tumorigenesis. This review provides an integrated synthesis of current evidence on iron metabolism across the continuum of colorectal cancer development, from preneoplastic lesions to advanced disease. We analyzed data from epidemiological, experimental, and mechanistic studies addressing systemic and cellular iron homeostasis, including the hepcidin–ferroportin axis, as well as iron handling within tumor cells and the tumor microenvironment. Available data indicate that colorectal epithelial cells progressively develop an iron-retentive phenotype, characterized by increased iron uptake and reduced export, leading to expansion of the intracellular labile iron pool. This imbalance contributes to oxidative stress, DNA damage, metabolic adaptation, and activation of oncogenic signaling pathways while also influencing immune responses. However, epidemiological findings on dietary iron and CRC risk remain inconsistent, highlighting the context-dependent nature of iron-related effects. In conclusion, iron metabolism represents a dynamic regulator of CRC progression and a mechanistic framework for understanding stage-specific tumor evolution, although further studies are needed to clarify how iron-dependent pathways differ across colorectal tumor subtypes and microenvironmental contexts. Full article

Mobile edge computing (MEC) enables computation-intensive and latency-sensitive tasks to be offloaded from mobile devices to nearby edge servers. Most existing MEC task offloading studies formulate offloading as a selection problem over a fixed or fully available set of candidate servers, which is restrictive in heterogeneous MEC scenarios with task-node eligibility constraints. Under such constraints, a task can be processed by an edge server only when task attributes, service requirements, link conditions, and node states jointly satisfy the corresponding eligibility conditions. The feasible action set therefore varies over time, while offloading decisions are further coupled with edge-node-side queue competition and long-term load evolution. To address this problem, this paper proposes Resource-oriented Scheduling Coordination (RoSCo), a load-aware task offloading method with scheduling-level constraint handling for eligibility-constrained MEC systems. In this paper, scheduling coordination refers to the joint use of feasible-action control, priority-aware edge-node service-order modeling, and load-responsive feedback within the task offloading decision process; it does not denote inter-server communication, task aggregation, federated model aggregation, or a distributed coordination protocol. RoSCo constructs a dynamic feasible action set, applies eligibility-aware action masking to exclude infeasible offloading actions, incorporates priority-aware edge-node service-order information to characterize queueing competition among heterogeneous tasks, and designs a load-responsive reward to guide congestion mitigation and load balancing. A dueling double deep Q-network (D3QN) is adopted as the value-learning backbone, while the main methodological contribution lies in embedding task-specific feasible-action control, priority-aware node-side queue information, and load-responsive feedback into the constrained offloading process. Simulation results show that RoSCo reduces the task drop rate and edge-node load imbalance while maintaining competitive task completion delay and energy consumption, especially under high-load and sparse-eligibility conditions. Full article

We study real-time next-window badminton action prediction from wearable IMU streams where the system must predict the action label of the upcoming 100 ms window using past-only (causal) information. To handle severe class imbalance in continuous streams, we employ window-level downsampling of the dominant background class and compress multi-sensor time/frequency features using PCA before temporal modeling. We evaluate the full pipeline under a hop-based streaming protocol and show that our BiLSTM + MHSA model achieves high recognition performance (test accuracy 96.36%, Macro-F1 95.82%) while remaining deployable in real time, reaching 58.20 windows/s end to end (including preprocessing), i.e., 5.82× the real-time requirement (10 windows/s under a 100 ms output interval), on a Windows PC with an NVIDIA RTX 3080 GPU. These results support low-latency applications such as live coaching feedback and tactical analytics. Full article

China has established the world’s largest municipal wastewater treatment system through rapid infrastructure expansion over the past two decades. However, under the transition from infrastructure expansion toward urban renewal and low-carbon development, wastewater systems are increasingly challenged by regional imbalances and structural inefficiencies. Existing studies have primarily focused on individual facilities or specific operational issues, while multidimensional system-level assessments remain limited. To address this gap, this study proposed a multidimensional assessment framework for evaluating wastewater system development in China from three dimensions: infrastructure adequacy, operational performance, and adaptive capacity. Based on national and provincial statistical data, regional disparities and development patterns were systematically analyzed using correlation analysis and hierarchical cluster analysis. Results showed that treatment capacity expansion in several provinces outpaced sewer network development, resulting in low hydraulic loading rates and underutilized facilities. Extraneous water infiltration remained a widespread issue, increasing unnecessary wastewater handling, energy consumption, and treatment burden. Reclaimed water development was influenced more strongly by policy-oriented planning and water resource constraints than by economic level alone. In addition, eastern coastal provinces generally demonstrated stronger infrastructure adequacy and operational performance, whereas several western and northeastern provinces remained constrained by insufficient adaptive capacity and sewer coordination. Overall, China’s wastewater sector is transitioning from treatment-oriented expansion toward system-oriented renewal. Future strategies should prioritize sewer rehabilitation, hydraulic efficiency improvement, reclaimed water integration, and adaptive infrastructure planning. The proposed framework can support future infrastructure monitoring, regional policy evaluation, and low-carbon wastewater system transformation. Full article

The recent seismic activity in southeastern Turkey in February 2023 again emphasized the critical need to promptly evaluate structural damage to assist in emergency response operations. This study introduces a comprehensive ensemble deep learning approach to structural damage classification following earthquake events, based on a dataset containing 13,270 high-resolution images with 15 different damage classes. Six different state-of-the-art convolutional neural network models (VGG16, ResNet50, InceptionV3, DenseNet121, EfficientNetB0, and MobileNetV2) are combined using a weighted voting approach to handle extreme class imbalance using weighted categorical cross-entropy loss. An integrated explainability component is incorporated into the trained convolutional neural network models to highlight the image regions that contribute to the predicted damage class, thereby improving the interpretability of deep learning decisions in safety-critical post-disaster assessment scenarios. The performance evaluation results show that the ensemble model achieves a test accuracy of 93.77%, with an increase of 2.67% compared to the best performing model individually. Notably, the ensemble model improves performance in minority classes like collapsed buildings. The proposed framework can be used to provide a powerful approach to structural damage evaluation, balancing accuracy with interpretability, to assist structural engineers in post-earthquake evaluation procedures. Full article

Phishing susceptibility reflects both stable psychological traits and transient user states, but confirmed victim cases remain rare in survey studies. This study evaluated BioHARP, a feasibility framework that pairs an outcome-independent psychometric prior with a prospective bio-adaptive fusion design. Using $N=136$ anonymized respondents (12 strict victims), we constructed 69 pre-incident predictors after excluding administrative metadata, exposure indicators, and post-incident response items. A cost-sensitive TabTransformer was trained without synthetic minority generation and benchmarked against six conventional tabular baselines and FT-Transformer under identical splits, unified preprocessing, and model-appropriate cost-sensitive imbalance handling. Out-of-sample performance was primarily assessed with a 60-seed repeated stratified hold-out protocol with fixed four-positive/thirty-negative test composition. Across the sixty splits, TabTransformer yielded a mean AUC of $0.534\pm 0.157$, whereas CatBoost yielded $0.736\pm 0.108$. On fixed Seed 100, TabTransformer reached AUC $=0.8167$ and CatBoost AUC $=0.775$; for the single-init TabTransformer, this was the best-observed split and was therefore interpreted as an optimistic upper-end point estimate. Threshold-dependent metrics were reported separately as an exploratory analysis with explicit leakage labeling. The physiological fusion layer was evaluated as an outcome-informed oracle upper bound, reaching AUC $=0.944$ on Seed 100 and $0.878\pm 0.058$, range [0.73, 0.98], across 70 alternative scenario RNG seeds. This result was interpreted strictly as theoretical headroom rather than deployment-calibrated performance. Overall, BioHARP was framed as a feasibility framework with a clearly bounded physiological-fusion design and explicit calibration and sensor requirements for future deployment-ready bio-adaptive detectors. Full article

In remote sensing data analysis, multi-class classification plays a critical role in distinguishing multiple pattern types, and decision trees are particularly well-suited for this task due to their computational efficiency and interpretability. Existing decision tree approaches often suffer from suboptimal handling of multi-class problems, vulnerability to class imbalance, or degraded generalization ability. To address these limitations, this paper proposes an adaptive multi-splitting multivariate decision tree designed explicitly for multi-class classification. The core of our approach is an effective homogeneity cluster discovery strategy that directly optimizes a multi-class sample separation criterion at each node, eliminating dependency on decomposition schemes and mitigating the associated class imbalance problem. This is coupled with an adaptive splitting mechanism that dynamically chooses between multi-splitting and bi-splitting at each node based on local data geometry and class labels. Experimental evaluations on a synthetic multi-class dataset, noisy remote sensing RGB scene image datasets demonstrate that the proposed model outperforms existing decision tree methods in classification accuracy and F1 score with compact tree structures and maintains competitive computational efficiency. In the remote sensing hyperspectral image classification application, the proposed model improves overall accuracy by up to 6.99% over the baseline deep learning model on the highly class-imbalanced Indian Pines dataset. This work provides a flexible and effective multivariate decision tree classifier, which can improve multi-class classification performance while keeping high efficiency. Full article

Intelligent housing-dimensional urban physical examination requires evaluating complex visual scenes in aging communities. Existing methods and datasets are insufficient for these heterogeneous tasks and severe class imbalances. To address this, we introduce the Housing-dimensiOnal visUal inSpection imagE Dataset (HOUSED) with a hierarchical labeling scheme, and propose a hierarchical Vision Mixture of Experts (VMoE) framework. At its core, the proposed CS-DisVMoE module utilizes a CS-Soft routing mechanism to capture spatial feature correlations, optimizing expert assignment and reducing inference overhead. Additionally, a FENNEL-based non-linear graph partitioning mechanism converts pre-trained dense weights into semantically coherent expert initializations, accelerating convergence while preserving localized visual clustering. To address the hierarchical labels, we design a composite loss function: a Supervised Contrastive Loss acts as a parent-category soft constraint to accelerate convergence, while Focal Loss mitigates data imbalance and handles fine-grained subcategory classification via hard sample mining. Across evaluated datasets, the full proposed framework improves accuracy by an average of 4.3% over the ViT-Tiny baseline and 1.81% over the best-performing VMoE baseline. Furthermore, it achieves these improvements with lower computational costs. Further tests on mixed public vision datasets verify its generalizability and competitive performance for complex-scene applications. Full article

The rapid expansion of Internet of Things (IoT) technologies has highlighted the need for reliable intrusion detection systems (IDSs), yet the majority of existing studies rely on single-dataset evaluations, raising concerns about their real-world generalisation capability. This study addresses this limitation by systematically investigating distributional shift across heterogeneous IoT intrusion detection datasets and their impact on model behaviour. To achieve this, a unified feature space is constructed using BoT-IoT, ToN-IoT, and UNSW-NB15 datasets, followed by a comprehensive preprocessing pipeline including attack class alignment, distribution-preserving sampling for class imbalance, and feature selection based on cross-dataset feature value propagation analysis. Furthermore, feature-specific transformations and correlation-based dimensionality reduction are applied to enhance statistical consistency and model stability. To simulate realistic deployment scenarios, models are trained on combinations of datasets and evaluated on unseen datasets. The results reveal that distributional inconsistencies and dataset-specific feature biases significantly degrade cross-dataset performance, despite strong within-dataset results. The proposed framework provides a systematic understanding of feature-level behaviour across datasets, identifying both stable and bias-prone features. These findings highlight the necessity of distribution-aware preprocessing and feature analysis for developing robust and generalisable IoT intrusion detection systems. Full article