Search Results (726)

The rapid proliferation of Internet of Things (IoT) devices has significantly increased vulnerability to Distributed Denial of Service (DDoS) attacks, which can severely disrupt network operations. DDoS attacks in IoT networks disrupt communication and compromise service availability, causing severe operational and economic losses. In this paper, we present a Deep Learning (DL)-based Intrusion Detection System (IDS) tailored for IoT environments. Our system employs three architectures—Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Transformer-based models—to perform binary, three-class, and 12-class classification tasks on the CiC IoT 2023 dataset. Data preprocessing includes log normalization to stabilize feature distributions and SMOTE-based oversampling to mitigate class imbalance. Experiments on the CIC-IoT 2023 dataset show that, in the binary classification task, the DNN achieved 99.2% accuracy, the CNN 99.0%, and the Transformer 98.8%. In three-class classification (benign, DDoS, and non-DDoS), all models attained near-perfect performance (approximately 99.9–100%). In the 12-class scenario (benign plus 12 attack types), the DNN, CNN, and Transformer reached 93.0%, 92.7%, and 92.5% accuracy, respectively. The high precision, recall, and ROC-AUC values corroborate the efficacy and generalizability of our approach for IoT DDoS detection. Comparative analysis indicates that our proposed IDS outperforms state-of-the-art methods in terms of detection accuracy and efficiency. These results underscore the potential of integrating advanced DL models into IDS frameworks, thereby providing a scalable and effective solution to secure IoT networks against evolving DDoS threats. Future work will explore further enhancements, including the use of deeper Transformer architectures and cross-dataset validation, to ensure robustness in real-world deployments. Full article

Internal crack defects in high-strength low-alloy (HSLA) steels during continuous casting pose significant challenges to downstream processing and product reliability. However, due to the inherent class imbalance in industrial defect datasets, conventional machine learning models often suffer from poor sensitivity to minority class instances. This study proposes a predictive framework that integrates conditional tabular generative adversarial network (CTGAN) for synthetic minority sample generation and CatBoost for classification. A dataset of 733 process records was collected from a continuous caster, and 25 informative features were selected using mutual information. CTGAN was employed to augment the minority class (crack) samples, achieving a balanced training set. Feature distribution analysis and principal component visualization indicated that the synthetic data effectively preserved the statistical structure of the original minority class. Compared with the other machine learning methods, including KNN, SVM, and MLP, CatBoost achieved the highest metrics, with an accuracy of 0.9239, precision of 0.9041, recall of 0.9018, and F1-score of 0.9022. Results show that CTGAN-based augmentation improves classification performance across all models. These findings highlight the effectiveness of GAN-based augmentation for imbalanced industrial data and validate the CTGAN–CatBoost model as a robust solution for online defect prediction in steel manufacturing. Full article

In recent years, data augmentation techniques have become the predominant approach for addressing highly imbalanced classification problems in machine learning. Algorithms such as the Synthetic Minority Over-sampling Technique (SMOTE) and Conditional Tabular Generative Adversarial Network (CTGAN) have proven effective in synthesizing minority class samples. However, these methods often introduce distributional bias and noise, potentially leading to model overfitting, reduced predictive performance, increased computational costs, and elevated cybersecurity risks. To overcome these limitations, we propose a novel architecture, FADEL, which integrates feature-type awareness with a supervised discretization strategy. FADEL introduces a unique feature augmentation ensemble framework that preserves the original data distribution by concurrently processing continuous and discretized features. It dynamically routes these feature sets to their most compatible base models, thereby improving minority class recognition without the need for data-level balancing or augmentation techniques. Experimental results demonstrate that FADEL, solely leveraging feature augmentation without any data augmentation, achieves a recall of 90.8% and a G-mean of 94.5% on the internal test set from Kaohsiung Chang Gung Memorial Hospital in Taiwan. On the external validation set from Kaohsiung Medical University Chung-Ho Memorial Hospital, it maintains a recall of 91.9% and a G-mean of 86.7%. These results outperform conventional ensemble methods trained on CTGAN-balanced datasets, confirming the superior stability, computational efficiency, and cross-institutional generalizability of the FADEL architecture. Altogether, FADEL uses feature augmentation to offer a robust and practical solution to extreme class imbalance, outperforming mainstream data augmentation-based approaches. Full article

This study addresses fairness in machine learning for student academic performance prediction using the UCI Student Performance dataset. We comparatively evaluate logistic regression, Random Forest, and XGBoost, integrating the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and 5-fold cross-validation for robust model training. A comprehensive fairness analysis is conducted, considering sensitive attributes such as gender, school type, and socioeconomic factors, including parental education (Medu and Fedu), cohabitation status (Pstatus), and family size (famsize). Using the AIF360 library, we compute the demographic parity difference (DP) and Equalized Odds Difference (EO) to assess model biases across diverse subgroups. Our results demonstrate that XGBoost achieves high predictive performance (accuracy: 0.789; F1 score: 0.803) while maintaining low bias for socioeconomic attributes, offering a balanced approach to fairness and performance. A sensitivity analysis of bias mitigation strategies further enhances the study, advancing equitable artificial intelligence in education by incorporating socially relevant factors. Full article

Unexpected machine failures in industrial environments lead to high maintenance costs, unplanned downtime, and safety risks. This study proposes a proactive predictive model using a hybrid of eXtreme Gradient Boosting (XGBoost) and Neural Networks (NN) to forecast machine failures. A synthetic dataset capturing recent breakdown history and time since last failure was used to simulate industrial scenarios. To address class imbalance, SMOTE and class weighting were applied, alongside a focal loss function to emphasize difficult-to-classify failures. The XGBoost model was tuned via GridSearchCV, while the NN model utilized ReLU-activated hidden layers with dropout. Evaluation using stratified 5-fold cross-validation showed that the NN achieved an F1-score of 0.7199 and a recall of 0.9545 for the minority class. XGBoost attained a higher PR AUC of 0.7126 and a more balanced precision–recall trade-off. Sample predictions demonstrated strong recall (100%) for failures, but also a high false positive rate, with most prediction probabilities clustered between 0.50–0.55. Additional benchmarking against Logistic Regression, Random Forest, and SVM further confirmed the superiority of the proposed hybrid model. Model interpretability was enhanced using SHAP and LIME, confirming that recent breakdowns and time since last failure were key predictors. While the model effectively detects failures, further improvements in feature engineering and threshold tuning are recommended to reduce false alarms and boost decision confidence. Full article

Sustainable agriculture requires replacing agrochemicals with environmentally friendly products. One alternative is bacterial inoculants with plant-growth-promoting (PGP) activity. Bacterial consortia offer advantages over single-strain inoculants, as they possess more PGP traits and allow the exploitation of bacterial synergies. Synthetic bacterial communities (SynComs) can be used as inoculants that are thoroughly characterized and assessed for efficiency and safety. Here, we describe the construction of a SynCom composed of seven bacterial strains isolated from the rhizosphere of tomato plants and other orchard vegetables. The strains were identified by 16S rDNA sequencing as Pseudomonas spp. (two isolates), Rhizobium sp., Ensifer sp., Microbacterium sp., Agromyces sp., and Chryseobacterium sp. The metagenome of the combined strains was sequenced, allowing the identification of PGP traits and the assembly of their individual genomes. These traits included nutrient mobilization, phytostimulation, and biocontrol. When inoculated into tomato plants in an agricultural soil, the SynCom caused minor effects in soil and rhizosphere bacterial communities. However, it had a high impact on the gene expression pattern of tomato plants. These effects were more significant at the systemic than at the local level, indicating a priming effect in the plant, as signaling through jasmonic acid and ethylene appeared to be altered. Full article

In transient stability assessment (TSA) of power systems, the extreme scarcity of unstable scenario samples often leads to misjudgments of fault risks by assessment models, and this issue is particularly pronounced in new-type power systems with high penetration of renewable energy sources. To address this, this paper proposes a physics-constrained diffusion-based scenario expansion method. It constructs a hierarchical conditional diffusion framework embedded with transient differential equations, combines a spatiotemporal decoupling analysis mechanism to capture grid topological and temporal features, and introduces a transient energy function as a stability boundary constraint to ensure the physical rationality of generated scenarios. Verification on the modified IEEE-39 bus system with a high proportion of new energy sources shows that the proposed method achieves an unstable scenario recognition rate of 98.77%, which is 3.92 and 2.65 percentage points higher than that of the Synthetic Minority Oversampling Technique (SMOTE, 94.85%) and Generative Adversarial Networks (GANs, 96.12%) respectively. The geometric mean achieves 99.33%, significantly enhancing the accuracy and reliability of TSA, and providing sufficient technical support for identifying the dynamic security boundaries of power systems. Full article

The rapid growth of Internet of Things (IoT) environments has increased security challenges due to heightened exposure to cyber threats and attacks. A key problem is the class imbalance in attack traffic, where critical yet underrepresented attacks are often overlooked by intrusion-detection systems (IDS), thereby compromising reliability. We propose Feature-Importance GAN SMOTE (FIGS), an innovative, realistic intrusion-detection framework designed for IoT environments to address this challenge. Unlike other works that rely only on traditional oversampling methods, FIGS integrates sensitivity-based feature-importance analysis, Generative Adversarial Network (GAN)-based augmentation, a novel imbalance ratio (GIR), and Synthetic Minority Oversampling Technique (SMOTE) for generating high-quality synthetic data for minority classes. FIGS enhanced minority class detection by focusing on the most important features identified by the sensitivity analysis, while minimizing computational overhead and reducing noise during data generation. Evaluations on the CICIoMT2024 and CICIDS2017 datasets demonstrate that FIGS improves detection accuracy and significantly lowers the false negative rate. FIGS achieved a 17% improvement over the baseline model on the CICIoMT2024 dataset while maintaining performance for the majority groups. The results show that FIGS represents a highly effective solution for real-world IoT networks with high detection accuracy across all classes without introducing unnecessary computational overhead. Full article

This study introduces an enhanced Intrusion Detection System (IDS) framework for Denial-of-Service (DoS) attacks, utilizing network traffic inter-arrival time (IAT) analysis. By examining the timing between packets and other statistical features, we detected patterns of malicious activity, allowing early and effective DoS threat mitigation. We generate real DoS traffic, including normal, Internet Control Message Protocol (ICMP), Smurf attack, and Transmission Control Protocol (TCP) classes, and develop nine predictive algorithms, combining traditional machine learning and advanced deep learning techniques with optimization methods, including the synthetic minority sampling technique (SMOTE) and grid search (GS). Our findings reveal that while traditional machine learning achieved moderate accuracy, it struggled with imbalanced datasets. In contrast, Deep Neural Network (DNN) models showed significant improvements with optimization, with DNN combined with GS (DNN-GS) reaching 89% accuracy. However, we also used Recurrent Neural Networks (RNNs) combined with SMOTE and GS (RNN-SMOTE-GS), which emerged as the best-performing with a precision of 97%, demonstrating the effectiveness of combining SMOTE and GS and highlighting the critical role of advanced optimization techniques in enhancing the detection capabilities of IDS models for the accurate classification of various types of network traffic and attacks. Full article

Connected and Autonomous Vehicles (CAVs) remain vulnerable to cyberattacks due to inherent security gaps in the Controller Area Network (CAN) protocol. We present a structured Python (3.11.13) framework that repairs structural inconsistencies in a public CAV dataset to improve the reliability of machine learning-based intrusion detection. We assess the effect of training data volume and compare Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers across four attack types: DoS, Fuzzy, RPM spoofing, and GEAR spoofing. XGBoost outperforms RF, achieving 99.2 % accuracy on the DoS dataset and 100 % accuracy on the Fuzzy, RPM, and GEAR datasets. The Synthetic Minority Oversampling Technique (SMOTE) further enhances minority-class detection without compromising overall performance. This methodology provides a generalizable framework for anomaly detection in other connected systems, including smart grids, autonomous defense platforms, and industrial control networks. Full article

This study aims to evaluate the potential of Lemna minor (common duckweed) for the removal of galaxolide (HHCB) from polluted water, a compound commonly used in consumer products such as perfumes and detergents. The focus was to identify the optimal conditions for removal, determine the removal efficiency, and elucidate the mechanisms involved. The experiment was conducted by cultivating Lemna minor using as a cultivation medium synthetic sewage and laboratory solutions (MilliQ water) containing galaxolide at two levels of concentration (1034 µg·L⁻¹ and 2326 µg·L⁻¹). The plants were exposed to light for 16 h a day and grown at pH 5. Removal efficiency was assessed through liquid chromatography (HPLC) with fluorescence detection (FLD). Kinetics of observed process was modelled using a pseudo-first-order equation. The study of the HHCB decay mechanism included determining the contributions to the final effect of the following processes occurring simultaneously: sorption on the plant surface, photodegradation, and uptake by Lemna. The removal efficiency (RE%) of galaxolide by Lemna minor was 99.7% when aqueous standard solution was used as the cultivation medium after 14 days, and between 97.8% and 98.6% in the case of wastewater samples. Sorption onto plants surface, photodegradation, and uptake by the plants were identified as the primary mechanisms for HHCB removal. Toxicity studies revealed that galaxolide exposure adversely affected Lemna minor growth, altering photosynthetic pigments (chlorophyll and carotenoid) levels. Full article

Machine learning (ML) techniques are becoming increasingly valuable for modeling the transport of pollutants in plant systems. However, two challenges (small sample sizes and a lack of quantitative calculation functions) remain when using ML to predict migration in hydroponic systems. For the bioaccumulation of per- and polyfluoroalkyl substances, we studied the key factors and quantitative calculation equations based on data augmentation, ML, and symbolic regression. First, feature expansion was performed on the input data after data preprocessing; the most important step was data augmentation. The original training set was expanded nine times by combining the synthetic minority oversampling technique and a variational autoencoder. Subsequently, the four ML models were applied to the test set to predict the selected output parameters. Categorical boosting (CatBoost) had the highest prediction accuracy (R² = 0.83). The Shapley Additive Explanation values indicated that molecular weight and exposure time were the most important parameters. We applied three symbolic regression models to obtain accurate prediction equations based on the original and augmented data. Based on augmented data, the high-dimensional sparse interaction equation exhibited the highest accuracy (R² = 0.776). Our results indicate that this method could provide crucial insights into absorption and accumulation in plant roots. Full article

Background/Objectives: Adrenocortical carcinoma (ACC) is a rare, aggressive malignancy with poor prognosis, particularly in metastatic cases. The Ki-67 proliferation index is a recognized marker of tumor aggressiveness, yet its role in guiding diagnostic imaging and surgical decision-making remains underexplored. This study evaluates Ki-67’s predictive value for metastasis at diagnosis, leveraging artificial intelligence (AI) to inform personalized, minimally invasive strategies for ACC management. Methods: We retrospectively analyzed 53 patients with histologically confirmed ACC from the Adrenal-ACC-Ki67-Seg dataset in The Cancer Imaging Archive. All patients had Ki-67 indices from surgical specimens and preoperative contrast-enhanced CT scans. Descriptive statistics, t-tests, ANOVA, and multivariable logistic regression evaluated associations between Ki-67, tumor size, age, and metastasis. Random Forest classifiers—with and without the Synthetic Minority Oversampling Technique (SMOTE)—were developed to predict metastasis. A Ki-67-only model served as a baseline comparator. Model performance was assessed using the area under the curve (AUC) and DeLong’s test. Results: Patients with metastatic disease had significantly higher Ki-67 indices (mean 39.4% vs. 21.6%, p < 0.05). Logistic regression identified Ki-67 as the sole significant predictor (OR = 1.06, 95% CI: 1.01–1.12). The Ki-67-only model achieved an AUC of 0.637, while the SMOTE-enhanced Random Forest achieved an AUC of 0.994, significantly outperforming all others (p < 0.001). Conclusions: Ki-67 is significantly associated with metastasis at ACC diagnosis and demonstrates independent predictive value in regression analysis. However, integration with machine learning models incorporating tumor size and age significantly improves overall predictive accuracy, supporting AI-assisted risk stratification and precision imaging strategies in adrenal cancer care. Full article

The aircraft fuel pump is a critical component of the aviation fuel supply system, and its fault diagnosis is essential in ensuring flight safety. However, in practical operating conditions, fault samples are scarce and data distributions are highly imbalanced, which severely limits the ability of traditional models to identify minority-class faults. To address this challenge, this paper proposes a fault diagnosis method for aircraft fuel pumps based on adaptive synthetic data augmentation using a Conditional Variational Autoencoder (CVAE). The CVAE generates semantically consistent and feature-diverse minority-class samples under class-conditional constraints, thereby enhancing the overall representational capacity of the dataset. Simultaneously, the Adaptive Synthetic (ADASYN) approach adaptively augments hard-to-classify samples near decision boundaries, enabling fine-grained control over sample distribution. The integration of these two techniques establishes a “broad coverage + focused refinement” augmentation strategy, effectively mitigating the class imbalance problem. Experimental results demonstrate that the proposed method significantly improves the recognition performance of minority-class faults on real-world aircraft fuel pump fault datasets. Full article

As a synthetic analogue of vasopressin, desmopressin or DDAVP has well established hemostatic properties. We present a review of DDAVP and summarize the clinical and laboratory evidence for its use in hemophilia A, von Willebrand disease (VWD), platelet function disorders, uremia, liver cirrhosis, and pregnancy, followed by illustrative examples of its broad efficacy from our local practice. In brief, DDAVP acts to release von Willebrand factor (VWF) and factor VIII from endogenously stored reserves, thereby correcting plasma deficiencies present in mild to moderately affected patients with hemophilia A and VWD. Thus, DDAVP represents a non-transfusional therapy for these disorders. Typically, a trial of DDAVP is arranged to assess individual responsiveness before employing DDAVP clinically, since there is individual variation in responsiveness. Thereafter, DDAVP can be utilized in responsive patients for clinical use and provides a factor replacement sparing strategy in these patients for some clinical situations. Nevertheless, DDAVP is best applied as a factor replacement sparing strategy, especially for minor procedures or short-term use. Full article