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

An XGBOOST predictive model of void ratio in sandy soils with shear-wave velocity as major input

The customer review is always beneficial for a company for making its product at the next level. There are thousands of products and their reviews which are impossible to analysis manually. Perspective analysis or sentiment analysis is an automated process which retrieves subjective opinion from the review and categorized as positive and negative. Through which an organization can apprehend that where their invention goes on. Living life with uncomfortable looks has a negative impact on our daily life. So, day by day increasing the use of anti-aging products is appreciable. Major portion of such data are available on Web sites like twitter, Facebook, Linked-In and also from e-commerce sites like Amazon, Flip-kart, and various types of blogs. Hence, this research paper aims to guide a sentimental analysis approach to customer reviews of anti-aging products from Amazon. Classify each review as an affirmative review; otherwise, neglect review by adopting several machine learning strategies. This research builds a model using supervised and ensemble machine learning approaches. The method of ensemble machine learning approach applied in this analysis is voting which altogether seven different classifiers: Gaussian Naive Bayes, logistic regression, random forest, bagging using decision tree algorithm, and boosting using extreme gradient boosting (XGB), SVM, k-nearest neighbor algorithm and give output according to best accuracy. The data have also been analyzed using text blob and VADER and accuracy score also compares with this model. The experiment in this research done using Python and NLTK and have created different scenarios for the evaluation of proposed methodology against the classifiers. The scenarios in feature extraction steps such as by using different types of N-grams and analysis the accuracy for each and every classifier. The result which achieved the highest accuracy in logistic regression for using unigram but for some scenario, voting classifier also gives an optimum output.KeywordsSentiment analysisEnsemble machine learningGaussian Naive BayesBaggingLogistic regressionBoostingk-nearest neighbor

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.

The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices with little to no human intervention. IoT enables data aggregation and analysis on a large scale to improve life quality in many domains. In particular, data collected by IoT contain a tremendous amount of information for anomaly detection. The heterogeneous nature of IoT is both a challenge and an opportunity for cybersecurity. Traditional approaches in cybersecurity monitoring often require different kinds of data pre-processing and handling for various data types, which might be problematic for datasets that contain heterogeneous features. However, heterogeneous types of network devices can often capture a more diverse set of signals than a single type of device readings, which is particularly useful for anomaly detection. In this paper, we present a comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomaly detection. Rather than using one single machine learning model, ensemble learning combines the predictive power from multiple models, enhancing their predictive accuracy in heterogeneous datasets rather than using one single machine learning model. We propose a unified framework with ensemble learning that utilises Bayesian hyperparameter optimisation to adapt to a network environment that contains multiple IoT sensor readings. Experimentally, we illustrate their high predictive power when compared to traditional 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.

Background and objectiveThe increasing global prevalence of diabetes has led to a surge in complications, significantly burdening healthcare systems and affecting patient quality of life. Early prediction of these complications is critical for timely intervention, yet existing models often rely heavily on clinical indicators while underutilizing fundamental laboratory test parameters. This study aims to bridge this gap by leveraging the 12 most frequently tested laboratory indicators in diabetic patients to develop an optimized predictive model for diabetes complications.MethodsA comprehensive dataset was established through meticulous data collection from a high-volume tertiary hospital, followed by extensive data cleaning and classification. Various machine learning classifiers, including Random Forest, XGBoost, Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained on this dataset to evaluate their predictive performance. We further introduced an ensemble learning model with Bayesian optimization to enhance accuracy and cost-efficiency. Additionally, feature importance analysis was conducted to refine the model by reducing testing costs while maintaining high predictive accuracy.ResultsOur ensemble model with Bayesian optimization demonstrated superior performance, achieving over 90% accuracy in predicting various diabetic complications, with an outstanding 98.50% accuracy and 99.76% AUC for diabetic nephropathy. Feature correlation analysis enabled a refined model that not only improved predictive accuracy but also reduced overall medical costs by 2.5% through strategic feature elimination.ConclusionsThis study makes three key contributions: (1) Development of a high-quality dataset based on fundamental laboratory indicators, (2) Creation of a highly accurate predictive model using ensemble learning and Bayesian optimization, particularly excelling in diabetic nephropathy prediction, and (3) Implementation of a cost-efficient diagnostic approach that reduces testing expenses without compromising accuracy. The proposed model provides a strong foundation for future research and practical clinical applications, demonstrating the potential of integrating machine learning with cost-conscious medical testing.

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.

The dynamic behavior of sand samples in earthquake-prone regions must be thoroughly characterized and incorporated into building design processes. Seismic events induce significant changes in the stress-strain behavior and strength characteristics of soils, which in turn lead to ground deformations and potential structural damage. Therefore, it is essential to determine key dynamic soil properties such as the maximum shear modulus, the normalized shear modulus reduction curve, and the damping ratio curve, which are representative of the site conditions. However, the laboratory-based determination of the maximum shear modulus is both time-consuming and costly. To address this limitation, empirical models have been developed to provide practical alternatives for researchers. In this study, a series of resonant column tests were conducted on silty sand samples collected from a site located along the North Anatolian Fault Zone (NAFZ), one of the most active fault systems in Turkey. The tests were performed under varying confining pressures, void ratios, and water contents. Based on the experimental results, an empirical model with high predictive capability is proposed for engineering applications.

In recent decades, various software effort estimation (SEE) algorithms have been suggested. Unfortunately, generating high-precision accuracy is still a major challenge in the context of SEE. The use of traditional techniques and parametric approaches is largely inaccurate because they produce biased and subjective accuracy. Meanwhile, none of the machine learning methods performed well. This study applies the AdaBoost ensemble learning method and random forest (RF), on the other hand the Bayesian optimization method is applied to determine the hyperparameters of this model. The PROMISE repository and the ISBSG dataset were used to build the SEE model. The developed model was comprehensively compared with four machine learning methods (classification and regression tree, k-nearest neighbor, multilayer perceptron, and support vector regression) under 3-fold cross validation (CV). It can be seen that the RF method based on AdaBoost ensemble learning and bayesian optimization outperforms this approach. In addition, the AdaBoost-based model assigns a feature importance rating, which makes it a promising tool in software effort prediction.

The influence of vegetation cover on the spectroscopic estimation of soil properties

Against the backdrop of booming engineering construction in loess areas, achieving rapid and accurate prediction of the loess collapsibility coefficient is of great significance. This study takes the loess in the Longdong area as the research object. Based on analyzing the correlation between the collapsibility coefficient and various soil property indices, a rapid determination standard for the collapsibility of loess in this area is proposed. Prediction models between the loess collapsibility coefficient and void ratio, depth, saturation, and compression coefficient are established using three machine learning algorithms: random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM). The prediction results are subjected to interpretability analysis using the Shapley additive explanations (SHAP) technique. The research results show that loess with a void ratio less than 0.960 and a soil layer depth greater than 16m can be used as the rapid determination standard for non-collapsible loess in this area. The XGBoost model has higher prediction accuracy and applicability, with coefficients of determination (R2) of 0.910 and 0.900 for the training set and testing set, respectively. The prediction accuracy for identifying whether loess is collapsible or nocollapsible reaches 92.7%. SHAP analysis reveals that the influence trends of various factors on model predictions are consistent with theoretical cognition, and soils with a depth less than 11m and a void ratio greater than 1.02 have a high collapsibility tendency. The results and conclusions of this study can provide a theoretical basis for subsequent engineering construction in the Longdong area.

Dynamic properties, such as shear modulus and damping ratio, are critical for civil engineering applications and essential for accurate dynamic response analysis. This study introduces a novel Deep Neural Network (DNN) approach to predict the normalized shear modulus (G/Gmax ) and damping ratio (D) of granular soils over a wide strain range. Utilising a comprehensive dataset from cyclic triaxial (CT) and resonant column (RC) tests, we developed a Deep Feed-Forward Neural Network (DFFNN) model. The model incorporates grading characteristics, shear strain, void ratio, mean effective confining pressure, consolidation stress ratio, and specimen preparation method as inputs. The DFFNN demonstrated high accuracy with testing results of 0.9830 for G/Gmax and 0.9396 for D, outperforming traditional empirical models and other intelligent techniques such as Shallow Neural Network (SNN), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR). This data-driven approach offers a robust and adaptable method for predicting the dynamic properties of granular soils across diverse conditions.

In high-performance computing job scheduling systems, to accurate predict the job runtime can effectively utilize the idle resource fragments generated during cluster computing as well as backfill them for improving scheduling performance. Because the runtime of high-performance computing jobs are affected by many factors thus are complicated non-linear problem. The ensemble machine learning method is used to predict the runtime of jobs in cluster computing. By comparing the prediction results of different models on the job log data sets from three real high-performance computing systems, it is found that the LightGBM algorithm has higher prediction accuracy, faster computation speed, shorter training time of the model and achieve better overall performance.

Protein toxins are defense mechanisms and adaptations found in various organisms and microorganisms, and their use in scientific research as therapeutic candidates is gaining relevance due to their effectiveness and specificity against cellular targets. However, discovering these toxins is time-consuming and expensive. In silico tools, particularly those based on machine learning and deep learning, have emerged as valuable resources to address this challenge. Existing tools primarily focus on binary classification, determining whether a protein is a toxin or not, and occasionally identifying specific types of toxins. For the first time, we propose a novel approach capable of classifying protein toxins into 27 distinct categories based on their mode of action within cells. To accomplish this, we assessed multiple machine learning techniques and found that an ensemble model incorporating the Light Gradient Boosting Machine and Quadratic Discriminant Analysis algorithms exhibited the best performance. During the tenfold cross-validation on the training dataset, our model exhibited notable metrics: 0.840 accuracy, 0.827 F1 score, 0.836 precision, 0.840 sensitivity, and 0.989 AUC. In the testing stage, using an independent dataset, the model achieved 0.846 accuracy, 0.838 F1 score, 0.847 precision, 0.849 sensitivity, and 0.991 AUC. These results present a powerful next-generation tool called MultiToxPred 1.0, accessible through a web application. We believe that MultiToxPred 1.0 has the potential to become an indispensable resource for researchers, facilitating the efficient identification of protein toxins. By leveraging this tool, scientists can accelerate their search for these toxins and advance their understanding of their therapeutic potential.

Epigenetic clocks estimate biological age from DNA methylation patterns at CpG sites, providing robust predictions of mortality and morbidity risk. “Blue zones”—regions of exceptional longevity—offer a unique opportunity to investigate how biological aging diverges from chronological age. However, standard clocks are typically trained on large, heterogeneous datasets, reflecting average population trends rather than region-specific dynamics. Using data from the Costa Rican Longevity and Healthy Aging Study (CRELES), we profiled DNA methylation from residents of the Nicoya blue zone (n = 206) and a comparison population in other parts of Costa Rica (n = 875). We propose training a SuperLearner, an ensemble machine learning approach, on the non-Nicoyan Costa Ricans to optimize predictive performance across existing clocks and flexible machine learners. Theoretically justified by its Oracle property, SuperLearner performs asymptotically as well as the best candidate predictor in the ensemble, resulting in a weighted combination of algorithms used to predict age. We then used this trained model to construct a calibrated hypothesis test comparing residual age distributions between the blue zone region and the comparison population. Comparing our approach to the five top-performing epigenetic clocks (ranked by MSE) in the Costa Rican cohort, only SuperLearner suggested age deceleration (an average of ~ 1 year) in the non-Nicoyan reference group. Before calibration, SuperLearner showed the strongest evidence for slowed biological aging among blue zone Nicoyans, estimating a three-year reduction ( = −3.05, 95% CI [−3.64, −2.46]) in epigenetic age. Calibrating with non-Nicoyan Costa Ricans improved consistency between estimates in all clocks, decreasing the estimated aging advantage in Nicoyans to about two years ( = −1.96, 95% CI [−2.56, −1.37]). This approach provides a robust framework for estimating longevity in distinct regions when a relevant comparison population is available.