Water is essential to human living; hence, maintaining its purity is vital to health. This work seeks to find the best algorithm for the statistically imputed dataset and the most accurate drinking ...

One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing. An experimental study shows that already ...

SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes ...

Machine learning models, including XGBoost, Random Forest, Support Vector Machine, and k-Nearest Neighbors, were trained for ED risk prediction. Key predictors included advanced age, smoking history, ...

Researchers have developed a new machine learning algorithm that excels at interpreting optical spectra, potentially enabling faster and more precise medical diagnoses and sample analysis.

Prediction of Patients With High-Risk Osteosarcoma on the Basis of XGBoost Algorithm Using Transcriptome and Methylation Data From SGH-OS Cohort. If you have the appropriate software installed, you ...

Methodology This study employs a machine learning approach to predict Autism Spectrum Disorder (ASD) using the XGBoost algorithm, a gradient-boosting framework optimised for structured data ...

The objective of this paper to develop machine learning models—Random Forest (RF) and eXtreme Gradient Boosting (XGBoost)—to predict pavement Surface Curvature Index (SCI), a key indicator of pavement ...