Gaussian processes (GPs) have attracted considerable attention in assisting evolutionary algorithms (EAs) to solve computationally expensive optimization problems (EOPs) because they can directly ...

An artificial-intelligence algorithm that discovers its own way to learn achieves state-of-the-art performance, including on some tasks it had never encountered before. Joel Lehman is at Lila Sciences ...

A new machine learning model developed by The George Institute for Global Health can successfully predict heart disease risk in women by analyzing mammograms. The findings were published today in ...

What would you like to Propose? I propose adding flowcharts for selected algorithms to make it easier for beginners to understand the logic visually. Flowcharts will complement the existing code ...

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 ...

SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes means ...

Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...

ABSTRACT: Road traffic accidents are one of the global safety and socioeconomic challenges. According to WHO (2024), it has caused over 1.19 million annual fatalities. It is also projected to cause ...

Adaptive algorithms have immensely advanced, becoming integral for innovation across multiple industries. These intelligent systems adjust content and strategies to improve the experiences of users by ...

The emergence of using Machine Learning Techniques in software testing started in the 2000s with the rise of Model-Based Testing and early bug prediction models trained on historical defect data. It ...