BRAC University Institutional Repository
Preserving Knowledge, Advancing Research, Sharing Scholarship
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
Detecting faulty machinery of waste water treatment plant using statistical analysis & machine learning
(Institute of Electrical and Electronics Engineers Inc., 2022-01-01) Ul Islam, Md. Mazed; Mondal, Joyanta Jyoti; Shihab I.F.; Department of Computer Science and Engineering
The goal of wastewater treatment is to eliminate contaminants from wastewater and convert them into effluent/discharge that can be reintroduced into the water cycle. In order to monitor, analyze plant performance, and decrease environmental pollution in wastewater treatment facilities, a model for fault detection must be developed. In this study, we examine different time and cost-efficient machine learning approaches to monitor the operation of a Waste Water Treatment Plant (WWTP) and identify plant faults as an alternative to human, laboratory-based time consuming, costly, and challenging techniques. This will allow us to develop a time and cost-efficient approach to detect such problems. To discover plant defects, we collect one year of unsupervised WWTP data and convert the data into supervised data. Using several machine learning algorithms based on water quality standard measurements (pH, BOD, COD, and suspended solid), we establish whether or not the data is valid.
LSTM-ANN based price hike sentiment analysis from Bangla social media comments
(Institute of Electrical and Electronics Engineers Inc., 2022-01-01) Chakraborty, Sovon; Uddin Talukdar, Muhammad Borahn; Yaseen Morshed Adib, Muhammed; Mitra, Sowmen; Rabiul Alam, Md. Golam; Department of Computer Science and Engineering
Price hike has always been a substantial concern for people all over the world. The crisis gets more conspicuous, and people find themselves more confounded when even the bare minimum of expenses still exceeds the amount they can get to earn. This tension tends to invite chaos in society as the number of people affected increases. Bangladesh is currently undergoing a formidable wave of price hikes. People have been expressing mixed reactions on social media regarding this issue. Hence, understanding the overall public sentiment can be crucial for policymaking and preventing chaos in society. This study utilizes social media comments for analyzing underlying sentiments. Data were collected from the Facebook pages of some popular Bangladeshi media for this purpose, and thereby a specialized dataset was constructed. The dataset contains 2000 public comments annotated with three polarity values- positive, negative, and neutral. A hybrid LSTM-ANN deep architecture has been exploited in this research. The model outperforms other state-of- the-art models in terms of less trainable parameters along with an F1-score of 88.47%.
A data-driven insight to enhancing stress management through chatbot interaction among undergraduate students
(Institute of Electrical and Electronics Engineers Inc., 2024-01-01) Zaman, Md. Mohibur; Rahman, Md. Rifat; Ur Rasul, Mehbub Shifat; Zaman, Md Showrav; Fahim, Md. Mohyminul Islam; Miami, Anika Tahsin; Sakif, Md. Sadiqul Islam; Noor, Jannatun; Department of Computer Science and Engineering
Student stress management at the undergraduate level is a significant issue in the educational world. Therefore, this stress is a challenging issue that needs to be dealt with. As higher educational requirements stack up and their challenges grow, most students struggle to keep the required balance between their studies and preparation. This study establishes a deeper investigation of stress management among undergraduate students using machine-learning algorithms to identify factors contributing to stress and provide solutions. The research aims to illuminate the fundamental causes and health implications of stress for students. Through surveys and questionnaires, the study categorizes stress stages, identifying patterns and enabling the use of new stress management strategies. The findings are used to address concerns shared by undergraduate students and determine interventions to help them cope effectively which aim to provide students with the strength, knowledge, resources, and support needed to manage stress effectively and live a fulfilling life during their university years and future years. The findings will be used to develop policies and programs backed by science to ensure emotional and academic success for students.
Interpretable deep learning approaches for reliable GI image classification: A study with the HyperKvasir dataset
(Institute of Electrical and Electronics Engineers Inc., 2024-01-01) Wahid, Saif Bin; Rothy, Zarin Tasnim; News, Raisul Kabir; Rieyan S.A.; Department of Computer Science and Engineering
Deep learning has emerged as a promising tool for automating gastrointestinal (GI) disease diagnosis. However, multi-class GI disease classification remains underexplored. This study addresses this gap by presenting a framework that uses advanced models like InceptionNetV3 and ResNet50, combined with boosting algorithms (XGB, LGBM), to classify lower GI abnormalities. InceptionNetV3 with XGB achieved the best recall of 0.81 and an F1 score of 0.90. To assist clinicians in understanding model decisions, the Grad-CAM technique, a form of explainable AI, was employed to highlight the critical regions influencing predictions, fostering trust in these systems. This approach significantly improves both the accuracy and reliability of GI disease diagnosis.
FAANG stock price prediction: A hybrid approach integrating deep learning with ensemble learning
(Institute of Electrical and Electronics Engineers Inc., 2024-01-01) Anis, Sadaf M.; Kabbya, Md Asif Shahidullah; Talukder, Anika Hasan; Chowdhury, Iffat Jahan; Hossain, Muhammad Iqbal; Department of Computer Science and Engineering
In this research, a novel hybrid approach has been proposed by implementing several well known deep learning models such as : Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), Autoencoder, Multilayer Perceptron (MLP), and Recurrent Neural Network (RNN) and integrating the deep learning models with ensemble learning techniques such as - Stacking Ensemble, Voting ensemble to a unified framework to predict stock prices of major FAANG companies dataset. Proposed ensemble learning techniques give better performance and accuracy than individual deep learning models. The stacking ensemble uses a meta-learner to combine predictions from individual deep learning models, while on the other hand voting ensemble averages the predictions. Stacking ensemble demonstrates consistent performance across datasets from major FAANG companies outperforming individual models and reducing the variability observed in standalone predictions. The research also employs Advanced Evaluation Metrics such as : R2 score and Mean Squared Logarithmic Error (MSLE) , F1 - score to evaluate performance providing nuanced insights into predictive accuracy for time-series data with exponential trends. Unlike traditional methods that rely only on a single model our proposed hybrid approach leverages complementary strengths of multiple architectures ensuring robustness and improved accuracy across datasets. By integrating predictions through a meta learner the proposed method achieves consistent performance, outperforming individual models. The novelty of this research lies in the comprehensive integration of multiple deep learning models and regularization techniques which ensure enhanced generalization and overcoming the inherent challenges in time-series financial forecasting across various datasets.