How machine learning helps MEMS actuators move in perfect lines

Microelectromechanical systems (MEMS) electrothermal actuators are widely used in applications ranging from micro-optics and microfluidics to nanomaterial testing, thanks to their compact size and ...

Enterprises achieve 50% faster testing cycles with AI-driven autonomous quality assurance

Real-world deployments show 40% test cycle efficiency improvement, 50% faster regression testing, and 36% infrastructure cost savings.
PsyPost on MSN

Scientists use machine learning to control specific brain circuits

A team of researchers in Japan has developed an artificial intelligence tool called YORU that can identify specific animal ...

AI captures particle accelerator behavior to optimize machine performance

Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...
Duke Pratt School of Engineering

Mapping Behavior with Machine Learning

By transforming movement into data, Timothy Dunn is reshaping how scientists can study behavior and the brain.
The Southern Maryland Chronicle on MSN

How are QA teams using machine learning to predict test failures in real time?

QA teams now use machine learning to analyze past test data and code changes to predict which tests will fail before they run. The technology examines patterns from previous test runs, code commits, ...
IEEE

Leveraging Machine Learning Algorithms for Diabetes Prediction

Abstract: This paper analyzes the performance of different LDA combinations with machine learning algorithms in predicting diabetes based on clinical data. The analysis involves patient records with ...
Frontiers

Testing the testing effect on prolific: when retrieval practice fails to boost learning

The testing effect—whereby retrieving information through testing improves long-term retention more than restudying—is one of the most robust and well-documented phenomena in educational psychology.
IEEE

Enhancing Random Forest Using Genetic Algorithm for Lifelong Machine Learning

Abstract: Learning over time for machine learning (ML) models is emerging as a new field, often called continual learning or lifelong Machine learning (LML). Today, deep learning and neural networks ...
pv magazine International

Test tracking algorithms for vehicle-integrated photovoltaics

Researchers at Spain's Technical University of Madrid (UPM) developed a model framework to simulate the performance of vehicle integrated photovoltaic (VIPV) modules under realistic dynamic shading ...
PNAS

Identifying variation in dinosaur footprints and classifying problematic specimens via unbiased unsupervised machine learning

Dinosaur footprints are iconic fossils, but it is challenging to identify their makers. This is illustrated by a long-standing debate about whether some footprints from the Late Triassic-Early ...
Frontiers

MALDI-TOF MS in conjunction with machine learning: toward a new era for antimicrobial susceptibility testing

1 Department of Laboratory Medicine, Center of Infectious Diseases and Pathogen Biology, The First Hospital of Jilin University, Changchun, Jilin, China 2 College of Medical Technology, Beihua ...