## 4 Main Algorithm
\(K\)-fold cross-validation (CV) [32] is a common technique used in ML to assess the performance of a predictive model while simultaneously reducing the risk of overfitting. This technique divides the available dataset into \(K\) parts, known as folds, of approximately equal size. Subsequently, the model is trained and evaluated \(K\) times, each time using a different part of the dataset as the test set and the remaining \(K-1\) parts as the training set. The goal of \(K\)-fold cross-validation is to evaluate how well the model generalizes to unseen data, avoiding the risk of misleading evaluations due to specific random divisions of the dataset. In our model, \(K\) has been set to 10. ### Analysis of Experimental Results

Experiments for classification were conducted using both the binary method and the multi-class method. In the following subsection, we present the results related to both methods. **Binary results analysis. ** The performance analysis of the binary classification is displayed in Figures 3 and 4. The confusion matrices are depicted in Figure 5.

The ROC curves are presented in Figure 6.

From Figures 3-6, in the analysis of binary results, we find that TLA-NET produces a better outcome in terms of both performance rate and a lower error rate, as well as a better AUC score. **Multi-class results analysis. ** The performance analysis of the multi-class classification is displayed in Figures 7 and 8. The confusion matrices are depicted in Figure 9.

The ROC curves are presented in Figure 10.

From Figures 7-10, in the analysis of the multi-class results, we find that TLA-NET yields better performance rates and a lower error rate, as well as a superior AUC score. Figure 3: Binary classification performance evaluation of different models with the proposed approach. Figure 4: Binary classification error evaluation of different models with the proposed approach. Figure 5: Confusion matrices for binary classification labels across different models. \[\textbf{MSE}=\frac{\sum_{i=1}^{n}(\textbf{predicted}(i)-\textbf{actual}(i))^ {2}}{n}, \tag{3}\]0 ROC curves for the binary classification labels across different models. \[\textbf{MSE}=\frac{\sum_{i=1}^{n}(\textbf{predicted}(i)-\textbf{actual}(i))^ {2}}{n}, \tag{3}\]1 Multi-class classification performance evaluation of different models with the proposed approach. \[\textbf{MSE}=\frac{\sum_{i=1}^{n}(\textbf{predicted}(i)-\textbf{actual}(i))^ {2}}{n}, \tag{3}\]2 Multi-class classification error evaluation of different models with the proposed approach. \[\textbf{MSE}=\frac{\sum_{i=1}^{n}(\textbf{predicted}(i)-\textbf{actual}(i))^ {2}}{n}, \tag{3}\]3 Confusion matrices for multi-class classification labels across different models. ## 6 Conclusions and Future Work

Bullying often occurs on social media because it provides aggressors with a place to hide, making them difficult to track down and avoiding direct confrontation. This situation makes it challenging to identify instances of bullying, resulting in suffering for the victims and often causing mental distress, sadness, and, in severe cases, even suicide. In response to this toxic climate on social media, it is essential to take proactive as well as reactive action. With the goal of classifying cyberbullying, this study has presented the process of automatically translated data generation and how deep learning models perform on this dataset. Furthermore, a modified LSTM-autoencoder model has been developed, which offers highly accurate performance on both noise-free and noisy data. The raw data represent the dataset we collected from the organization. The semi-noisy dataset refers to the augmented data we added to the raw data, while the noisy dataset refers to the data we translated from the Bangla and Hindi languages to the English language dataset. We have conducted a comparative analysis of our proposed model against some state-of-the-art ML approaches. To evaluate the model performances, we have used several state-of-the-art metrics and composite evaluation tools.