Residual Neural Network for the Accurate Recognition of Human Action and Compared with Bayesian Regression

Abstract

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.

Keywords

• bayesian regression
• classifiers
• human action
• machine learning
• novel residual neural network
• recognition
• technology