Beyond achieving technical excellence, the study underscores the practical utility of explainable AI in flood risk management ...

Previous methods struggle to incorporate real-time data or account for nonlinear interactions among macroeconomic variables. This leaves a critical gap in anticipating fiscal stress events. Machine ...

The strategic advantage of QML continues to expand its presence in industries that deal with complex, high-dimensional data.

To delineate the metastatic microenvironment,, researchers in China have developed an explainable machine learning model that identifies ... To build this model, the researchers employed XGBoost, a ...

Babies born with low birth weight (less than 2.5 kg) are 20 times more likely to die. They are also more likely to develop neurological and cardiovascular diseases, diabetes, and growth problems later ...

So now, suddenly, when faced with terms like “hyperparameter tuning” and “unsupervised learning,” a 50-year-old brain initiates its own version of a denial-of-service attack in these courses.

The increasing prevalence of myopia is a global health concern, with high myopia increasing the risk of vision damage. This necessitates the use of ...

E-Vega Mobility Labs has developed EV Doctor, a compact, AI-powered device that diagnoses EV battery health in just 15 ...

When someone starts a new job, early training may involve shadowing a more experienced worker and observing what they do ...

Traffic classification (TC) is a fundamental task of network management and monitoring operations. Previous works relying on selected packet header fields (e.g. port numbers) or application layer ...

Some types of predictive analytics software use machine learning to revise algorithms based on learnings from the data collected over time, continuously improving prediction accuracy.