Clustering-based AI forecasts river water levels using just a few long records

Reliable and scalable water level prediction is crucial in hydrology for effective water resources management, especially ...
IEEE

Learning-Based Model Predictive Control With High-Probability Safety Using Gaussian Mixture Models

Abstract: This article introduces a learning-based model predictive control (MPC) framework that leverages Gaussian mixture models (GMMs) to address dynamic system uncertainties effectively. To ...
Scientific Research Publishing

Rasmussen, C.E. and Williams, C.K.I. (2005) Gaussian Processes for Machine Learning. The MIT Press.

ABSTRACT: This paper introduces a method to develop a common model based on machine learning (ML) that predicts the mechanical behavior of a family with three composite materials. The latter are ...
Scientific Research Publishing

Explainable Machine Learning in Risk Management: Balancing Accuracy and Interpretability ()

1 School of Taxation and Public Administration, Shanghai Lixin University of Accounting and Finance, Shanghai, China. 2 School of Business, Computing and Social Sciences, University of Gloucestershire ...
Phys.org

Gaussian processes provide a new path toward quantum machine learning

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...
Hosted on MSN

Logistic Regression Machine Learning Example ¦ Simply Explained

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...
PNAS

Physics-tailored machine learning reveals unexpected physics in dusty plasmas

Dusty plasma, a mixture of ions, electrons, and charged dust particles, is common throughout the universe. Understanding and modeling dusty plasma require precise knowledge of the complex interactions ...