Agentic AI is “opening up a lot of really exciting possibilities” for engineers, MathWorks’s Seth DeLand tells Design News. As a product marketing manager at MathWorks, DeLand works with MATLAB ...

Increases in computing power and the availability of more data through social media and crowdsourcing have facilitated the use of machine-learning in psychological research. Machine learning has been ...

Earth observation machine learning pipelines differ fundamentally from standard computer vision workflows. Imagery is typically delivered as large, georeferenced scenes, labels may be raster masks or ...

In this tutorial, we explore how to harness Apache Spark’s techniques using PySpark directly in Google Colab. We begin by setting up a local Spark session, then progressively move through ...

Automated Machine Learning (AutoML) aims to streamline the end-to-end process of ML models, yet current approaches remain constrained by rigid rule-based frameworks and structured input requirements ...

ABSTRACT: We consider various tasks of recognizing properties of DRSs (Decision Rule Systems) in this paper. As solution algorithms, DDTs (Deterministic Decision Trees) and NDTs (Nondeterministic ...

Abstract: WSNs have demonstrated to play a key role in many applications ranging from environmental monitoring to military surveillance. The problem of efficient routing however is challenging due to ...

The success of a molecular dynamics simulation depends on the accuracy of the force field used to define the atomic interactions. It is challenging to train both classical and modern machine-learning ...