Researchers have shown how random forest algorithms can be applied to complex ecological models to uncover the mechanisms driving system behavior. By analyzing a stage‑structured consumer‑resource ...

Combining machine learning and feature selection, this research accurately predicts aluminum levels in marine environments, ...

Researchers at Bangladesh Agricultural University (BAU) have developed an advanced Artificial Intelligence (AI)-based model capable of providing early and accurate flood forecasts, offering a major ...

Afforestation—establishing forests on previously non-forested land, or where forests have not existed for a long time—is one of the nature-based and cost-effective solutions for climate change ...

Artificial intelligence (AI) is emerging as a powerful tool to predict food consumption patterns and guide policy decisions, ...

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 ...

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

Soil acidification is one of the pressing issues confronting global farmland today. Studies indicate that approximately 40% ...

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

Missing data can derail even the most promising analysis, but modern imputation techniques are transforming this challenge into a solvable problem. From straightforward substitutions to advanced ...

Researchers conducted a systematic review to assess the risk of bias and applicability of prediction models for fear of recurrence in patients with cancer.

Discover how explainable AI enhances Parkinson’s disease prediction with improved accuracy and clinical interpretability.