Perspective Analysis of Anti-aging Products Using Voting-Based Ensemble Technique

Hepatitis B virus (HBV) infection remains a public health concern in China, especially among middle-aged and older adults. Accurate prediction models can assist in early detection and targeted interventions. However, predicting HBV infection using population-level data is challenging due to its relatively low prevalence, which creates highly imbalanced datasets that undermine the performance of traditional predictive models. This study aimed to develop a machine learning model to predict HBV infection using an ensemble approach designed to handle class imbalance. Our study conducted a cross-sectional analysis of participants aged 45 and above from the China Health and Retirement Longitudinal Study 2011. To address the class imbalance in HBV infection, the synthetic minority oversampling technique (SMOTE) was employed. We developed machine learning models, including logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), adaptive boosting classifier (AdaBoost), extreme gradient boosting (XGBoost), and stacking ensemble model (SEM), to predict HBV infection and identify predictors. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (AP), accuracy, sensitivity, specificity, precision, F1 score, and Brier score. Shapley additive explanation (SHAP) analysis was applied to interpret each predictor’s individual contribution and direction to the model’s output. In the final analysis, 8,954 participants were included, among whom 0.64% self-reported HBV infection, confirming highly imbalanced data. The SEM model achieved balanced performance (AUC: 87.93%, AP: 18.85%, F1: 15.66%, Brier score: 0.03). Machine learning models also identified several new important predictors, including birth season, depression, and knee height. An ensemble machine learning model incorporating techniques to manage data imbalance can accurately predict HBV infection in Chinese middle-aged and older adults. The novel predictors we identified offer new insights into HBV risk in ageing populations and support the development of more targeted prevention strategies.

Prediction and variable importance analysis for small-strain stiffness of soil based on ensemble learning with Bayesian optimization

The digital marketing for food delivery business is the focus of this study, which investigates the use of ensemble machine learning (ML) approaches. The study's overarching goal is to pave the way for artificial intelligence (AI)-based recommendations by analyzing consumer data with the hope of discovering consumer preferences and predicting behavior. In order to improve the accuracy of predictions, the ensemble method combines the results of decision trees, naïve Bayes, and k-nearest neighbor algorithms. Both the decision tree and nearest neighbor algorithms were able to obtain perfect predictions with zero error and 100% accuracy, as seen in the accuracy matrix charts. On the other hand, the naïve Bayes method was able to accurately identify labels in all classes with a minimal error rate of 0.028 and a high accuracy of 97.175%. With a success rate of over 90%, the majority vote method allows models to be integrated using less than 50% of the randomized data, which minimizes customer dissatisfaction. When taken as a whole, these ML algorithms greatly improve the efficiency and efficacy of food delivery business digital marketing campaigns by cutting down on wasted time and money.

Sentiment analysis on the satisfaction level of PDDikti operators is very important to find out how PDDikti operators feel after the version of the academic reporting application for higher education was upgraded, namely Neo Feeeder. The increase in the version of this application causes some of the features in it to not function properly. So some academic reporting activities from tertiary institutions experience problems. As a result of this condition, the most felt impact is students, where students experience delays in graduation. Then it is necessary to evaluate through sentiment analysis from PDDikti operators to find out the response from operators and be able to provide positive suggestions to developers from the PDDikti reporting application. This study applies several classification methods for sentiment analysis at once, including the Random Forest algorithm, the Support Vector Machine algorithm, the Multinomial Naïve Bayes algorithm, the Decision Tree algorithm, and the K-Nearest Neighbor algorithm. Of the 5 methods applied, the results of their performance accuracy will be compared. The performance of the highest classification algorithm is the K-Nearest Neighbor (K-NN) algorithm which produces an accuracy value when testing data, which is up to 90% using the oversampling technique in unbalanced classes. While the lowest classification accuracy performance value is in the Multinomial Naïve Bayes (MNB) algorithm with a value of 76%. It is proven that oversampling can help the performance of the classification algorithm to be more optimal. Thus, it should be noted that the balance of data classes is an important factor when applying the classification method.

This study conducts an extensive comparative analysis of computational intelligence approaches aimed at predicting the compressive strength (CS) of concrete, utilizing two non-destructive testing (NDT) methods: the rebound hammer (RH) and the ultrasonic pulse velocity (UPV) test. In the ensemble learning approach, the six most popular algorithms (Adaboost, CatBoost, gradient boosting tree (GBT), random forest (RF), stacking, and extreme gradient boosting (XGB)) have been used to develop the prediction models of CS of concrete based on NDT. The ML models have been developed using a total of 721 samples, of which 111 were cast in the laboratory, 134 were obtained from in-situ testing, and the other samples were gathered from the literature. Among the three categories of analytical models—RH models, UPV models, and combined RH and UPV models; seven, ten, and thirteen models have been used respectively. AdaBoost, CatBoost, GBT, RF, Stacking, and XGB models have been used to improve the accuracy and dependability of the analytical models. The RH-M5, UPV-M6, and C-M6 (combined UPV and RH model) models were found with highest performance level amongst all the analytical models. The MAPE value of XGB was observed to be 84.37%, 83.24%, 77.33%, 59.46%, and 81.08% lower than AdaBoost, CatBoost, GBT, RF, and stacking, respectively. The performance of XGB model has been found best than other soft computing techniques and existing traditional predictive models.

Landslides are among the most hazardous geomorphological processes, especially in mountainous terrains where they threaten lives, disrupt infrastructure, and degrade ecosystems. The Chamoli district in Uttarakhand, India, with its rugged topography, erratic rainfall, and anthropogenic disturbances, is highly susceptible to such events. This research focuses on generating a detailed landslide susceptibility map for Chamoli using four machine learning algorithms Naïve Bayes (NB), K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). A total of sixteen causative factors were selected and processed using Geographic Information System (GIS) techniques. To avoid multicollinearity and ensure the reliability of input variables, statistical validation was performed. The landslide inventory, consisting of 778 past events, was split into 70% training and 30% testing datasets for model development and evaluation. The models were assessed using statistical indicators such as sensitivity, specificity, precision, accuracy, F1-score, Matthews Correlation Coefficient (MCC), and the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). Among the models, XGBoost showed the highest performance (AUC = 0.95), followed by RF (0.83), KNN (0.79), and NB (0.78). The susceptibility analysis revealed that XGBoost categorized 17.53% of the area as highly vulnerable, outperforming RF (14.08%), KNN (14.00%), and NB (4.55%). The enhanced accuracy of XGBoost and RF stems from their ensemble learning approach, which effectively captures nonlinear relationships and mitigates overfitting. The resulting susceptibility maps are crucial tools for risk management, infrastructure planning, and sustainable development in hazard-prone Himalayan regions. This study reinforces the importance of machine learning in natural hazard assessment and suggests that future efforts should incorporate real-time data, finer-resolution remote sensing, and deep learning methods to further refine prediction accuracy.

Over the past few decades, it has been observed a remarkable progression in the development of computer aid models in the field of civil engineering. Machine learning models provide a reliable and robust alternative modeling design for solving complex engineering issues. The current study introduced three versions of newly explored ensemble machine learning models [extreme gradient boosting (XGBoost), multivariate adaptive regression spline (MARS) and random forest (RF)] for fiber-reinforced polymer (FRP) composite strain prediction. An experimental dataset were collected from the literature with total number of 729 experiments. The dataset is presented the FRP strain and its influential parameters including material geometry, strength properties, strain properties, FRP properties, and confinement properties. Five different input combination were built for the prediction process of the FRP composite strain. The current research results were validated against the well-established literature review empirical formulations and the machine learning models. In general, the modeling results confirmed the capacity of the proposed new ML models in predicting the strain enhancement ratio. The fifth input combination incorporated all the predators attained the best modeling accuracy results. However, the developed MARS model could achieve acceptable and superior prediction results using only strain properties parameters. Overall, the research finding confirmed the significant of the proposed ensemble ML as reliable alternative computer aid model for solving strain enhancement ratio problem.

Residential building energy consumption estimation: A novel ensemble and hybrid machine learning approach

Stress is an almost basic human instinct, especially in the internet age. People’s health is being endangered by psychological stress. It is critical to diagnose stress early on to provide proactive therapy. In the age of the Internet, social media has an enormous impact on human thinking. It can cause mental health issues such as stress but can also play an important part in detecting it. Advancements in machine learning and natural language processing have enabled information extraction from a huge amount of raw textual data. In this study machine learning approach is used to detect stress in a social media text. Data used in this study is from Reddit. Classical and Ensemble machine learning approaches were used for stress detection. Classical Techniques include Decision Tree, Logistic Regression, support vector machine, Random Forest, and Na ̈ıve Bayes. Ensemble approaches used are boosting, bagging, and voting. The ensemble approach was able to provide better results than all machine learning baselines on the dataset. Also, it was able to outperform all non-transformed-based neural network architectures discussed in the baseline. The best-performing model in this study is Logistic regression with a 76.6% F1 Score.

The aaccurate prediction of enzymes with environment detoxification functions is crucial, not only to achieve a better understanding of bioremediation strategies, but also to alleviate environmental pollution. In the present study, a novel machine learning model was introduced which classifies enzymes by their toxin degradation ability. In this model, two different sets of data were used which include enzymes that can catalyze the toxin degradation as a positive dataset and non-toxin-degrading enzymes as a negative dataset. Further, a comparison of multiple classifiers was performed to find the best model and a Random Forest (RF) classifier was selected due to its strong performance. To enhance the accuracy, we combined RF with a Deep Neural Network (DNN), forming an ensemble model which effectively integrated both techniques. This combination achieved 95% precision, surpassing individual models. Our ensemble model not only ensures high prediction accuracy but also reliably differentiates toxin-degrading enzymes from non-degrading ones. This study highlights the power of combining classical machine learning with deep learning to advance prediction. Our model represents a significant step in enzyme classification and serves as a valuable resource for environmental biotechnology, food nutrition, and health applications.

Although the goal of rectal cancer treatment is to restore gastrointestinal continuity, some patients with rectal cancer develop a permanent stoma (PS) after sphincter-saving operations. Although many studies have identified the risk factors and causes of PS, few have precisely predicted the probability of PS formation before surgery. To validate whether an artificial intelligence model can accurately predict PS formation in patients with rectal cancer after sphincter-saving operations. Patients with rectal cancer who underwent a sphincter-saving operation at Taipei Medical University Hospital between January 1, 2012, and December 31, 2021, were retrospectively included in this study. A machine learning technique was used to predict whether a PS would form after a sphincter-saving operation. We included 19 routinely available preoperative variables in the artificial intelligence analysis. To evaluate the efficiency of the model, 6 performance metrics were utilized: accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiving operating characteristic curve. In our classification pipeline, the data were randomly divided into a training set (80% of the data) and a validation set (20% of the data). The artificial intelligence models were trained using the training dataset, and their performance was evaluated using the validation dataset. Synthetic minority oversampling was used to solve the data imbalance. A total of 428 patients were included, and the PS rate was 13.6% (58/428) in the training set. The logistic regression (LR), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), random forest, decision tree and light gradient boosting machine (LightGBM) algorithms were employed. The accuracies of the logistic regression (LR), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), random forest (RF), decision tree (DT) and light gradient boosting machine (LightGBM) models were 70%, 76%, 89%, 93%, 95%, 79% and 93%, respectively. The area under the receiving operating characteristic curve values were 0.79 for the LR model, 0.84 for the GNB, 0.95 for the XGB, 0.95 for the GB, 0.99 for the RF model, 0.79 for the DT model and 0.98 for the LightGBM model. The key predictors that were identified were the distance of the lesion from the anal verge, clinical N stage, age, sex, American Society of Anesthesiologists score, and preoperative albumin and carcinoembryonic antigen levels. Integration of artificial intelligence with available preoperative data can potentially predict stoma outcomes after sphincter-saving operations. Our model exhibited excellent predictive ability and can improve the process of obtaining informed consent.

This study aimed to investigate the value of nonenhanced computed tomography (CT)-based radiomics in determining disease progression in breast cancer patients with bone marrow metastases and to develop a model for assessing treatment efficacy. A total of 134 breast cancer patients with bone metastases were enrolled from three hospitals. Nonenhanced CT was performed after two cycles of drug treatment. The images were categorized into an invalid and a valid group according to disease progression status. The largest osteolytic lesions' maximum cross-sections in the CT images were selected as regions of interest (ROIs) for feature extraction. Variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used to reduce feature dimensionality. K-nearest neighbor algorithm (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT) algorithms were trained to establish radiomics models. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic performance of the models. The KNN classifier demonstrated the best performance compared to the random grouping method. In the validation group, the area under the ROC curve (AUC) was 0.810. In the cross-validation method, the RF classifier showed the best performance with an AUC of 0.84. Nonenhanced CT-based radiomics provides a promising method for evaluating the efficacy of systemic drug therapy in breast cancer patients with osteolytic bone metastases.

Energy management of hybrid energy system sources based on machine learning classification algorithms

The development of online transportation applications such as Maxim has increased the need for sentiment analysis to understand user opinions from reviews on the Google Play Store. The main challenges in this analysis are language diversity, variations in writing style, and data imbalance, which affect model accuracy. This study aims to evaluate the performance of the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) algorithms, as well as ensemble approaches through the Voting Classifier and Combined Classifier, in sentiment analysis of Maxim app reviews. The dataset consists of 2,851 Indonesian-language reviews collected through web scraping from the Google Play Store in 2025. Sentiment labels were automatically determined based on user ratings, where ratings of 4–5 were categorized as positive and ratings below 4 as negative, with an initial distribution of 2,295 positive and 556 negative reviews before balancing using SMOTE–Tomek Links. Preprocessing steps included case folding, tokenization, stopword removal, and stemming using Sastrawi, while feature weighting was performed with unigram TF-IDF. The Combined Classifier merged the probability scores from the SVM, KNN, and RF models to produce the final prediction. Evaluation was conducted using 5-Fold Cross Validation with accuracy, precision, recall, F1-score, and ROC-AUC as evaluation metrics. The results show that RF and the Combined Classifier achieved the best performance with 85% accuracy, 87% precision, 85% recall, 86% F1-score, and 0.91 ROC-AUC, while SVM and the Voting Classifier ranked in the middle and KNN ranked the lowest. These findings confirm that ensemble learning, particularly the Combined Classifier, effectively improves the accuracy and stability of review classification compared to individual methods.

Threats to businesses, organizations, and individuals have become more complicated and varied due to the constant evolution of cyberattack strategies brought about by the quick development of Internet technologies. Cross-Site Scripting (XSS) remains one of the most pervasive and dangerous threats among the many that target online application security. This emphasizes the importance of strong detection mechanisms to protect user information and preserve system integrity. Combining machine learning with intrusion detection systems successfully addresses this problem and enhances XSS detection capabilities. This work provides a new feature extraction technique designed specifically for XSS, called Comprehensive Analysis of XSS Features (CAXF). We combine it with Leader Class and Confidence Decision Ensemble (LCCDE), an ensemble machine learning approach. This combination efficiently identifies various XSS attack types, such as reflected, stored, and DOM-based variations. Extensive trials were carried out on well-annotated datasets with typical samples of these XSS categories. The findings demonstrate that the suggested CAXF-LCCDE framework significantly outperforms conventional resilience and detection accuracy models. Furthermore, the results highlight how crucial tailored feature engineering and algorithm selection are for cybersecurity applications. This work provides important information for creating stronger and more dependable XSS attack defenses.