Final random-forest-based models outperformed all publicly available risk scores on internal and external test sets.

HFpEF in hypertrophic cardiomyopathy predicts adverse outcomes. Discover how machine learning improves risk assessment.

The results show that the Decision Tree model emerged as the top-performing algorithm, achieving an accuracy rate of 99.36 percent. Random Forest followed closely with 99.27 percent accuracy, while ...

Afforestation—establishing forests on previously non-forested land, or where forests have not existed for a long time—is one ...

Read more about AI can’t deliver climate gains without strong governance and capacity building on Devdiscourse ...

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 ...

Scientists at the European Centre for Medium-Range Weather Forecasts have unveiled a machine learning technique that pinpoints optimal locations for tree planting, offering a powerful tool for climate ...

A new study has shown that biochar, a carbon-rich material produced from biomass, can significantly reduce phosphorus losses ...

Florida's Indian River Lagoon has been an ecosystem in decline going back to 2011, when harmful algal blooms led to a severe ...

Zoonova AI today announced the launch of Alpha AI, a new investing platform designed to make advanced market intelligence more accessible through a natural-language AI Command Center. Alpha AI ...