Human Activity Recognition Based on Deep-Temporal Learning Using Convolution Neural Networks Features and Bidirectional Gated Recurrent Unit With Features Selection-Reference-Cited by-同舟云学术

Human Activity Recognition Based on Deep-Temporal Learning Using Convolution Neural Networks Features and Bidirectional Gated Recurrent Unit With Features Selection

Published:2023 Issue: Volume:11 Page:33148-33159
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ahmad Tariq¹^ORCID,Wu Jinsong²^ORCID,Alwageed Hathal Salamah³,Khan Faheem⁴^ORCID,Khan Jawad⁵^ORCID,Lee Youngmoon⁵^ORCID

Affiliation:

1. School of Information and Communication Engineering, Guilin University of Electronic Technology, Guilin, China

2. School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, China

3. College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia

4. Department of Computer Engineering, Gachon University, Seongnam, South Korea

5. Department of Robotics, Hanyang University, Ansan, South Korea

Funder

Research Fund of Hanyang University

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/ielx7/6287639/10005208/10089162.pdf?arnumber=10089162

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5. A bag-of-words equivalent recurrent neural network for action recognition

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