A comparison of the recurrent neural network over AlexNet in predicting the influence of online games on mental health among youngsters.

This research aims to improve the precision of predicting the relationship between mental health issues and online gaming in young people by implementing an Enhanced Recurrent Neural Network in conjunction with AlexNet. In this study, two groups were formed: Novel Recurrent Neural Network and Alexnet. Each group had a sample size of 10, determined using ClinCalc software, with an alpha value of 0.05 and a 95% Confidence Interval (CI). The dataset used in the research included 20,200 observations related to gaming behavior and mental disorder information, collected from Kaggle. The accuracy of the Enhanced Recurrent Neural Network (ERNN) was found to be significantly higher (96.27%) compared to that of AlexNet (93.22%), as determined by an Independent sample T-test with a p-value of 0.001 (p<0.05). These results indicate a statistically significant difference between ERNN and AlexNet algorithms. The accuracy of the Enhanced Recurrent Neural Network surpasses that of AlexNet in predicting the impact of online games on the mental health of young individuals. [ABSTRACT FROM AUTHOR]