Using generative AI to design, train, or perform steps within a machine-learning system is risky, argues computer scientist Micheal Lones in a paper appearing in Patterns. Though large language models ...

aNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, ...

The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over ...

A new satellite-based analytical framework enables accurate estimation of crop sowing and emergence dates at the field scale. By integrating daily ...

A new satellite-based analytical framework enables accurate estimation of crop sowing and emergence dates at the field scale.

Methane is the second most important anthropogenic greenhouse gas after carbon dioxide, with a global warming potential roughly 28–34 times greater over a 100-year timescale. Major sources include ...

Abstract: Urban vegetation classification is challenging due to the heterogeneous nature of urban environments. Accurate mapping of urban vegetation, which plays a crucial role in regulating ...

Abstract: The purpose of this study is to estimate and predict onion wholesale price volatility using statistical and machine learning algorithms. Traditional models like ARIMA and GARCH were compared ...

A new study by Justin Grandinetti of the University of North Carolina at Charlotte challenges one of the most dominant narratives in artificial intelligence: that modern AI systems are inherently ...

Ischemic heart failure (IHF) is a major cause of cardiovascular morbidity worldwide, characterized by complex tissue remodeling and inflammation. However, reliable molecular biomarkers for early ...

Objective: This study aimed to develop and evaluate a machine learning (ML)–based algorithm to predict whether an initial vancomycin dose falls within the therapeutic range of the 24-hour area under ...