Results 21 to 30 of about 34,789 (293)

Multispectral Pansharpening Based on Multisequence Convolutional Recurrent Neural Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Multispectral (MS) pansharpening is defined as the fusion of spatial information in panchromatic (PAN) image and spectral information in MS image. In this work, we propose an MS pansharpening based on multisequence convolutional recurrent neural network (
Peng Wang, Zhongchen He, Ying Zhang, Gong Zhang, Hongchao Liu, Henry Leung   +5 more
doaj   +1 more source

Fast Graph Convolutional Recurrent Neural Networks [PDF]

2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019
This paper proposes a Fast Graph Convolutional Neural Network (FGRNN) architecture to predict sequences with an underlying graph structure. The proposed architecture addresses the limitations of the standard recurrent neural network (RNN), namely, vanishing and exploding gradients, causing numerical instabilities during training.
Kadambari, Sai Kiran, Chepuri, Sundeep Prabhakar   +1 more
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A convolutional recurrent neural network for strong convective rainfall nowcasting using weather radar data in Southeastern Brazil

Artificial Intelligence in Geosciences, 2022
Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences. These events have a high spatio-temporal variability, being difficult to predict by standard meteorological ...
Angelica N. Caseri, Leonardo Bacelar Lima Santos, Stephan Stephany   +2 more
doaj   +1 more source

Gated Graph Convolutional Recurrent Neural Networks [PDF]

2019 27th European Signal Processing Conference (EUSIPCO), 2019
Graph processes model a number of important problems such as identifying the epicenter of an earthquake or predicting weather. In this paper, we propose a Graph Convolutional Recurrent Neural Network (GCRNN) architecture specifically tailored to deal with these problems.
Ruiz, Luana, Gama, Fernando, Ribeiro, Alejandro   +2 more
openaire   +2 more sources

Method for predicting cutter remaining life based on multi-scale cyclic convolutional network

International Journal of Distributed Sensor Networks, 2022
In the process of predicting the remaining cutter life, the deep-learning method such as convolutional neural network does not consider the time correlation of different degradation states, which directly affects the accuracy of the remaining cutter life
Tao Li, Yinquan Yu, Chaoqiang Huang, Jinwen Yang, Yuanchang Zhong, Yong Hao   +5 more
doaj   +1 more source

Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks [PDF]

IEEE Transactions on Image Processing, 2016
Existing deep convolutional neural networks (CNNs) have shown their great success on image classification. CNNs mainly consist of convolutional and pooling layers, both of which are performed on local image areas without considering the dependencies among different image regions.
Zuo, Zhen, Shuai, Bing, Wang, Gang, Liu, Xiao, Wang, Xingxing, Wang, Bing, Chen, Yushi   +6 more
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Multiclass classification of myocardial infarction with convolutional and recurrent neural networks for portable ECG devices

Informatics in Medicine Unlocked, 2018
Myocardial infarction (MI) is a medical emergency for which the early detection of symptoms is desirable. The prevalence of portable electrocardiogram (ECG) devices makes frequent screening for MI possible. In this study, we develop an MI classifier that
Hin Wai Lui, King Lau Chow
doaj   +1 more source

Deep Feature Learning for Disease Risk Assessment Based on Convolutional Neural Network With Intra-Layer Recurrent Connection by Using Hospital Big Data

IEEE Access, 2018
This paper presents the analysis of real-life medical big data obtained from a hospital in central China from 2013 to 2015 for risk assessment of cerebral infarction disease.
Mohd Usama, Belal Ahmad, Jiafu Wan, M. Shamim Hossain, Mohammed F. Alhamid, M. Anwar Hossain   +5 more
doaj   +1 more source

Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN

BMC Medical Informatics and Decision Making, 2021
Background Epilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists’ reading of the electroencephalogram (EEG), which was time ...
Mengnan Ma, Yinlin Cheng, Xiaoyan Wei, Ziyi Chen, Yi Zhou   +4 more
doaj   +1 more source

ECG Classification With a Convolutional Recurrent Neural Network

Computing in Cardiology Conference (CinC), 2020
4 pages, 3 figures, presented at Computing in Cardiology 2020, under review for conference ...
Sigurthorsdottir, Halla, Van Zaen, Jérôme, Delgado-Gonzalo, Ricard, Lemay, Mathieu   +3 more
openaire   +2 more sources