# Gender Recognition of Bangla Names Using Deep Learning Approaches
## 8 Experimental Results and Discussion  
It is a very difficult and critical issue to extract information from Bangla names. In this work, five deep learning classifiers, such as LSTM, Bi-LSTM, stacked LSTM, and Conv1D and stacked Bi-LSTM, have been applied using our own dataset. To evaluate the effectiveness of our developed classifier models perfectly, we examined several performance metrics: accuracy, precision, recall, F1-score, and ROC AUC score. The tabular presentation of the performance metrics' values of the five proposed systems, along with the comparison, are neatly shown in Tables 4 and 5. Table 6 represents the summary of the experimental results. It is clearly depicted from Table 4 that the ConvNet-based model outperforms all in terms of accuracy, precision, recall, F1-score, and ROC AUC score performance metrics. From Table 5, it is also shown that the prediction accuracy of the Conv1D-based model is high for training, validation, and test dataset. The CNN-based system achieved the highest accuracy, 92.16%, and foremost, an F1 score of 0.920, which is clearly presented in Table 6. On the contrary, the LSTM-based system provides minimal performance with an accuracy of 71.57% and an F1 score value of 0.773 only. In addition, the graphical representation of the accuracy and loss values of the five different models are evidently displayed in Figures 4-6. From the experimental results, we can conclude that the Conv1D \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **ROC AUC Score** \\ \hline LSTM-Based Model & 0.715686 & 0.638712 & 0.980198 & 0.773438 & 0.718254 \\ Bi-LSTM-Based Model & 0.872549 & 0.878788 & 0.861386 & 0.870012 & 0.872441 \\ Stacked LSTM-Based Model & 0.813725 & 0.811881 & 0.811881 & 0.811881 & 0.813708 \\ Conv1D-Based Model & 0.921569 & 0.929293 & 0.910891 & 0.920021 & 0.921465 \\ Conv1D and Stacked Bi-LSTM-Based Model & 0.921569 & 0.938144 & 0.900991 & 0.919192 & 0.921369 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance of gender detection from Bangla name data. Figure 5: Performance comparison among the different models in terms of accuracy for training, validation, and test dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gender Classification Model Name** & **Training** & **Validation** & **Test** \\ \hline LSTM-Based Model & 83.51\% & 75.96\% & 71.57\% \\ Bi-LSTM-Based Model & 88.68\% & 85.79\% & 87.25\% \\ Stacked LSTM-Based Model & 86.62\% & 81.97\% & 81.37\% \\ Conv1D-Based Model & 95.65\% & 93.44\% & 92.16\% \\ Conv1D and Stacked Bi-LSTM-Based Model & 94.96\% & 90.26\% & 92.16\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of five different classifiers for training, validation, and test dataset. based classifier is very much robust and gives the best output among all of these. The implementation and trained models of this research are publicly available at the GitHub ([[https://github.com/[PERSON]/Gender-Prediction-from-Bangla-Names](https://github.com/[PERSON]/Gender-Prediction-from-Bangla-Names)]([https://github.com/[PERSON]/Gender-Prediction-from-Bangla-Names](https://github.com/[PERSON]/Gender-Prediction-from-Bangla-Names)), accessed on 26 December 2022). Figure 6: (**a–e**) Fictorial representation of epochs versus the loss of different networks for training and validation dataset. In Section 2, we presented some related works. From this section, it is shown that there is no study available in the literature that classifies gender from Bangladesh names in the Bengali language. This is the first research to infer gender from Bangladesh names in the Bengali language using our own dataset. For this reason, it is difficult to compare our achieved results with previous works. We looked into the challenge of determining gender from a user's first name in this study. We suggested character-based machine learning models and proved that our models are able to infer the gender of users with much higher accuracy.