Machine Learning Model Predicts Residual Strength of Aged Silk Heritage Without Damage
Researchers trained an XGBoost machine learning model to estimate the residual tensile strength of artificially aged silk textiles from nondestructive ...
Researchers trained an XGBoost machine learning model to estimate the residual tensile strength of artificially aged silk textiles from nondestructive ...
Hourly smart meter readings can now reveal far more than a building's total electricity consumption. Researchers have developed COF-Tool, an ...
An unsupervised machine learning analysis of 452,020 Colombian Saber Pro exam records identified eight distinct student profiles spanning a 69-point ...
Researchers in Cameroon built and tested a machine learning model that predicts antiretroviral resistance from routine clinical data, with XGBoost ...
Researchers constructed a monthly Manufacturing Economic Policy Uncertainty index for the United States from 1985 to 2025 using machine learning ...
A new GeoAI framework combining Google Earth Engine, ensemble machine learning, and SHAP explainability mapped landslide susceptibility across central Nepal ...
A narrative review finds AI models across nephrology achieve strong predictive performance, yet prospective validation and evidence of patient benefit ...
A comparative study of seven uncertainty-aware machine learning frameworks on real-world cargo EV data shows trade-offs between prediction accuracy, interval ...
A condition-stratified study of westbound Interstate I-94 found that XGBoost without weather variables delivered the most accurate hour-ahead traffic forecasts.
A wrist-worn motion sensor and a compact neural network detected prosthetic hand contact with 99.1 percent accuracy in lab tests.
© 2025 Scienmag - Science Magazine
© 2025 Scienmag - Science Magazine