Fuzzy regression models extend traditional statistical regression by integrating fuzzy set theory to better handle imprecision and uncertainty inherent in many real-world data sets. These models ...

Built on a new architecture KumoRFM-2 achieves state-of-the-art results across 41 predictive tasks and four major benchmarks, ...

What was the rationale behind applying machine learning (ML) models to improve identification probability in the absence of ...

A new study published in Genome Research presents an interpretable artificial intelligence framework that improves both the accuracy and transparency of genomic prediction, a key challenge in fields ...

Forecasting inflation has become a major challenge for central banks since 2020, due to supply chain disruptions and economic uncertainty post-pandemic. Machine learning models can improve forecasting ...

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 ...

Researchers at The University of Manchester have built a machine-learning model that prevents simulated molecules from flying ...

A machine-learning model trained on fewer than 300 molecules has flagged diatomic pairs with record-high electric dipole moments, several of them in combinations that chemists had not seriously ...

The increasing global demand for sustainable energy and carbon materials, alongside pressing environmental concerns, ...