AI content creation has exploded, creating a wave of auto-generated videos, scripts, and shows that compete with traditional programming. Live TV still holds power in news and sports, but audiences ...

Abstract: Traditional k-means clustering is widely used to analyze regional and temporal variations in time series data, such as sea levels. However, its accuracy can be affected by limitations, ...

Abstract: In a multi-source localization system, direction of arrival (DOA) estimation of angles always suffers from errors due to noise interference, sensor position inaccuracies, and other factors.

LinkedIn support accidentally revealed its algorithm: it tracks "viewer tolerance," reducing visibility for authors whose posts are consistently ignored. To succeed, diversify content types weekly, ...

Rocky high steep slopes are among the most dangerous disaster-causing geological bodies in large-scale engineering projects, like water conservancy and hydropower projects, railway tunnels, and metal ...

Accurately identifying fracture zones and their types in strata is of great significance for enhancing oil and gas recovery efficiency. Due to its complicated geological structure and long-term ...

The order would allow people to invest in crypto and private equity. President Donald Trump signed an executive order Thursday that will let Americans invest their 401(k) retirement savings in ...

I think this work might be interesting to the scikit-community. In this work, we discuss 2 classical algorithms for an sampling-based version of k-means, which return an epsilon-approximation of the ...

Amsterdam’s struggles with its welfare fraud algorithm show us the stakes of deploying AI in situations that directly affect human lives. What Amsterdam’s welfare fraud algorithm taught me about fair ...

When it comes to teaching math, a debate has persisted for decades: How, and to what degree, should algorithms be a focus of learning math? The step-by-step procedures are among the most debated ...

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 algorithms to a ...