Improved accuracy in automatic deduction of cyberbullying using recurrent neural network compare accuracy with random forest

This study compares the accuracy of a random forest classifier with a revolutionary recurrent neural network-based cyber bullying deduction. Supplies and Methods: Two businesses employ this strategy. This research used the Random Forest technique in Group 2 & Recurrent Neural Networks in Group 1 to analyze 20 samples from each group in order to evaluate the validity of the new Deduction of Cyber bullying. G power was used to calculate the sample size, and the pretest power was fixed at 80%. Findings: Random forest has an accuracy of 92.36%, whereas RNN has 94.55%. A statistically significant difference of 0.18 (p>0.05) has been discovered. To sum up, recurrent neural network methods outperform random forest classifiers in terms of accuracy.