Deep learning has been successfully applied in the field of medical diagnosis, and improving the accurate classification of ...

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 ...

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 ...

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., ...

Abstract: Graph convolutional networks (GCNs) have demonstrated remarkable performance in hyperspectral image classification and remote sensing scene recognition. However, their application to ...

Institute of Analytical Chemistry and Instrument for Life Science, The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an ...

Network segmentation is a security approach that divides the industrial network into smaller, solitary zones. Each acts as its own, distinct subnetwork, which limits access to devices or data and ...