Results 231 to 240 of about 36,148 (258)

An algorithm for seizure detection in rodents

Epilepsia Open, EarlyView.
Abstract Objective Epilepsy animal research often relies on long‐term intracranial electroencephalographic (iEEG) recordings. Here, we describe an artificial neural network (ANN) algorithm for automatic detection of seizures. Methods The algorithm was trained on iEEG recordings of three mouse models of chronic epilepsy: (1) the pilocarpine model of ...
Lyna Kamintsky, Gerben van Hameren, Itai Weissberg, Pooyan Moradi, Ofer Prager, Alaa Abu Ahmad, Lior Schori, Albert Becker, Yaniv Zigel, Alon Friedman   +9 more
wiley   +1 more source

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Convolutional Graph Neural Networks

2020
Applying deep learning to the pervasive graph data is significant because of the unique characteristics of graphs. Recently, substantial amounts of research efforts have been keen on this area, greatly advancing graph-analyzing techniques. In this study, the authors comprehensively review different kinds of deep learning methods applied to graphs. They
J. Joshua Thomas, Tran Huu Ngoc Tran, Gilberto Pérez Lechuga, Bahari Belaton   +3 more
openaire   +1 more source

Universal Readout for Graph Convolutional Neural Networks

2019 International Joint Conference on Neural Networks (IJCNN), 2019
Several machine learning problems can be naturally defined over graph data. Recently, many researchers have been focusing on the definition of neural networks for graphs. The core idea is to learn a hidden representation for the graph vertices, with a convolutive or recurrent mechanism.
Navarin N., Tran D. V., Sperduti A.
openaire   +1 more source

Explainability Methods for Graph Convolutional Neural Networks

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
With the growing use of graph convolutional neural networks (GCNNs) comes the need for explainability. In this paper, we introduce explainability methods for GCNNs. We develop the graph analogues of three prominent explainability methods for convolutional neural networks: contrastive gradient-based (CG) saliency maps, Class Activation Mapping (CAM ...
Phillip E. Pope, Soheil Kolouri, Mohammad Rostami, Charles E. Martin, Heiko Hoffmann   +4 more
openaire   +1 more source

Neighborhood convolutional graph neural network

Knowledge-Based Systems, 2023
Jinsong Chen 0002, Boyu Li 0003, Kun He 0001   +2 more
openaire   +1 more source

Weighted Feature Fusion of Convolutional Neural Network and Graph Attention Network for Hyperspectral Image Classification

IEEE Transactions on Image Processing, 2022
Yanni Dong, Quanwei Liu, Bo Du
exaly  

A novel graph convolutional feature based convolutional neural network for stock trend prediction

Information Sciences, 2021
Manrui Jiang, Wei-Guo Zhang, Zhensong Chen   +2 more
exaly  

Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network

Information Fusion, 2021
Shuihua Wang, J M Górriz, Xin Zhang
exaly  

CNN-G: Convolutional Neural Network Combined With Graph for Image Segmentation With Theoretical Analysis

IEEE Transactions on Cognitive and Developmental Systems, 2021
Yaran Chen, Dongbin Zhao, Bao Liu
exaly