| Issue |
E3S Web Conf.
Volume 399, 2023
International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
|
|
|---|---|---|
| Article Number | 04024 | |
| Number of page(s) | 9 | |
| Section | Computer Science | |
| DOI | https://doi.org/10.1051/e3sconf/202339904024 | |
| Published online | 12 July 2023 | |
Residual Neural Network for the Accurate Recognition of Human Action and Compared with Bayesian Regression
1 Research Scholar, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu. India. Pincode: 602105
2 Project Guide & Corresponding Author, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu. India. Pincode: 602105
* Corresponding Author Mail: rameshssecse@gmail.com
Aim: In this research article, the aim is to analyze and compare the performance of Residual Neural Network and Bayesian Regression for accurate recognition of human actions. Materials and Methods: The proposed machine learning classifier model uses 80% of the UCF101 dataset for training and the remaining 20% for testing. For the SPSS analysis, the results of two classifiers are grouped with 20 samples in each group. The sample size is determined using a pretest with G-power, with a sample size of 80%, a confidence interval of 95%, and a significance level of 0.014 (p<0.05). Result: The findings suggest that the novel residual neural network classifier and Bayesian regression classifier achieved accuracy rates of 95.63% and 93.97%, respectively, in identifying human activities accurately.The statistical significance value between residual neural networks and Bayesian regression has been calculated to be p=0.014 (independent sample t-test p<0.05), indicating a statistically significant difference between the two classifiers.
Key words: Bayesian Regression / Classifiers / Human Action / Machine Learning / Novel Residual Neural Network / Recognition / Technology
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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