BRAC University Institutional Repository
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A digital platform for collecting, preserving, and sharing BRAC University’s scholarly, academic, and institutional outputs.
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Faculty members and students are invited to submit their research publications, theses, dissertations, and scholarly works to increase visibility, access, and long-term preservation.
Recent Submissions
A machine learning approach to predict movie success from youtube trailer comments
(Institute of Electrical and Electronics Engineers Inc., 2022-01-01) Khan, Farden Ehsan; Ruhan, Ahmed Mahir; Shamsuddin, Rifat; Ashraf, Faisal Bin; Department of Computer Science and Engineering
Social media use has increased to such levels in recent years that it has transformed into a trend-setting powerhouse, introducing subjects that would have previously remained outside of the public eye. Through people's shared opinions and responses about a trend on social media, we hope to determine how long it can hold an audience's attention on its own. We will analyze the sentiment of individuals toward a particular topic using the information gleaned from social media comments. Our work will be based on unreleased films and make predictions about how they will turn out when they are released. In this work, we have processed and examined accumulated reviews about a film to see whether the general public feels positively or negatively about it and to calculate the likelihood that a certain film will be a success. From this, we can infer how the success of a movie or product is influenced by both positive and negative attention before its release.
Ethics and methods for collecting sensitive data: Examining sexual and reproductive health needs of and services for Rohingya refugees at Cox’s bazar, Bangladesh
(University of Hawaii at Manoa, 2020-01-01) Aktar, Bachera; Ahmed, Rushdia; Hassan, Raafat; Farnaz, Nadia; Ray, Pushpita; Awal, Abdul; Shafique, Sharid Bin; Hasan, Md Tanvir; Quayyum, Zahidul; Jafarovna M.B.; Kobeissi L.H.; El Tahir K.; Chawla B.S.; Rashid S.F.; BRAC James P Grant School of Public Health
During humanitarian emergencies, such as the forced displacement of the Rohingya diaspora, women and adolescent girls become highly vulnerable to sexual and reproductive health (SRH) issues and abuse. Although sensitive in nature, community-driven information is essential for designing and delivering effective community-centric SRH services. This article provides an overview of the theoretical framework and methodologies used to investigate SRH needs, barriers, and challenges in service-delivery and utilization in the Rohingya refugee camps in Cox’s Bazar, Bangladesh. It also offers insights on important methodological and ethical factors to consider while conducting research in a similar context. A concurrent mixed-method study was undertaken in ten randomly selected Rohingya refugee camps between July and November 2018. The design consisted of a cross-sectional household survey of 403 Rohingya adolescent girls and women, along with an assessment of 29 healthcare facilities. The team also completed in-depth interviews with nine adolescent girls, 10 women, nine formal and nine informal healthcare providers, key informant interviews with seven key stakeholders and seven influential community members. Lastly, three focus group discussions were undertaken with a group of 18 Rohingya men. Our theoretical framework drew from the socio-ecological models developed by Karl Blanchet and colleagues (2017) insofar as they considered a multiplicity of related contextual and cross-cutting factors. Building good rapport with community gatekeepers was key in accessing and sustaining the relationship with the various respondents. The data collected through such context-specific research approaches is critical in designing community-centric service-delivery mechanisms, and culturally and gender-sensitive SRH interventions in humanitarian crises.
IoT network attack detection using XAI and reliability analysis
(Institute of Electrical and Electronics Engineers Inc., 2022-01-01) Tabassum, Sabrina; Parvin, Nazia; Hossain, Nigah; Tasnim, Anika; Rahman, Rafeed; Hossain, Muhammad Iqbal; Department of Computer Science and Engineering
IoT has emerged as one of the most sophisticated techniques in recent years. But inadequate security controls are the most typical barrier to IoT expansion as the devices transmit a huge amount of data. Nowadays different machine learning and deep learning models are used to detect various IoT attacks. In our previous research, we applied Decision Tree, Random Forest, AdaBoost, XGBoost, ANN, and MLP to the IoT/IIoT dataset of TON IoT datasets to classify IoT network attacks. In binary classification, we got above 96% accuracy for all methods. In contrast, AdaBoost and ANN underperformed in multiclass classification. As accuracy improves, models get more complicated, and these models are frequently seen as black boxes that are difficult to interpret. Though these models give highly precise results, an explanation is required in order to comprehend and accept the models' decisions. Here comes XAI which emphasizes a variety of ways for breaking the black-box nature of Machine Learning and Deep Learning models as well as delivering human-level explanations. In this article, we have extended our work by analyzing different machine learning and deep learning methodologies using XAI to explain the categorization of IoT network attacks. LIME, SHAP, and ELI5 approaches have been used to interpret and explain which will increase transparency and reliability.
Mitigating the ready-made garment sector’s Covid-19 crisis in Bangladesh: Protecting workers or the interests of the industry?
(Helsinki University Press, 2026-01-01) Sultan, Maheen; Antara, Iffat Jahan; Islam T.
Bangladesh’s ready-made garments (RMG) sector is the country’s primary export industry and the largest source of foreign currency, employing around 2.59 million workers. However, its exposure to global volatility and crises was vividly demonstrated during the COVID-19 pandemic. Faced with order cancellations, factory closures, and widespread unemployment, the government swiftly responded with a financial stimulus package to cover workers’ wages. Despite these efforts, RMG workers experienced job losses, reduced incomes, health risks, and various other insecurities. We take a political economy lens to explore the interests, incentives, and relative power of various stakeholders. The chapter seeks to understand the allocation and implementation of the financial stimulus packages, examining who gained and who lost, and why. It discusses health and livelihood risks confronting workers and the challenges of retaining employment, safeguarding earnings, and having their voices heard. We undertake an analysis of policy responses and their accomplishments and challenges. The chapter draws upon interview data, ongoing media monitoring, and secondary literature related to labour rights and the RMG sector. It concludes with a discussion of the implications for workers’ welfare and rights in the post-pandemic economic landscape, where Bangladesh, much like other South Asian economies, remains susceptible to potential global economic downturns.
Smartphone sensor-based human activity recognition using LSTM networks: Development and implementation in android applications
(Institute of Electrical and Electronics Engineers Inc., 2024-01-01) Sikder, Debabrata; Rafin, Nafiz Imtiaz; Alam, Md. Golam Rabiul; Uddin M.Z.; Department of Computer Science and Engineering
The machine learning approach to estimate human activity using smartphone sensor data is challenging. In this work, a Human Activity Recognition (HAR) approach is conducted based on the Long Short-Term Memory (LSTM) model, which can recognize six different behaviors: Downstairs, Jogging, Sitting, Standing, Upstairs, and Walking. To achieve the best potential result, various machine learning and statistical approaches were explored. The LSTM model, known for its effectiveness in sequence prediction, was chosen for its ability to run efficiently on lightweight edge devices such as smartphones. This model achieved a test accuracy of 97%. Finally, the model was exported and deployed in an Android application, providing a user-friendly interface.