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Gender Prediction Based on various Nationality Names using Deep Learning techniques

Kumar, Deepak (2023) Gender Prediction Based on various Nationality Names using Deep Learning techniques. Masters thesis, Dublin, National College of Ireland.

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Gender Prediction task has been carried out in this paper for various nationality names. Gender prediction by name was not a simple task to implement. Gender is an attribute of demographic data of individuals which plays a significant role in identifying patterns and analysing the industrial, business, and medical data to plan the strategies for the market. Previously, many authors have carried out work in the same domain to predict gender based on the name of different languages and different nationalities. However, this paper also deals with gender prediction of different nationalities using text classification which is different from others as no one has used the sequence classification technique to predict gender. In this paper, the name of individuals has been considered and classified using different sequence classification technique and trained by multiple deep learning techniques. An open-source dataset acquired from UCI is used for text classification. DistilBERT, BERT, RoBERTa and XLNet are the four model which has to implement in this paper for analysis of prediction and comparison proved DistilBERT model as the most efficient model based on results obtained by computing metrics and time.

Item Type: Thesis (Masters)
Nayak, Prashanth
Uncontrolled Keywords: Gender Prediction; Deep Learning; DistilBERT; Multi-Class Text Classification
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
H Social Sciences > HF Commerce > Marketing
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 19 May 2023 15:11
Last Modified: 19 May 2023 15:11

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