A national-scale mineral potential assessment mapped areas across Australia that are prospective for unconformity-related ...

An artificial intelligence (AI) machine-learning model has been developed that can predict the risk of early death in trauma ...

Reports demonstrates that whole-brain Single Photon Emission Computed Tomography (SPECT) imaging, combined with advanced ...

Aims To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous ...

The paper by Sajiki et al 1 gives us a fascinating glimpse of the potential benefits of applying machine learning to large ...

Supervised machine learning improves predictions of compressive strength in industrial waste-modified concrete, supporting ...

Researchers developed a new model to predict the likelihood of critical illness in patients with connective-tissue disease-associated ILD.

Chinese scientists have developed a machine learning-based typhoon rapid intensification forecasting model which has been ...

Explore predictive modeling for compound prioritization, including in silico screening, toxicology models, and lead selection ...

Abstract: Iterative learning control (ILC) has demonstrated effectiveness in urban traffic signal control systems. However, conventional ILC methods typically require infinite iterations to achieve ...

Random forest regression is a tree-based machine learning technique to predict a single numeric value. A random forest is a collection (ensemble) of simple regression decision trees that are trained ...

Abstract: In recent years, Machine Learning (ML) models have been introduced across diverse scientific fields, due to their strong predictive performance. However, in many applications the demand for ...