Graph convolutional network (GCN) has been successfully applied to capture global non-consecutive and long-distance semantic information for text classification. However, while GCN-based methods have shown promising results in offline evaluations, they commonly follow a seen-token-seen-document paradigm by constructing fixed document-token graph cannot make inferences on new documents. It is ch...

ABSTRACT The Fermi-LAT DR1 and DR2 4FGL catalogues feature more than 5000 gamma-ray sources of which about one fourth are not associated with already known objects, approximately third blazars uncertain nature. We perform a three-category classification the independently, using an ensemble Artificial Neural Networks (ANNs) to characterize them based on likelihood being Pulsar (PSR), BL Lac type...

Diabetic Retinopathy (DR) is a disease caused by high level of glucose in retina vessels. This malicious put millions people around the world at risk for vision loss each year. Being life-threatening disease, early diagnosis can be an effective step treatment and prevention loss. To automate process, computer-aided methods are not only useful detecting diabetic signatures but also provide infor...

<p>This paper presents a novel version of hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply PCA dimensional reduction technique feature matrices image datasets in order reduce “noise” and redundant features runtime constructing Then, classic graph based semisupervised method, semi-supervised network, our proposed are emplo...