HADE: Exploiting Human Action Recognition Through Fine-Tuned Deep Learning Methods-Reference-Cited by-同舟云学术

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HADE: Exploiting Human Action Recognition Through Fine-Tuned Deep Learning Methods

Published:2024 Issue: Volume:12 Page:42769-42790
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Karim Misha¹,Khalid Shah¹^ORCID,Aleryani Aliya²^ORCID,Tairan Nasser²^ORCID,Ali Zafar³^ORCID,Ali Farman⁴^ORCID

Affiliation:

1. School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, Pakistan

2. Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia

3. School of Computer Science and Engineering, Southeast University, Nanjing, China

4. Department of Applied AI, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, South Korea

Funder

Deanship of Scientific Research at King Khalid University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10474006.pdf?arnumber=10474006

Reference81 articles.

1. Design of a Real-Time Physical Activity Detection and Classification Framework for Individuals With Type 1 Diabetes

2. An overview of Human Action Recognition in sports based on Computer Vision

3. Human activity recognition in artificial intelligence framework: a narrative review

4. Review on human activity recognition using vision-based method;Kang;J. Ambient Intell. Humanized Computing,2016

5. Mental Health of Working Adults during the COVID-19 Pandemic: Does Physical Activity Level Matter?

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