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Developing Novel Activation Functions Based Deep Learning LSTM for Classification

Published:2022 Issue: Volume:10 Page:97259-97275
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Essai Ali Mohamed H.¹^ORCID,Abdel-Raman Adel B.²,Badry Eman A.³

Affiliation:

1. Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Qena, Egypt

2. Department of Electrical Engineering, Faculty of Engineering, South Valley University, Qena, Egypt

3. Department of Computer and System, Faculty of Engineering, Al-Azhar University, Cairo, Egypt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09885032.pdf?arnumber=9885032

Reference38 articles.

1. Effects of Different Activation Functions for Unsupervised Convolutional LSTM Spatiotemporal Learning

2. Deep convolutional neural network for decoding motor imagery based brain computer interface

3. Gaussian error linear units (GELUs);hendrycks;arXiv 1606 08415,2016

4. A skewed derivative activation function for SFFANNs

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