In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...

A research team shows that phenomic prediction, which integrates full multispectral and thermal information rather than ...

A research paper by scientists from Beihang University proposed a machine learning (ML)-driven cerebral blood flow (CBF) prediction model, featuring multimodal imaging data integration and an ...

From a governance perspective, the use of explainable AI is particularly significant. Infrastructure decisions involve public ...

Background Falls can repeatedly occur as people age, which leads to injury, disability and mortality in older adults. Sleep ...

A new AI developed at Duke University can uncover simple, readable rules behind extremely complex systems. It studies how ...

A research team at Duke University has developed a new AI framework that can uncover simple, understandable rules that govern ...

This new AI acts like a digital scientist, turning messy data into simple rules that explain how the world really works.

Abstract: State estimation for nonlinear models has been a longstanding challenge in the field of signal processing. Classical nonlinear filters, such as the extended Kalman filter (EKF), unscented ...

This document provides a detailed explanation of the MATLAB code that demonstrates the application of the Koopman operator theory for controlling a nonlinear system using Model Predictive Control (MPC ...

Abstract: This paper presents a novel approach to practical nonlinear model predictive control (PNMPC) using Kolmogorov–Arnold networks (KANs) as prediction models. KANs are based on the ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In this research work authors have experimentally validated a blend of Machine ...