Residual Neural Network for the Accurate Recognition of Human Action and Compared with Bayesian Regression
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 rem...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
|
| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04024.pdf |
| _version_ | 1797775946250452992 |
|---|---|
| author | V Narmatha S Ramesh |
| author_facet | V Narmatha S Ramesh |
| author_sort | V Narmatha |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T22:43:00Z |
| format | Article |
| id | doaj.art-29dfe021ead94c42a39343c0de83149a |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-12T22:43:00Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-29dfe021ead94c42a39343c0de83149a2023-07-21T09:28:46ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013990402410.1051/e3sconf/202339904024e3sconf_iconnect2023_04024Residual Neural Network for the Accurate Recognition of Human Action and Compared with Bayesian RegressionV Narmatha0S Ramesh1Research Scholar, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha UniversityProject Guide & Corresponding Author, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha UniversityAim: 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04024.pdfbayesian regressionclassifiershuman actionmachine learningnovel residual neural networkrecognitiontechnology |
| spellingShingle | V Narmatha S Ramesh Residual Neural Network for the Accurate Recognition of Human Action and Compared with Bayesian Regression E3S Web of Conferences bayesian regression classifiers human action machine learning novel residual neural network recognition technology |
| title | Residual Neural Network for the Accurate Recognition of Human Action and Compared with Bayesian Regression |
| title_full | Residual Neural Network for the Accurate Recognition of Human Action and Compared with Bayesian Regression |
| title_fullStr | Residual Neural Network for the Accurate Recognition of Human Action and Compared with Bayesian Regression |
| title_full_unstemmed | Residual Neural Network for the Accurate Recognition of Human Action and Compared with Bayesian Regression |
| title_short | Residual Neural Network for the Accurate Recognition of Human Action and Compared with Bayesian Regression |
| title_sort | residual neural network for the accurate recognition of human action and compared with bayesian regression |
| topic | bayesian regression classifiers human action machine learning novel residual neural network recognition technology |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04024.pdf |
| work_keys_str_mv | AT vnarmatha residualneuralnetworkfortheaccuraterecognitionofhumanactionandcomparedwithbayesianregression AT sramesh residualneuralnetworkfortheaccuraterecognitionofhumanactionandcomparedwithbayesianregression |