Hybrid Sampling and Ensemble Learning for Food Safety Sampling Inspection Classification.

The occurrence of wind shear and severe thunderstorms during the final approach phase contributes to nearly half of all aviation accidents. Pilots usually employ the go-around procedure in order to lower the likelihood of an unsafe landing. However, multiple factors influence the go-arounds induced by wind shear. In order to predict the wind shear-induced go-around, this study utilized a cutting-edge AI-based Combined Kernel and Tree Boosting (KTBoost) framework with various data augmentation strategies. First, the KTBoost model was trained, tested, and compared to other Machine Learning models using the data extracted from Hong Kong International Airport (HKIA)-based Pilot Reports for the years 2017–2021. The performance evaluation revealed that the KTBoost model with Synthetic Minority Oversampling Technique - Edited Nearest Neighbor (SMOTE-ENN)- augmented data demonstrated superior performance as measured by the F1-Score (94.37%) and G-Mean (94.87%). Subsequently, the SHapley Additive exPlanations (SHAP) approach was employed to elucidate the interpretation of the KTBoost model using data that had been treated with the SMOTE-ENN technique. According to the findings, flight type, wind shear magnitude, and approach runway contributed the most to the wind shear-induced go-around. Compared to international flights, Hong Kong-based airlines endured the highest number of wind shear-induced go-arounds. Shear due to the tailwind contributed more to the go-around than the headwinds. The runways with the most wind shear-induced Go-arounds were 07C and 07R.

Predicting survival outcomes in critical accidents has been a focal point in machine learning research. This study addresses several limitations of existing methods, including insufficient management of data imbalance, lack of emphasis on hyperparameter tuning, and proneness to overfitting. Many existing models struggle to generalize effectively on imbalanced datasets or depend on default hyperparameter settings, resulting in biased predictions. By integrating Principal Component Analysis (PCA), hyperparameter optimization, and resampling methods, as well as combining Edited Nearest Neighbors (ENN) with the Synthetic Minority Oversampling Technique (SMOTE), the model significantly improves predictive accuracy and model generalization. An ensemble model combining seven machine learning algorithms—Logistic Regression, Support Vector Machine, KNN, Random Forest, XGBoost, LightGBM, and CatBoost—was applied to predict survival outcomes. Stochastic Weighted Averaging (SWA) was applied to mitigate overfitting and enhance generalization. The accuracy increased from 91.97% to 94.89% after SWA was applied in this specific scenario. The combination of PCA-based dimensionality reduction, hyperparameter tuning, and resampling techniques (ENN + SMOTE) ensured the model handled data imbalance and optimized predictive accuracy. The final model demonstrated excellent performance, with Area Under the Curve (AUC) and Average Precision (AP) values both reaching 0.98, indicating high accuracy and precision. These improvements were validated using the Titanic dataset in a binary classification problem of predicting passenger survival. The results emphasize that ensemble learning, enhanced by SWA, offers a powerful framework for handling imbalanced and complex datasets, providing significant advancements in predictive modeling accuracy. This study provides insights into how machine learning techniques can be effectively combined to solve classification challenges in real-world scenarios.

Class imbalance in vehicle payment method classification can cause predictive models to become biased toward the majority. This study aims to build a classification model for automotive consumer payment methods using Extreme Gradient Boosting (XGBoost), with class balancing handled through the Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN), and model interpretability performed using SHAP (SHapley Additive Explanations). The dataset consisted of 11,011 records and 13 attributes derived from Toyota vehicle delivery order transactions. Results show that the XGBoost model without balancing achieved 67.37% accuracy but only 0.24 recall for the Cash class. After applying SMOTE, the recall for the Cash class improved to 0.58, while ADASYN produced a similar improvement at 0.59, with overall accuracy maintained at around 61–62% and a stable ROC-AUC of 0.65. Feature importance and SHAP analysis identified c_vehicle_model and c_city as the most influential factors in predicting the payment method. From a business perspective, the improved ability to detect cash customers reduces the risk of misclassification and enables dealers to better segment customer payment preference. This supports more effective marketing campaigns, sales strategies, and financing risk management. The combination of XGBoost, SMOTE, ADASYN, and SHAP has proven effective in handling imbalanced data while offering transparent interpretability of predictions, making it a practical foundation for data-driven decision-making in the automotive industry.

Background: Stroke remains a critical global health concern, disproportionately affecting individuals with hypertension which is a well-established, modifiable risk factor. Traditional risk scoring systems often fall short in accurately predicting stroke onset due to their reliance on fixed clinical thresholds and limited variable interaction modeling. As the complexity of health data increases, machine learning (ML) and explainable artificial intelligence (XAI) present powerful tools to uncover hidden patterns and enable precision risk stratification. Objectives: This study proposes a novel, interpretable machine learning framework that uses ensemble learning and SHapley Additive exPlanations (SHAP) to enhance stroke risk prediction in hypertensive patients. The objective is twofold: to improve the predictive power of stroke models and to provide clinically relevant insights that support real-time, data-driven decisions in preventive care. Methods: We utilized a real-world clinical dataset encompassing demographic, physiological, and behavioral variables associated with stroke. Data preprocessing included k-nearest neighbor imputation for missing values, normalization of continuous features, and class balancing via Synthetic Minority Oversampling Technique (SMOTE). A hybrid feature selection pipeline, combining the sparsityenforcing capabilities of LASSO regression with the iterative refinement of Recursive Feature Elimination (RFE), was employed to identify the most salient predictors. Multiple ML models, including logistic regression, deep neural networks, random forests, and gradient boosting machines, were trained and validated using cross-validation. SHAP values were computed post-training to enable individualized, interpretable model outputs. Results: Ensemble models, particularly Gradient Boosting and Random Forest, demonstrated superior discriminative performance, achieving AUC-ROC scores above 0.78 following class balancing. The integrated LASSO-RFE approach revealed age, hypertension status, and average glucose levels as dominant predictors across models. SHAP visualizations confirmed the influence of these features, while also highlighting nuanced interactions involving lifestyle and socioeconomic variables. Logistic Regression, when optimized for recall, achieved the highest balanced accuracy (0.77), reinforcing the clinical utility of simpler models when interpretability is paramount. Conclusion: This study introduces a transparent and high-performing machine learning framework for stroke risk prediction in hypertensive individuals. By integrating ensemble learning, hybrid feature selection, and explainable AI, the framework bridges the gap between predictive modeling and clinical applicability. These findings support the deployment of interpretable ML tools in routine care, enabling proactive interventions, personalized patient education, and ultimately, reduced stroke incidence.

Many local outlier detection algorithms have been proposed inspired by the idea of local outlier factor (LOF). However, they often have low detection performance and are sensitive to neighborhood size because there is a major defect in their calculation formulas of outlier degree and the kNN (k-nearest neighbors) method is widely used to quantify a neighborhood of an instance. To address these issues, we define a novel nearest neighbors tree (NNT) to measure a neighborhood of an instance. Meanwhile, we propose a local structure outlier factor (LSOF), which score each local structure instead of each data point and report the top-scored local structures as anomalous local structures, where outliers and groups of outliers are easily divided according to characteristics of the NNT. Our experimental results demonstrate that the competitive behavior of our method on both synthetic and real-world datasets.

Objective: Alzheimer's disease (AD) is a neurodegenerative disorder that severely impairs cognitive function across various age groups, ranging from early to late sixties. It progresses from mild to severe stages, so an accurate diagnostic tool is necessary for effective intervention and treatment planning. Methods: This work proposes a novel AD classification architecture that integrates depthwise separable convolutional layers with traditional convolutional layers to efficiently extract features from structural magnetic resonance imaging (sMRI) scans. This model benefits from excellent feature extraction and lightweight operation, which reduces the number of parameters without compromising accuracy. The model learns from scratch with optimized weight initialization, resulting in faster convergence and improved generalization. However, medical imaging datasets contain class imbalance as a major challenge, which often results in biased models with poor generalization to the underrepresented disease stages. A hybrid sampling approach combining SMOTE (synthetic minority oversampling technique) with the ENN (edited nearest neighbors) effectively handles the complications of class imbalance issue inherent in the datasets. An explainable activation space occlusion sensitivity map (ASOP) pixel attribution method is employed to highlight the critical regions of input images that influence the classification decisions across different stages of AD. Results and Conclusions: The proposed model outperformed several state-of-the-art transfer learning architectures, including VGG19, DenseNet201, EfficientNetV2S, MobileNet, ResNet152, InceptionV3, and Xception. It achieves noteworthy results in disease stage classification, with an accuracy of 98.87%, an F1 score of 98.86%, a precision of 98.80%, and recall of 98.69%. These results demonstrate the effectiveness of the proposed model for classifying stages of AD progression.

Outlier detection is an important task in data mining with numerous applications, including credit card fraud detection, video surveillance, etc. A recent work on outlier detection has introduced a novel notion of local outlier in which the degree to which an object is outlying is dependent on the density of its local neighborhood, and each object can be assigned a Local Outlier Factor (LOF) which represents the likelihood of that object being an outlier. Although the concept of local outliers is a useful one, the computation of LOF values for every data objects requires a large number of κ-nearest neighbors searches and can be computationally expensive. Since most objects are usually not outliers, it is useful to provide users with the option of finding only n most outstanding local outliers, i.e., the top-n data objects which are most likely to be local outliers according to their LOFs. However, if the pruning is not done carefully, finding top-n outliers could result in the same amount of computation as finding LOF for all objects. In this paper, we propose a novel method to efficiently find the top-n local outliers in large databases. The concept of "micro-cluster" is introduced to compress the data. An efficient micro-cluster-based local outlier mining algorithm is designed based on this concept. As our algorithm can be adversely affected by the overlapping in the micro-clusters, we proposed a meaningful cut-plane solution for overlapping data. The formal analysis and experiments show that this method can achieve good performance in finding the most outstanding local outliers.

- Healthcare fraud detection is an important challenge in modern healthcare systems due to the large volume of medical claims and the highly imbalanced nature of fraud datasets. Fraudulent claims represent only a small portion of the total claims, which makes it difficult for traditional machine learning models to accurately identify fraudulent activities. Conventional techniques such as Random Oversampling (ROS), Synthetic Minority Oversampling Technique (SMOTE), and Random Undersampling (RUS) are commonly used to address class imbalance, but they often lead to issues such as overfitting, noise generation, or loss of important information. In this study, a machine learning-based approach is proposed to improve Medicare fraud detection using the Medicare Part B dataset. The proposed framework applies a hybrid resampling technique called SMOTE-ENN, which combines SMOTE for generating synthetic minority samples with Edited Nearest Neighbors (ENN) to remove noisy and irrelevant data instances. Logistic Regression is used as the classification algorithm to detect fraudulent healthcare claims. The model performance is evaluated using multiple metrics, including accuracy, precision, recall, F1-score, AUC-ROC, and Area Under the Precision-Recall Curve (AUPRC). Experimental results demonstrate that the proposed approach achieves an accuracy of 98%, indicating its effectiveness in handling imbalanced datasets and improving fraud detection in healthcare systems. Key Words: Healthcare Fraud Detection, Machine Learning, SMOTE-ENN, Logistic Regression, Imbalanced Data, Medicare Claims.

RBPro-RF: Use Chou’s 5-steps rule to predict RNA-binding proteins via random forest with elastic net

PMU data manipulation attacks (DMA) which maliciously modify the synchrophasor measurements in power systems, is an emerging type of cyber-attack aiming to mislead the control center into taking erroneous control actions. Widespread application of PMU produces a vast amount of time-synchronized historical operating data, making it possible to turn to machine learning approaches to detect the attacks. However, anomalous events occur rarely in practice. To construct a balanced training dataset, where normal and anomalous instances need to be evenly extracted and labeled from historical operating records, is sure to cost plenty of human efforts and may introduce uncontrollable experience errors. In this paper, we design a novel detection model with Synthetic Minority Over-sampling TEchnique (SMOTE)-based data augmentation and Edited Nearest Neighbors (ENN)-based data cleaning technique to reconstruct the imbalanced training dataset into a balanced one to train the subsequent XGBoost classifier. Experiments show that although rare anomalous historical event records are needed, our model is still able to effectively detect out DMAs in power systems.

The study presents a comprehensive framework for optimizing customer retention budget by integrating clustering, classification, and mathematical optimization techniques. The study begins with the IBM Telco dataset, which is prepared through data cleansing, encoding, and scaling. In the preliminary phase, customer segmentation is performed using K-Means clustering, with ? = 3 and ? = 4 identified as optimal based on the elbow method and Silhouette score. The configurations produced three (Premium, Standard, Low) and four (Premium, Standard Plus, Standard, Low) customer segments based on purchase preferences, which served as input features for churn prediction. In the second phase, the dataset was divided into training and test sets in an 80:20 ratio, followed by data balancing using the Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbors (ENN). Multiple classification algorithms were evaluated, including Naive Bayes (NB), Random Forest (RF), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) using F1-score as the performance metric. CatBoost and LightGBM, with k values of 3 and 4, respectively, were the highest-performing classification models, with only minimal differences in performance. Ultimately, customer segmentation established customer prioritization, whereas churn prediction assessed customer churn likelihood. Four distinct configurations were assessed utilizing mixed-integer linear programming (MILP) to optimise retention budget allocation within uniform budget constraints, discount amounts, and churn thresholds. In both the k=3 and k=4 scenarios, CatBoost surpassed LightGBM, with CatBoost at K=3 effectively discounting 66% of at-risk consumers across all three segments, hence improving the intervention's efficacy and budget allocation, making it the ideal choice for maximizing customer retention. The results demonstrate the importance of segmentation in enhancing retention budgeting and budget optimization, particularly concerning parameter sensitivity.

Cardiovascular diseases remain one of the leading causes of mortality worldwide, particularly in low- and middle-income countries. Early and accurate prediction of heart disease is essential for timely intervention and improved patient outcomes. Machine learning techniques offer promising solutions; however, challenges such as class imbalance, lack of interpretability, and limited real-world validation persist. In this study, a machine learning–based heart disease prediction framework was developed using a real-world clinical dataset comprising 5000 patient records collected from healthcare facilities in Bangladesh. Data preprocessing included cleaning, feature encoding, train–test splitting, and class imbalance handling using the Synthetic Minority Oversampling Technique (SMOTE). Multiple machine learning models—Logistic Regression, Decision Tree, Support Vector Machine, and Random Forest—were evaluated using 10-fold stratified cross-validation. Model performance was assessed using accuracy, precision, recall, and F1-score. SHAP (SHapley Additive exPlanations) was employed to enhance model interpretability. The best-performing model was deployed as a web-based decision support system. Among the evaluated models, the Random Forest classifier achieved the best performance, with an accuracy of 98%, recall of 96%, and F1-score of 96%. Ablation studies demonstrated the effectiveness of SMOTE, feature integration, and ensemble modeling. SHAP analysis identified clinically relevant features contributing to heart disease prediction, enhancing transparency and trust in model decisions. The proposed framework provides an accurate, interpretable, and practical solution for heart disease prediction using real-world clinical data. The integration of explainable machine learning and web-based deployment highlights its potential for clinical decision support. Future work will focus on multi-center prospective validation and adaptive model updating to further improve generalizability and real-world applicability. • A large real-world clinical dataset of 5000 patients was used for robust heart disease prediction. • A Random Forest model with SMOTE achieved high performance (98% accuracy, 96% recall). • Comprehensive ablation and model comparison analyses validate methodological robustness. • SHAP-based explainability provides transparent and clinically interpretable predictions. • The proposed system is deployed as a web-based clinical decision support tool.

Predicting mortality among people living with HIV enables clinicians to implement timely, targeted, and preventive interventions at the start of antiretroviral therapy (ART). However, prognostic models must rely strictly on baseline predictors to avoid look-ahead bias and ensure scientific validity. This study evaluates machine-learning (ML) algorithms for baseline mortality prediction using routine electronic medical record data. This study aims to predict mortality among people living with HIV receiving ART using baseline clinical and sociodemographic characteristics through ML models in public health facilities of Gondar City Administration, Northwest Ethiopia. The retrospective cohort study was conducted using electronic medical record data from 12,871 people living with HIV on ART (2005-2024). Seven base classifiers were evaluated using stratified 10-fold cross-validation. Synthetic minority oversampling technique (SMOTE)-balanced variants were used only for sensitivity analysis. SMOTE oversampling was applied only to training folds; the final evaluation used the original imbalanced test data. Shapley Additive Explanations (SHAP) analysis identified key baseline predictors. Gradient boosting on the original data achieved superior performance (accuracy 87.0%, F1-score 0.619, area under the receiver operating characteristic curve 0.859), outperforming extreme gradient boosting (F1-score 0.609, area under the receiver operating characteristic curve 0.835) and SMOTE variants. The SHAP analysis identified education level, lack of formal education (+0.84), and a low baseline cluster of differentiation 4 (CD4; a type of immune cell count) count of 140 cells/mm³ (+0.54) as substantially increasing predicted mortality risk. Urban residence (-0.35) and working functional status (-0.12) showed protective effects, whereas age (45 y; -0.02) had minimal influence in the illustrated case. Globally, lower CD4 counts and the absence of formal education were consistently associated with increased mortality risk. Ensemble ML models demonstrated moderate-to-strong discrimination for predicting mortality among people living with HIV using strictly baseline routine electronic medical record data. SHAP-based interpretability confirmed that educational attainment and baseline CD4 count were the dominant determinants of predicted mortality risk, underscoring the combined influence of socioeconomic vulnerability and immunological status at ART initiation. These findings support the potential utility of interpretable ML models for early risk stratification and targeted clinical decision-making in resource-limited settings; however, external validation is required before routine clinical implementation.

After the local outlier factor was first proposed, there is a large family of local outlier detection approaches derived from it. Since the existing approaches only focus on the extent of overall separation between an object and its neighbors, and ignore the degree of dispersion between them, the precision of these approaches will be affected by various degrees in the scattered datasets. In addition, the outlier data occupy a relatively small amount in the dataset, but the existing approaches need to perform local outlier factor calculation on all data during the outlier detection, which greatly reduces the efficiency of the algorithms. In this paper, we redefine a local outlier factor called local deviation coefficient (LDC) by taking full advantage of the distribution of the object and its neighbors. And then, we propose a safe non-outlier objects elimination approach named as rough clustering based on multi-level queries (RCMLQ) to preprocess the datasets to eliminate the non-outlier objects to the utmost. Finally, an efficient local outlier detection approach named as efficient density-based local outlier detection for scattered data (E2DLOS) is proposed based on the LDC and RCMLQ. The RCMLQ greatly reduces the amount of data that needs to be quantified for local outlier factor and the LDC is more sensitive to the degree of anomaly of the scattered datasets, and so the E2DLOS improves the existing local outlier detection approaches in time efficiency and detection accuracy. Experiments show that the LDC can better reflect the true abnormal situations of the data for the scattered datasets. And the RCMLQ can be used in parallel with the traditional methods of improving the efficiency of the nearest neighbor search, which can further improve the efficiency of the E2DLOS algorithm by about 16%.

Endoscopic disease activity monitoring is important for the long-term management of patients with ulcerative colitis (UC), there is currently no widely accepted non-invasive method that can effectively predict endoscopic disease activity. We aimed to develop and validate machine learning (ML) models for predicting it, which are desired to reduce the frequency of endoscopic examinations and related costs. The patients with a diagnosis of UC in two hospitals from January 2016 to January 2021 were enrolled in this study. Thirty nine clinical and laboratory variables were collected. All patients were divided into four groups based on MES or UCEIS scores. Logistic regression (LR) and four ML algorithms were applied to construct the prediction models. The performance of models was evaluated in terms of accuracy, sensitivity, precision, F1 score, and area under the receiver-operating characteristic curve (AUC). Then Shapley additive explanations (SHAP) was applied to determine the importance of the selected variables and interpret the ML models. A total of 420 patients were entered into the study. Twenty four variables showed statistical differences among the groups. After synthetic minority oversampling technique (SMOTE) oversampling and RFE variables selection, the random forests (RF) model with 23 variables in MES and the extreme gradient boosting (XGBoost) model with 21 variables in USEIS, had the greatest discriminatory ability (AUC = 0.8192 in MES and 0.8006 in UCEIS in the test set). The results obtained from SHAP showed that albumin, rectal bleeding, and CRP/ALB contributed the most to the overall model. In addition, the above three variables had a more balanced contribution to each classification under the MES than the UCEIS according to the SHAP values. This proof-of-concept study demonstrated that the ML model could serve as an effective non-invasive approach to predicting endoscopic disease activity for patients with UC. RF and XGBoost, which were first introduced into data-based endoscopic disease activity prediction, are suitable for the present prediction modeling.