The study addresses heterogeneous UAV cooperative task assignment under complex constraints via an energy learning ...

Recent survey delivers the first systematic benchmark of TSP solvers spanning end-to-end deep learners, hybrid methods and ...

In recent years, machine learning (ML) algorithms have proved themselves to be remarkably useful in helping people deal with different tasks: data classification and clustering, pattern revealing, ...

Supervised learning algorithms learn from labeled data, where the desired output is known. These algorithms aim to build a model that can predict the output for new, unseen input data. Let’s take a ...

Machine learning has a wide range of applications in the finance, healthcare, marketing and transportation industries. It is used to analyze and process large amounts of data, make predictions, and ...

WEST LAFAYETTE, Ind. — To expand the availability of electricity generated from nuclear power, several countries have started developing designs for small modular reactors (SMRs), which could take ...

For about a decade, computer engineer Kerem Çamsari employed a novel approach known as probabilistic computing. Based on probabilistic bits (p-bits), it’s used to solve an array of complex ...

Researchers have used a machine learning model to identify three compounds that could combat aging. They say their approach could be an effective way of identifying new drugs, especially for complex ...

William Brady does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond ...

The primary motivation for the PI’s team was achieving motion performance considered impossible by others. This pertains to the Bode sensitivity integral, which implies that enhancing one frequency ...