Comparison of novel reinforcement learning with random forest algorithm to improve prediction rate of social vulnerability in web application.

The prediction rate of social vulnerability in a web application based on a comparison of the F1-Measure of novel Reinforcement learning and the Random forest method. Components and Techniques: Outcome F1-measure and the Random Forest Algorithm with a New Reinforcement Learning Algorithm (83.85 percent). There are a total of 55,336 samples to be analysed, split evenly between two groups. Discussion and Results With an F1-measure score of 83.85 percent, the novel Reinforcement learning algorithm outperforms the more traditional Random forest approach for detecting social vulnerability (74.58 percent). In this study's final analysis, the Novel Reinforcement Learning algorithm was shown to be more accurate in predicting social vulnerability than the Random Forest technique. [ABSTRACT FROM AUTHOR]