Bachelor of Science in Computer Science

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    Interpretable Bangla fake news classification using BERT and traditional machine learning approaches
    (BRAC University, 9/29/2022) Anan, Ramisa; Modhu, Elizabeth Antora; Suter, Arjun; Sneha, Ifrit Jamal; Rasel, Annajiat Alim; Abdullah, Matin Saad; Mostakim, Moin
    Fake news is a type of content that is inaccurate or misleading and it is usually published with the intention of damaging a person or organization’s reputation. It has recently grown significantly in the online forum and on social media platform like Facebook, Reddit, Twitter etc. Because of its falsified statements, people are often persuaded by false news, which has serious consequences in the real world. As a result, there is a growing interest in the field of fake news identification, even though the majority of fake news identification studies are for English language whereas just few of them are for Bangla language. In our study, we come up with a BERT-based system that uses Stratified K-fold cross validation that can achieve 98.45% test accuracy, whereas only the Random Forest can achieve 86.83% accuracy among all the traditional machine learning models. Furthermore, we used Local Interpretable Model-Agnostic Explanations to provide explainability to our system. In this research, we have used the existing BanFakeNews dataset to identify Bangla Fake News. The primary focus of this paper is to develop a model that can recognize fake news in natural language processing so that the developed model can decrease the time it takes individuals to extract fake news from social media.
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    Virtual teaching assistant for undergraduate students using natural language processing & deep learning
    (BRAC University, 2022-05) Sakib, Sadman Jashim; Joy, Baktiar Kabir; Rydha, Zahin; Nuruzzaman, Md.; Anik, Khaled Ahmmed; Rasel, Annajiat Alim; Abdullah, Matin Saad
    Online education’s popularity has been continuously increasing over the past few years. Many universities were forced to switch to online education as a result of COVID-19. In many cases, even after more than two years of online instruction, colleges were unable to resume their traditional classroom programs. A growing number of institutions are considering a hybrid approach to education, in which some face-to-face teaching is augmented with online learning. Nevertheless, many online education systems are inefficient, and this results in a poor rate of student retention. In this paper, we are offering a primary dataset, a virtual teaching assistant named VTA-bot, and its system architecture. In addition, we are showing a first implementation of the suggested system, which consists of a chatbot that can be queried about the content and topics of the ‘Programming Language I’ course, an introductory programming language course offered by the CSE department of Brac University. Students in their first year of university will benefit from this strategy, which aims to increase student participation and involvement in online education.