A Microsoft analog optical computer has solved two optimization problems and shown potential for AI workloads using less energy.

Demographic bias gaps are closing in face recognition, but how training images are sourced is becoming the field’s biggest privacy fight.

Abstract: The performance of k-means clustering algorithm depends on the selection of distance metrics. The Euclid distance is commonly chosen as the similarity measure in k-means clustering algorithm ...

ABSTRACT: Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering ...

Right now, quantum computers are small and error-prone compared to where they'll likely be in a few years. Even within those limitations, however, there have been regular claims that the hardware can ...

Abstract: The k-means method is widely utilized for clustering. Its simplicity, efficacy, and swiftness make it a favored choice among clustering algorithms. It faces the challenge of sensitivity to ...

1 Intelligent Equipment Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China 2 Information Technology Research Center, Beijing Academy of Agriculture and ...

The year 2024 is the time when most manual things are being automated with the assistance of Machine Learning algorithms. You’d be surprised at the growing number of ML algorithms that help play chess ...

ABSTRACT: Domaining is a crucial process in geostatistics, particularly when significant spatial variations are observed within a site, as these variations can significantly affect the outcomes of ...