Results 171 to 180 of about 5,349,001 (214)

A lightweight deep convolutional neural network for detecting artifacts in continuous EEG signals. [PDF]

Clin Neurophysiol Pract
Nyanney E, Thirumala PD, Visweswaran S, Geng Z.   +3 more
europepmc   +1 more source

Multi-FusNet-convolutional neural network with improved Huber loss function for plant leaf disease detection and classification. [PDF]

Front Plant Sci
Shruthi BS, Narasimha Murthy MS, Aldakheel EA, Khafaga DS.   +3 more
europepmc   +1 more source

TB-SERS analyzer: Analysis tool for tuberculosis prediction based on Raman spectroscopy with machine learning and convolutional neural network. [PDF]

PLoS Comput Biol
Eisiri J, Juntagran C, Trisakul K, Kaewseekhao B, Nuntawong N, So-In C, Faksri K.   +6 more
europepmc   +1 more source

A multi-scale feature fusion gaze estimation model based on convolutional neural network and vision transformer. [PDF]

Sci Rep
Wang P, Wang X, Yuan S, Cong S.
europepmc   +1 more source

Convolutional Neural Network for Specimen-Invariant Structural Health Monitoring of FRC Under Flexural Loading. [PDF]

Sensors (Basel)
Sapidis GM, Kansizoglou I, Naoum MC, Papadopoulos NA, Tsintotas KA, Voutetaki ME, Gasteratos A.   +6 more
europepmc   +1 more source

Differential convolutional neural network

Neural Networks, 2019
Convolutional neural networks with strong representation ability of deep structures have ever increasing popularity in many research areas. The main difference of Convolutional Neural Networks with respect to existing similar artificial neural networks is the inclusion of the convolutional part.
Mehmet Sarigul, Buse Melis Özyildirim, Mutlu Avci   +2 more
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Attentiondrop for Convolutional Neural Networks

2019 IEEE International Conference on Multimedia and Expo (ICME), 2019
Dropout has been widely used in fully connected networks but becomes less effective for convolutional neural networks (CNNs), since the spatially correlated features still allow dropped information to flow through the network. To make dropout more practical for CNNs, structured dropout methods have been recently proposed by dropping regions with fixed ...
Zhihao Ouyang, Yan Feng, Zihao He, Tianbo Hao, Tao Dai 0001, Shu-Tao Xia   +5 more
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Denoising Convolutional Neural Network

2015 IEEE International Conference on Information and Automation, 2015
Convolutional Neural Network (CNN) is a kind of deep artificial neural network. CNN has kinds of merits, such as multidimensional data input, and fewer parameters. However, the network always has the problem of overfitting due to lots of connection in the full connection layer.
Qingyang Xu, Chengjin Zhang, Li Zhang 0043   +2 more
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In vitro convolutional neural networks

Nature Machine Intelligence, 2022
© 2022 Springer Nature Limited. Published 11 July 2022. The author declares no competing interests.
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