Tag: uncertainty quantification

A new hybrid 3D CNN-graph attention framework with entropy-guided fusion achieved an AUROC of 0.964 and delineated five new exploration ...

Researchers have released evidencelib, an open-source Python library that unifies Dempster–Shafer and Dezert–Smarandache evidence modeling, high-conflict fusion rules, and decision ...

Researchers have developed a unified scoring method that combines multiple accuracy metrics with model confidence to provide a more clinically ...

A new vertically augmented three-dimensional Empirical Bayesian Kriging framework reconstructs subsurface soil conditions with dramatically reduced error, cutting predictive error ...

Researchers have combined Bayesian physics-informed neural networks with Stein variational gradient descent to deliver fast, probabilistic forecasts of contaminant transport ...

A new digital twin framework uses ensemble forecasting and a fast, explainable confidence index to defer non-critical water maintenance during ...

Researchers have combined quantum-mechanical simulations, neural networks, and Bayesian statistics to cut the voltage prediction error of a physics-based lithium-ion ...

A new label-scarce graph AI framework reliably detects deep, subtle mineralization anomalies at the Kalatongke copper-nickel deposit using only 906 ...

A locked uncertainty-aware AI triage model safely automated about 37 percent of cervical lymph node ultrasound cases while deferring the ...

Researchers have developed UbiQVision, a framework that fuses explainable AI attributions from deep learning ensembles using Dempster–Shafer evidence theory to ...