pv magazine International

AI now can now predict solar cell efficiency from wafer quality

Korea University researchers have developed a machine-learning framework that predicts solar cell efficiency from wafer quality, enabling early wafer screening and optimized production paths. Using ...

Machine learning helps solve a central problem of quantum chemistry

By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made ...

WBA Publishes Industry First Guidance on Artificial Intelligence and Machine Learning for Intelligent Wi-Fi

New Wireless Broadband Alliance report lays out the frameworks and priorities needed to scale intelligent Wi-Fi without ...
The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Fast AI for satellite learns to quantify uncertainty

Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of ...

First-ever black hole image sharpened using machine learning

The historic first image of the Messier 87 (M87) supermassive black hole, captured using the Event Horizon Telescope, has ...

Orbiit Recovery Ecosystem Advances the Use of AI and Machine Learning in Addiction Recovery

Behavior-Derived Intelligence Transforms How Recovery Is Supported, Measured, and Sustained Human behavior leaves a ...
Tech Xplore

Smarter machine-learning models can improve phishing website detection

Phishing websites remain a persistent cybersecurity threat, exploiting users by imitating trusted online services. New ...
TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and data preprocessing. If you''ve ever built a predictive model, worked on a ...
Physics World

Transfer learning could help muon tomography identify illicit nuclear materials

Muons tend to scatter more from high-atomic-number materials, so the technique is particularly sensitive to the presence of materials such as uranium. As a result, it has been used to create systems ...