## 4 Results and Discussion  
The presented AFSA-WELM model was simulated using a dataset collected from the Kaggle repository. The dataset holds 12,729 samples with 11,661 data classified as non-cyberbullying and 1068 data in the cyberbullying class. Figure 3 illustrates the confusion matrices offered by the AFSA-WELM model for distinct sizes of training/testing datasets. With a training/testing data split of 90:10, the proposed model identified 1158 samples in the non-cyberbullying class and 104 samples in the cyberbullying class. In addition, with a training/testing data split of 80:20, the AFSA-WELM method identified 2323 samples in the non-cyberbullying class and 187 samples in the cyberbullying class. Meanwhile, with a training/testing data split of 70:30, the proposed model identified 3432 samples in the non-cyberbullying class and 215 samples in the cyberbullying class. Finally, with a training/testing data split of 60:40, the AFSA-WELM technique identified 4619 samples in the non-cyberbullying class and 347 samples in the cyberbullying class. Table 2 reports the overall classifier results of our proposed model with different training/testing dataset sizes. With a training/testing dataset split of 90:10, the AFSA-WELM model resulted in an \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 99.14%, 95.22%, 99.53%, and 97.25%, respectively. Moreover, with a training/testing dataset split of 80:20, the proposed model resulted in an \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 98.59%, 96.26%, 94.25%, and 95.23% correspondingly. In addition, with a training/testing dataset split of 70:30, the proposed model resulted in an \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 95.50%, 86.88%, 82.46%, and 84.49%, correspondingly. Lastly, with a training/testing dataset split of 60:40, the proposed model resulted in an \(accu_{y}\), \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 97.53%, 93.19%, 90.22%, and 91.64%, correspondingly. A brief precision-recall examination of the AFSA-WELM model with different training/testing dataset sizes is portrayed in Figure 4. By observing the figure, it can be noticed that the AFSA-WELM model achieved maximum precision-recall performance for all datasets. The figure shows the recall and the precision values of the four different samples. Figure 3: Confusion matrix of the proposed model for different training/testing dataset sizes. Where (**a**) considers 90:10 training/testing data split, (**b**) considers 80:20 training/testing data split, (**c**) considers 70:30 training/testing data split, and (**d**) considers 60:40 training/testing data split. Figure 5 portrays the ROC investigation of the proposed model with different training/testing dataset sizes. The figure shows the false positive rate and the true positive rate. The figure has four different training/testing samples to verify the stability of the proposed model's performance. The figure shows that the AFSA-WELM model produces adequate results with maximum ROC values for the different class labels. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline \multicolumn{5}{c}{Training/Testing (90:10)} \\ \hline Non-Cyberbullying & 99.14 & 100.00 & 99.06 & 99.53 \\ Cyberbullying & 99.14 & 90.43 & 100.00 & 94.98 \\ Average & 99.14 & 95.22 & 99.53 & 97.25 \\ \hline \multicolumn{5}{c}{Training/Testing (80:20)} \\ \hline Non-Cyberbullying & 98.59 & 99.02 & 99.44 & 99.23 \\ Cyberbullying & 98.59 & 93.50 & 89.05 & 91.22 \\ Average & 98.59 & 96.26 & 94.25 & 95.23 \\ \hline \multicolumn{5}{c}{Training/Testing (70:30)} \\ \hline Non-Cyberbullying & 95.50 & 96.98 & 98.14 & 97.56 \\ Cyberbullying & 95.50 & 76.79 & 66.77 & 71.43 \\ Average & 95.50 & 86.88 & 82.46 & 84.49 \\ \hline \multicolumn{5}{c}{Training/Testing (60:40)} \\ \hline Non-Cyberbullying & 97.53 & 98.32 & 98.99 & 98.65 \\ Cyberbullying & 97.53 & 88.07 & 81.46 & 84.63 \\ Average & 97.53 & 93.19 & 90.22 & 91.64 \\ \hline \hline \end{tabular}
\end{table}