To solve this challenge, in this paper we propose a novel spammer detecting method using DCNN (Diffusion Convolution Neural Network) which is a graph-based model. And DCNN model can learn behavior information from other users through the graph structure (i.e., social network relationships). ...
A cross-domain complex convolution neural network for undersampled magnetic resonance image reconstruction 2024, Magnetic Resonance Imaging Citation Excerpt : Even though its neural networks have nonlinear layers, it is challenging to handle the complex exponentials required for IFT, which can impact the...
@inproceedings{gasteiger_diffusion_2019, title = {Diffusion Improves Graph Learning}, author = {Gasteiger, Johannes and Wei{\ss}enberger, Stefan and G{\"u}nnemann, Stephan}, booktitle={Conference on Neural Information Processing Systems (NeurIPS)}, year = {2019} }...
Therefore, the underlying relationship between FC and SC, as well as the complicated interactions among network nodes, has not been sufficiently studied and fully utilized to discover disease-related biomarkers. To tackle these problems, we propose a Diffusion-Convolution-Bilinear Neural Network (DCB-...
Deep face liveness detection based on nonlinear diffusion using convolution neural network. Signal, Image and Video Processing, pages 1-8, 2016.Alotaibi, A.; Mahmood, A. Deep face liveness detection based on nonlinear diffusion using convolution neural network. Signal Image Video Process. 2017, ...
The augmentation algorithms were applied independently to each data channel and a shallow as well as a deep convolutional neural network (CNN) was trained on the five augmented sets separately. We used area under receiver operating characteristic (ROC) curve (AUC) to evaluate the performance of ...
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv 2017, arXiv:1707.01926. [Google Scholar] Chaolong, L.; Zhen, C.; Wenming, Z.; Chunyan, X.; Jian, Y. Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition. In Proceedings of the Thirty-...
However, correlations of acquired adjacent gradient directions are often ignored. To make use of this information in a neural network, a spherical convolutionis necessary. This work evaluates three different ways to include spherical information: 2D projection, local spherical convolutionand Fourier ...
A classifier algorithm utilizing a recurrent residual convolutional neural network (RRCNN) was developed for classification between low (1–6) and high (7–10) DWI-ASPECTS groups. The model performance was compared with a pre-trained VGG16, Inception V3, and a 3D convolutional neural network (...