Enhance the AI Virtual System Accuracy with Novel Hand Gesture Recognition Algorithm Comparing to Convolutional Neural Network

The objective of this study is to enhance the precision of AI virtual systems by implementing Novel Hand Gesture Recognition techniques in comparison to Convolutional Neural Network. Materials and Methods: To recognise hand motions, a Convolutional Neural Network with distinct training and testing stages is utilized. The average Gpower for the test is between 0.05 and 0.85, or around 85%. Sample size is determined as 27,455 for each group using G Power 3.1 software (G Power setting parameters: α=0.05 and power=0.85). Results and Discussion: Novel Hand gesture recognition 92.60% identifies between objects and boosts the observed accuracy with a statistically non-significant value of p=0.123 (p>0.05) in comparison to the convolutional neural network's 88.59%. Conclusion: Comparison of the Novel Hand gesture Recognition algorithm and Convolutional Neural Network in terms of performance that shows Hand gesture recognition has 91.62% with better accuracy.