# Gender Recognition of Bangla Names Using Deep Learning Approaches
###### Abstract  
The name of individuals has a specific meaning and great significance. Individuals' names generally have substantial gender differences, and explicitly, Bengali names usually have a solid sexual identity. We can determine if a stranger is a man or a woman based on their name with remarkably suitable precision. In this research, we primarily conducted a thorough investigation into gender prediction based on a person's name using DL-based methods. While various techniques have been explored for the English language, there has been little progress in the Bengali language. We address this gap by presenting a large-scale experiment with 2030 Bangladesni unique names. We used both convolutional neural network (CNN)- and recurrent neural network (RNN)-based deep learning methods to infer gender from the Bangladesni names in the Bengali language. We presented the one-dimensional CNN (Conv1D), simple long short-term memory (LSTM), bidirectional LSTM, stacked LSTM, and combined Conv1D and stacked bidirectional LSTM-based models and evaluated the performance of each scheme using our own dataset. Experimental results are analyzed on the basis of accuracy, precision, recall, F1-score, ROC AUC score, and loss performance metrics. The performance evaluative results show that Conv1D outperforms with 91.18% accuracy, which is likely to improve as the size of the training data grows. NLP; gender prediction; gender classification; feature extraction; deep learning; LSTM; CNN +
Footnote †: journal: Medical  
been studying and exploring the automated identification of human names [9; 10; 11; 12]. It is known from the literature reviewing that many researchers of academic institutions and industries have been working on NLP using AI, ML, and DL-based algorithms, but to our knowledge, we did not find many research works on gender inference from names. The majority of previous works available in the literature have focused on English, ignoring other languages such as Bangla. The currently available software for determining the sexual identity of people uses dictionary lookup algorithms. Currently, there is no freely available system/method to determine the sexual identity from Bangla names for research, industrial, or other uses. Many attempts are also being made to make the Bangla language more accessible on the web and in technological areas. CNN- and LSTM-based classification approaches find use in a large number of NLP tasks and other machine learning applications specifically for text and time sequence data [13; 14; 15; 16]. Our main contributions are Bangla name data collection, cleaning and preprocessing of collected data, preparation of a suitable dataset of Bangla names, an in-depth inspection of different word-level characteristics of Bangladesh names in the Bengali language that differentiate between the two sexes, identification of the attributes that are very useful in classification, and presentation of a state-of-the-art gender identification technique based on CNN and LSTM. This research is mostly concerned with the gender recognition of Bengali names. In this paper, we built Conv1D, LSTM, bidirectional LSTM, stacked LSTM, and combined Conv1D and stacked bidirectional LSTM-based gender classifier and critically evaluated the performance of each classifier using our prepared own dataset through the analysis of the values of accuracy, precision, recall, F1-score, ROC AUC score, and loss metrics. The following is a breakdown of the paper's structure. The related works will be covered in the second section, details of dataset preparation are mentioned in the third section, the proposed framework for the development of the gender classifier is presented in Section 4, and the description of the DNN-based experimental models will be introduced in the fifth part. The sixth section of this article will discuss the experimental setup and procedure, and analysis of the outcomes of the developed DNN-based gender classifiers will be presented in Section 7.