Projects


I take immense pride in the various projects I have undertaken during my academic career. These projects not only showcase my skills and expertise but also highlight my passion for applying theoretical knowledge to real-world challenges. Some notable projects include are as follows:



Automated Water Tank Level Indicator - BS, Semester Based Project

In this project, I conceptualized and developed an automated system with the primary function of monitoring and displaying the water level within a tank. The key objectives were to introduce efficiency, convenience, and sustainability into water usage. By automating the monitoring process, we aimed to achieve two important outcomes: the conservation of water resources and the reduction of energy consumption.


Library Management Application - BS, Semester Based Project

The Library Management Desktop Application was meticulously crafted to revolutionize the way libraries operate and manage their resources. At its core, this application seamlessly integrated a robust SQL database system with an intuitive Java user interface powered by JavaFX, creating a powerful tool that elevated library management to new heights.


Interactive Student Discussion Platform - BS, Semester Based Project

The Interactive Student Discussion Platform served as a digital space where students could engage in meaningful discourse, share knowledge, and exchange ideas. This innovative application harnessed the power of Java programming in conjunction with Oracle databases to create a robust and user-friendly tool for educational interaction.


Next Point-of-Interest Recommendation using Large Bipartite Networks - BS, Final Year Research

My FYP thesis was dedicated to the creation of an innovative recommendation system designed to enhance the user experience by providing personalized and context-aware POI recommendations. Leveraging the power of graph embedding, our proposed model exhibited substantial advancements in accuracy and performance when compared to existing methodologies.


Multi-Feature Point-of-Interest Recommendation using Graph Neural Network - M.Phil, Final Year Research

My work delves into the intricacies of POI recommendations, recognizing that the quality of recommendations is significantly enhanced by considering associated features. These features encompass a wide range of contextual information, including user preferences, historical interactions, location data, and more. My research aimed to harness this wealth of information to provide highly accurate and context-aware POI recommendations.