Motivation and objective: Emergency medicine is becoming a popular application area for
artificial intelligence methods but remains less investigated than other healthcare branches …

Background Neglected tropical diseases (NTDs) primarily affect the poorest populations,
often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a …

As climate-driven risks for the world's coastlines increase, understanding and predicting
morphological changes as well as developing efficient systems for coastal forecast has …

In data science and content-based retrieval, we find many domain-specific techniques that
employ a data processing pipeline with two fundamental steps. First, data entities are …

Background Recent research has demonstrated that machine learning (ML) has the
potential to improve several aspects of medical application for critical illness, including …

During Basic screening, it is challenging, if not impossible to detect breast cancer especially
in the earliest stage of tumor development. However, measuring the electrical impedance of …

Alzheimer's disease, characterized by progressive and irreversible deterioration of cognitive
functions, represents a significant health concern, particularly among older adults, as it …

Predicting car ownership patterns at high spatial resolution is key to understanding
pathways for decarbonisation—via electrification and demand reduction—of the private …

Background: Timely diagnosis of neonatal sepsis is challenging. We aimed to systematically
evaluate the diagnostic performance of sophisticated machine learning (ML) techniques for …

H2AX is a histone H2A variant that becomes phosphorylated upon genotoxic stress. The
phosphorylated H2AX (γ‐H2AX) plays an antioncogenic role in the DNA damage response …