Abstract: The brain signal classification is the basis for the implementation of brain–computer interfaces (BCIs). However, most existing brain signal classification methods are based on signal ...

Objectives This study aimed to investigate the effects of long-term and habitual physical activity on mortality and long-term care insurance (LTCI) certification among cancer survivors using a ...

Distributed Integrated Energy Microgrid, as a key infrastructure for the low-carbon transition of regional energy systems, faces critical challenges in achieving optimal operation—primarily due to ...

The project titled "Medical Image Classification for Disease Diagnosis Using Convolutional Neural Networks" aims to develop a robust and accurate machine learning model for the automatic ...

For over two decades, millions of people volunteered the computational capacity of their computers to help UC Berkeley scientists in their search for extraterrestrial intelligence (SETI). The goal of ...

This project provides a powerful and flexible PDF analysis microservice built with Clean Architecture principles. The service enables OCR, segmentation, and classification of different parts of PDF ...

When a natural disaster strikes, first responder managers face a flood of data — from drones, sensors, cameras, satellites, police, firefighters, and citizens — that must be sorted, secured, and ...

Officials confirmed Tuesday that the state of Nevada’s computer network was targeted in a cyberattack and is under active state and federal investigation as IT staffers work to restore service. The ...

Aiming to address the needs of a world in which the vast majority of business leaders see networks as more complicated than just two years ago, Extreme Networks has launched new capabilities in its ...

Department of Physics, University of Florida, Gainesville, Florida 32611, United States Center for Molecular Magnetic Quantum Materials, Gainesville, Florida 32611, United States Center for Molecular ...

Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different neighbors when aggregating their features to update a ...