Researchers used data from 72 BMW i3 trips and a lizard-inspired algorithm to improve predictions of EV battery charge and ...

Researchers have developed a meta-learning framework that recommends the best clustering algorithm for generating ...

A new explainable machine learning framework combining EEG signals with clinical data distinguishes Alzheimer's disease from ...

Stochastic gradient descent and Adam are optimization algorithms that update model parameters from estimated gradients, but ...

Researchers have developed the first known machine-learning-powered tool that can predict patient risk of hundreds of diseases based solely on patient health records and genetic profiles. The model ...

The current MA risk adjustment model has shortcomings, both in predictive accuracy and payment equity across the Medicare program, which could be mitigated using lessons from machine learning. MA ...

AdaBoost.R2 regression is a machine learning technique used to predict a single numeric value. AdaBoost.R2 builds a sequence of decision tree regressors where each accepted tree improves prediction ...

Background Machine learning (ML) could improve clinical decisions in patients with possible acute heart failure, but few studies have evaluated acceptance, and barriers or facilitators that lead to ...

Machine learning acts as a bridge between raw data and action, helping organizations turn patterns in data into predictions, automation and more informed decisions. Different machine learning methods ...