IEEE

Predicting the Compressive Strength of Recycled Concrete Using Ensemble Learning Model

Abstract: This research proposes a stacking machine learning method to accurately predict the compressive strength of recycled concrete. The model integrates eXtreme Gradient Boosting (XGBoost), Extra ...
IEEE

Implementing Calorie Burnt Prediction Through XGBOOST Algorithm Compared Over Random Forest Regressor

Abstract: This research aims to evaluate the XGBoost algorithm and the Lasso Regression approach for their respective abilities to forecast caloric expenditure during physical activity. Tools and ...
GitHub

Drift Detection in Gas Sensor Array at Different Concentration Levels ☢️

Sensor calibration is the process of adjusting a sensor's output to ensure it accurately reflects the true value of what it measures. By aligning sensor readings with known standards or reference ...
Scientific Research Publishing

Miyazaki, Y., Kawakami, M., Kondo, K., Hirabe, A., Kamimoto, T., Akimoto, T., et al. (2024) Logistic Regression Analysis and Machine Learning for Predicting Post-Stroke Gait ...

ABSTRACT: Postoperative nausea and vomiting (PONV) is a common complication after anesthesia and surgery. Traditional predictive models, such as Apfel scores, rely on linear assumptions and limited ...
Frontiers

Hyperspectral inversion model of ginkgo leaf yield prediction based on machine learning

Introduction: The yield of ginkgo biloba leaves serves as a critical indicator for assessing their growth and health status. However, current assessment methods primarily rely on manual harvesting and ...
Frontiers

Comparison of multiple machine learning methods for predicting postoperative hyperglycemia in patients without diabetes undergoing cardiac surgery

Background: Stress-induced hyperglycemia (SHG) represents a significant metabolic complication in non-diabetic cardiac surgery older adult patients, with substantial implications for postoperative ...