Researchers have developed a diagnostic panel that identifies cognitive decline by analyzing how blood proteins fold. This ...

Support vector machines improve classification by mapping inseparable signals into higher-dimensional spaces. Random forest models, through ensemble decision trees, increase robustness against ...

Published as an arXiv preprint, the paper details how unsupervised and self-supervised AI models are matching or surpassing supervised systems while uncovering biological patterns that traditional ...

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 ...

Last week, Nikkei Asia reported that researchers at Sony Group were working on technology to identify copyrighted music embedded in AI-generated tracks. The story was widely picke ...

Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.

The automatic detection of surface-level irregularities—defects or anomalies—in 3D data is of significant interest for ...

Researchers have developed a new artificial intelligence-based approach for detecting fatty deposits inside coronary arteries using optical coherence tomography (OCT) images. Because these lipid-rich ...

Researchers at the University of Bayreuth have developed a method using artificial intelligence that can significantly speed up the calculation of liquid properties. The AI approach predicts the ...

To evaluate the diagnostic performance of semi-supervised learning models for aggressive prostate cancer detection on MRI compared to fully supervised models trained with additional expert annotations ...