Browsing by Author "Ahmed, Tausif"

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A Web-based Solution for Searching Suitable Accommodation for People of All Professions: Find Home BD
(Daffodil International University, 2024-07-24) Ahmed, Tausif
In Bangladesh, finding rental accommodations is often challenging due to outdated methods and a fragmented rental market. "Find Home BD" addresses these issues with an innovative online platform that connects renters with ideal homes and allows property owners to showcase their properties effectively. Central to "Find Home BD" is a sophisticated AI-driven recommendation system that provides personalized property suggestionstailored to each user's unique preferences. Thissystem is enhanced by integrated live chat functionalities, enabling transparent and efficient communication between renters and property owners, thereby improving the rental process. The platform's user-centric design ensures an intuitive and engaging interface, making navigation straightforward and user-friendly. The AI recommendation engine analyzes user behavior and preferences to present the most relevant property options, increasing the likelihood of finding a suitable match quickly. The live chat feature not only speeds up communication but also builds trust between parties by facilitating direct interaction, addressing queries, and negotiating terms in real-time. By focusing on efficiency, transparency, and accessibility, "Find Home BD" aims to revolutionize the rental housing market in Bangladesh. It seeks to eliminate the inefficiencies and frustrations of traditional rental processes, offering a modern, streamlined solution that benefits both renters and property owners, ultimately redefining how rental accommodations are sought and secured.
Analysis of the impact of online education using EEG signals and machine learning algorithms
(BRAC University, 2021-01) Hoque, Ehsanul; Ahmed, Tausif; Shabab, Mohammad Adituzzaman; Bakhtier, Tahsin Mohammad; Abdullah, Sayeem Md; Chakrabarty, Amitabha
Online learning has allowed students from different walks of life to access a vast amount of information, allowing them to gain new skills. However, only having access to that information does not mean that the students will comprehend it. In this report, we study the impact of online education on students, specifically their confusion levels. The dataset that we have used in this report was taken from Kaggle. The dataset consists of mostly preprocessed Electroencephalogram (EEG) brain wave values i.e., Attention, Mediation, Raw, Delta, Theta, Alpha, Beta, and Gamma. Due to the limitations of the dataset, the accuracies of the Machine Learning models when only using EEG signal values were not satisfactory. Therefore, later into our research, we have decided to modify our dataset in order to better determine the confusion level of students. We have synthesized the dataset taken from Kaggle to form another dataset, where we took the content being viewed into account which led to better classification. The Machine Learning Algorithms that we have implemented in this paper are Decision Tree, Random Forest, Bagging with Random Forest, Gaussian Naive Bayes, K-Nearest Neighbors, Gradient Boosting, XGBoost, and Bidirectional-LSTM. For the dataset which consists of only EEG signal values, Bagging with Random Forest algorithm performed the best. It was able to predict whether or not a student was confused with an accuracy of 67.3%, while in the modified dataset, Bidirectional-LSTM had the highest accuracy of 80.9%. For both of the datasets, Gaussian Naive Bayes performed the worst with an accuracy of 59.2% and 63.6%, respectively.
Impact of Capital Adequacy on Bank’s Profitability: An Empirical Analysis on Rupali Bank PLC.” Submitted in partial fulfillment for the degree of Masters of Business Administration
(Comilla University, 26-May-2025) Ahmed, Tausif
This internship report examines the empirical relationship between capital adequacy and profitability at Rupali Bank PLC, a state-owned commercial bank in Bangladesh. Anchored in the Basel III regulatory framework, the study evaluates how key capital components—such as the Capital Adequacy Ratio (CAR), Tier 1 Capital Ratio, Common Equity Tier 1 (CET1), Capital Conservation Buffer (CCB), leverage ratio, risk-weighted assets (RWA), and the nonperforming loan (NPL) ratio—affect the bank's financial performance, measured by Return on Assets (ROA) and Return on Equity (ROE).
IoT based smart biofloc monitoring and maintenance system
(Brac University, 2022-09) Podder, Saurov; Anoy, Mohammad Fahim Sultan; Ahmed, Tausif; Jesan, Syed Nafew Hasan; Azad, AKM Abdul Malek; Mohsin, Abu S.M.; Rahman, Mosaddequr
Water quality has a great impact on the quality and quantity of fish production, and since Biofloc systems are designed for utilizing fish waste with the help of microorganisms. Hence, water quality monitoring and management (is vital in improving production quality and efficiency of Biofloc technologies). In this project, we developed an internet of things (IoT) system that gathers relevant information of the Biofloc environment with the help of sensors, this data is stored and processed in a cloud server, and relevant actuators are controlled for maintaining a suitable water quality. In addition, a mobile application is developed for providing a real time monitoring system.