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

As humans, our eyes take in two-dimensional images that our brains convert to three-dimensional experiences. This ability enables us to be aware of our position in space, judge distances, possess ...

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 ...

Statisticians from across Europe teamed up to train a competition-predicting, machine learning algorithm. This is what they found.

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 ...

The lack of precise, autonomous tools for monitoring and classifying cattle behavior limits farmers’ ability to make proactive and informed decisions regarding grazing and herd management. Currently, ...

Abstract: Quadrature Amplitude Modulation (QAM) is widely used in modern wireless communication systems to transmit data efficiently. Conventional QAM transceivers rely on specialized hardware to ...

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Individual prediction uncertainty is a key aspect of clinical prediction model performance; however ...