A research team led by professor Nan-Hee Kim from Korea University's College of Medicine (Nan-Hee Kim and So-Young Park of Department of Endocrinology and Metabolism; Min-Hee Kim and Jae-Young Kim of ...

In this tutorial, we’ll build on the foundation laid in the “Arduino-Based Solar Power System Using Python & Machine Learning, Part 1” project by exploring how to intelligently select and use machine ...

A machine learning model using routine clinical data more accurately predicted 5-year heart failure risk in patients with CKD ...

A research team led by professor Nan-Hee Kim from Korea University's College of Medicine (Nan-Hee Kim and So-Young Park of Department of Endocrinology and Metabolism; Min-Hee Kim and Jae-Young Kim of ...

MASLD is prevalent in T2DM patients, with a 65% occurrence rate, and poses a higher risk for severe liver diseases. The study analyzed 3,836 T2DM patients, identifying key predictors like BMI, ...

Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...

Enhanced prediction capability: Machine learning-based system matches and in some cases outperforms traditional forecasting systems, with particular improvements in northern Europe where conventional ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

A machine learning model that analyzes patient demographics, electronic health record data, and routine blood test results predicted a patient's risk of hepatocellular carcinoma (HCC), the most common ...

Electrical power systems engineers need practical methods for predicting solar output power under varying environmental conditions of a single panel. By integrating an Arduino-based real-time data ...