Semi-supervised learning merges supervised and unsupervised methods, enhancing data analysis. This approach uses less labeled data, making it cost-effective yet precise in pattern recognition.

Large language models are machine learning models designed for a range of language-related tasks such as text generation and ...

This review shows that machine learning enables early disease diagnosis, automates selective tea-bud harvesting, evaluates ...

This important study describes a deep learning framework that analyzes single-cell RNA data to identify a tumor-agnostic gene signature associated with brain metastases. The identified signature ...

Public health officials have long struggled to see outbreaks coming before hospitals fill up. Now a new generation of machine ...

Periodic maintenance is common too, but still inefficient and often based on time, not actual machine condition. That ...

The varied topography of the Western United States—a patchwork of valleys and mountains, basins and plateaus—results in minutely localized weather. Accordingly, snowfall forecasts for the mountain ...

A new workshop report titled “Artificial Intelligence with Open and Scaled Data Sharing in Semiconductor Manufacturing” was ...

Supervised machine learning uses labeled data to teach algorithms pattern recognition. It improves prediction accuracy in industries like finance and healthcare. Investors can gauge a company's ...