Abstract: Graph classification has always been a research hotspot in the field of graph neural networks and related areas. However, due to the complexity of graph data, finding a feasible algorithm ...

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Electroencephalography (EEG) holds immense potential for decoding complex brain patterns associated with cognitive states and neurological conditions. In this paper, we propose an end-to-end framework ...

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 ...

In patients with tetralogy of Fallot (TOF) repaired with a transannular patch (TAP), right ventricular outflow tracts (RVOT) are classified by shape using complex 3D measurements. RVOT shape is ...

ABSTRACT: In this article, we have described the Todd-Coxeter algorithm. Indeed, the Todd-Coxeter algorithm is a mathematical tool used in the field of group theory. It makes it possible to determine ...

ABSTRACT: In agriculture sector, machine learning has been widely used by researchers for crop yield prediction. However, it is quite difficult to identify the most critical features from a dataset.