A machine learning-driven framework accurately predicts MPA exposure and supports individualized dosing in childhood-onset LN.

Accurate assessment of soil salinity is critical for sustainable agriculture and food security, yet remains technically challenging at fine spatial scales.

A study published in The Journal of Engineering Research at Sultan Qaboos University presents an advanced intrusion detection system (IDS) designed to improve the accuracy and efficiency of ...

By transforming everyday smartphone signals into high-resolution mobility data, researchers have reconstructed how residents of Cuenca travel across the city and what those patterns mean for energy ...

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...

Abstract: Automated embryo quality prediction remains a critical challenge in Assisted Reproductive Technology (ART) due to the subjective nature of manual grading and the lack of methods that ...

Figure 1: LASSO feature ranking and SHAP explanatory for Cases 1, 2, and 3 feature selection models. A positive SHAP value indicates a positive impact on prediction, leading the model to predict 1 ...

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...