Abstract: The Gaussian process regression (GPR) model, which is a powerful machine learning tool for probabilistic prediction, is introduced into slope displacement prediction. Using this model, the ...

Abstract: In this brief, we present an accurate and efficient machine learning (ML) approach which predicts variations in key electrical parameters using process variations (PVs) from ultrascaled gate ...

The hydrologic system is subjected unprecedented stresses and increasing demands driven by climate variabilities, landuse changes, groundwater ...

An end-to-end Machine Learning based Credit Card Approval Prediction System that predicts whether a customer's credit card application should be Approved or Rejected based on demographic, career, ...

Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study ...

These codes are associated with the paper "A prediction and behavioural analysis of machine learning methods for modelling travel mode choice", which was published in ...

Neural networks, a fascinating technology inspired by the human brain, form the basis of artificial intelligence. These ...

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

An international research team involving the University of Bayreuth has, for the first time, analyzed the "inner workings" of ...

Image courtesy by QUE.com For decades, the search for room-temperature superconductors has been one of physics' most ...

The Electronics and Telecommunications Research Institute (ETRI) announced Wednesday that a new ITU-R report it developed — ...