As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Serverless service that generates dynamic Open Graph images that you can embed in your <meta> tags. For each keystroke, headless chromium is used to render an HTML ...

Abstract: Medical image segmentation is a crucial step toward automatic clinical diagnosis, which has received growing interest. Although some existing methods based on convolutional neural networks ...

turn images into navigable graphs and find the best path between 2 points using graph theory algorithms. Made for my year conclusion project, for IFPR Campus Cascavel ...

Decoding emotional states from electroencephalography (EEG) signals is a fundamental goal in affective neuroscience. This endeavor requires accurately modeling the complex spatio-temporal dynamics of ...

Graph convolutional networks demonstrate advantages such as low sample requirements and strong global information modeling capabilities in the semantic segmentation of synthetic aperture radar (SAR).

Visual Attention Networks (VANs) leveraging Large Kernel Attention (LKA) have demonstrated remarkable performance in diverse computer vision tasks, often outperforming Vision Transformers (ViTs) in ...

Brain-computer interfaces (BCIs) are advanced and innovative systems that enable direct communication between humans and external devices by utilizing data encoded in the brain activity (Shi et al., ...