Nature

Efficacy of automated machine learning models and feature engineering for diagnosis of equivocal appendicitis using clinical and computed tomography findings

This study evaluates the diagnostic efficacy of automated machine learning (AutoGluon) with automated feature engineering and selection (autofeat), focusing on clinical manifestations, and a model ...
Nature

Correlation based feature importance analysis for improving machine learning stability predictions in hybrid PV systems

The electric power grid is undergoing a paradigm shift to intelligent and digitally controlled systems rather than the conventional electromechanical systems. The advanced grid infrastructures are ...
Phys.org

Using geometry and physics to explain feature learning in deep neural networks

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...
AZoBuild

Machine Learning Improves Strength Predictions in Concrete

Supervised machine learning improves predictions of compressive strength in industrial waste-modified concrete, supporting ...
Plastics Technology

Artificial Intelligence Approaches Expedite Engineering Workflows

Machine learning models narrow down solutions in synthesis, compounding, product design, and more.
mccormick.northwestern.edu

New Machine Learning Framework Enables Data-Efficient Design of Advanced Metamaterials

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...
mccormick.northwestern.edu

MECH_ENG 395: Machine Learning for Mechanical Sciences

Mechanical engineering has traditionally relied on physics, mathematics, and empirical knowledge to design and optimize systems. Machine learning (ML) introduces powerful tools that can complement ...