Comparative Performance Analysis of Deep Neural Networks for Human Activity Recognition-Reference-Cited by-同舟云学术

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Comparative Performance Analysis of Deep Neural Networks for Human Activity Recognition

Published:2023-11-06 Issue: Volume:5 Page:62-67
ISSN:
Container-title:2023 18th International Conference on Emerging Technologies (ICET)
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Author:

Burney Muhammad Ashar¹,Waheed Ayesha¹,Khan Shuaib²,Ghori Sidra³,Shaikh Muhammad Bilal⁴,Qureshi Rizwan⁵

Affiliation:

1. School of Computing, National University of Computer and Emerging Sciences,Karachi,Pakistan

2. Government Degree College Shahdadpur,Shahdadpur,Pakistan

3. Karachi Institute of Biotechnology and Genetic Engineering, University of Karachi,Karachi,Pakistan

4. School of Engineering, Edith Cowan University,Perth,Australia

5. City University of Hong Kong,Dept. of Electrical Engineering,Hong Kong,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10374480/10374484/10374609.pdf?arnumber=10374609

Reference27 articles.

1. Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation

2. Tensorpose: Real-time pose estimation for interactive applications

3. Hybrid Separable Convolutional Inception Residual Network for Human Facial Expression Recognition

4. Data augmentation for improving deep learning in image classification problem

5. Object detection and segmentation by composition of fast fuzzy C-mean clustering based maps

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