## 4 Experimental Validation  
In this section, the outcomes of the HCOA-SACDC model are tested using benchmark datasets from the Kaggle repository [22]. The dataset holds 1049 samples under insult class and 2898 samples under normal class. This is a single-class classification problem. The label 0 implies a neutral comment, and 1 implies an insulting comment (neutral is regarded as not belonging to the insult class). The prediction should be a real number in the range from zero to one, where one is a 100% confident prediction that the comment is an insult. Figure 3 illustrates the confusion matrices presented by the HCOA-SACDC method with dissimilar training/testing (TR/TS) dataset sizes. With a TR/TS dataset of 90:10, the HCOA-SACDC method identified 85 instances of insult and 290 instances of normal. Moreover, with a TR/TS dataset of 80:20, the HCOA-SACDC approach identified 178 instances of insult and 554 instances of normal. Simultaneously, with a TR/TS dataset of 70:30, the HCOA-SACDC approach identified 233 instances of insult and 861 instances of  
normal. Concurrently, with a TR/TS dataset of 60:40, the HCOA-SACDC system identified 267 instances of insult and 1170 instances of normal. Table 1 provides a detailed classification outcome of the HCOA-SACDC technique with various sizes of data. The stimulation results indicate that the HCOA-SACDC system obtained the highest outcome in all aspects. Figure 4 reports a brief \(prec_{n}\) and \(rec_{l}\) inspection of the HCOA-SACDC method with dissimilar TR/TS dataset sizes. The results indicate that the HCOA-SACDC technique accomplishes increasing values of \(prec_{n}\) and \(rec_{l}\). For example, with a TR/TS of 90:10, the HCOA-SACDC method provided \(prec_{n}\) and \(rec_{l}\) values of 96.94% and 90.69%, respectively. Simultaneously, with a TR/TS of 70:30, the HCOA-SACDC system provided \(prec_{n}\) and \(rec_{l}\) values of 94.73% and 86.12%, respectively. Additionally, with a TR/TS of 60:40, the HCOA-SACDC methodology provided \(prec_{n}\) and \(rec_{l}\) values of 92.31% and 83.29%, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Score** \\ \hline \multicolumn{6}{c}{**Training/Testing (90:10)**} \\ \hline Insult & 94.94 & 98.84 & 81.73 & 99.66 & 89.47 \\ \hline Normal & 94.94 & 93.85 & 99.66 & 81.73 & 96.67 \\ \hline
**Average** & **94.94** & **96.34** & **90.69** & **90.69** & **93.07** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Result analysis of HCOA-SACDC approach with distinct measures and TR/TS datasets. Figure 3: Confusion matrix of HCOA-SACDC system under distinct TR/TS dataset sizes. Figure 5 reports a brief \(spec_{y}\) and \(F_{score}\) examination of the HCOA-SACDC method on distinct TR/TS dataset sizes. The results indicate that the HCOA-SACDC system accomplishes increasing values of \(spec_{y}\) and \(F_{score}\). For example, with a TR/TS of 90:10, the HCOA-SACDC approach provided \(spec_{y}\) and \(F_{score}\) values of 90.69% and 93.07%, respectively. With a TR/TS of 70:30, the HCOA-SACDC algorithm provided \(spec_{y}\) and \(F_{score}\) values of 86.12% and 89.32%, respectively. Finally, with a TR/TS of 60:40, the HCOA-SACDC method provided \(spec_{y}\) and \(F_{score}\) values of 83.29% and 86.64%, respectively. Figure 4: \(Prec_{n}\) and \(rec_{nI}\) results of HCOA-SACDC technique using distinct TR/TS dataset sizes. Figure 6 demonstrates a detailed \(acc_{y}\) analysis of the HCOA-SACDC system on distinct TR/TS dataset sizes. The results indicate that the HCOA-SACDC method accomplished increasing values of \(acc_{y}\). For instance, with a TR/TS of 90:10, the HCOA-SACDC approach provided an \(acc_{y}\) of 94.94%. Simultaneously, with a TR/TS of 70:30, the HCOA-SACDC system provided an \(acc_{y}\) of 92.32%. Moreover, with a TR/TS of 60:40, the HCOA-SACDC method provided an \(acc_{y}\) of 91.01%. Figure 5: \(Spec_{y}\) and \(F_{score}\) results of HCOA-SACDC technique using distinct TR/TS dataset sizes.