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 Bangladeshi unique names. We used both convolutional neural network (CNN)- and recurrent neural network (RNN)-based deep learning methods to infer gender from the Bangladeshi 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.

A Fine-Tuned Hybrid Stacked CNN to Improve Bengali Handwritten Digit Recognition

“…Finally, to classify our desired digit, we set the softmax activation function ŷ = f (z i ) ∑ 10 j=1 f (z i ) as an output layer [12]. Optimizer: In this research, we employed the Adam (Adaptive Moment Estimation) optimization algorithm, which automatically updates the model parameter based on computing the exponential moving average of the gradient and squared gradient [39].…”

Prediksi Gender Berdasarkan Nama Bahasa Indonesia Menggunakan Long Short Term Memory

“…Terdapat berbagai metode yang dapat digunakan untuk memprediksi jenis kelamin berdasarkan nama, seperti metode Long Short Term Memory (LSTM) dan Machine Learning [2], [3]Meskipun masalah ini terlihat sederhana, namun prediksi gender dari nama dapat menjadi sulit karena adanya ambiguitas dalam nama dan bahasa yang digunakan [4]. Sebagai solusinya, metode LSTM telah terbukti efektif dalam menangani masalah prediksi jenis kelamin dari nama dalam Bahasa Inggris [5], [6] dan bahasa lainnya [7], [8], [9], [10], [11]. Akan tetapi, masih terbatas penelitian yang memprediksi gender dari nama dalam Bahasa Indonesia menggunakan metode LSTM.…”

“…Beberapa penelitian terbaru telah menggunakan metode LSTM untuk memprediksi gender berdasarkan nama dalam berbagai bahasa seperti Bahasa China [7], Prancis [8], Inggris [9], Brazil [10], dan India [11]. Namun, masih sedikit penelitian yang telah dilakukan pada prediksi gender berdasarkan nama Bahasa Indonesia menggunakan LSTM [2], [16].…”

Be Aware of Your Text Messages: Fraud Attempts Identification Based on Semantic Sequential Learning for Financial Transactions through Mobile Services in Bangladesh

“…In the context of Bengali natural language processing (NLP), several approaches including deep learning (DL) techniques have proved their capabilities in several fields, such as handwritten Bengali text detection [26][27][28][29][30], using Deep learning Identification of Bangla overlaid text on videos [31][32][33], hand sign language to text using Neural Network [34], Automatic Speech Recognition [35][36][37][38], Machine Translation [39][40][41], Text Categorization [42][43][44] and Bangla Emotion Detection [44][45][46][47]. These are some works relevant to our methodology to propose a novel study on Bangladesh Bkash financial transaction awareness by detecting fraudulent texts using ML and DL.…”