http://dissertations.umu.ac.ug/xmlui/handle/123456789/115 2026-04-07T16:16:11Z 2026-04-07T16:16:11Z John Paul, Kiberu http://dissertations.umu.ac.ug/xmlui/handle/123456789/1400 2025-03-03T06:24:10Z 2024-07-01T00:00:00Z

Addressing student hostel acquisition challenges John Paul, Kiberu Hostel management in higher institutions of learning involves numerous challenges, including allocation of rooms, tracking of residents (students), fee management, and ensuring the overall well-being of students. The primary goal of this project is to develop a comprehensive Hostel Information Management System (HIMS) that efficiently addresses these challenges, using Muteesa I Royal University as a case study. This study investigated the issues faced by hostel administrators, staff, and students in managing hostel-related activities. It highlighted how leveraging Information Technology could enhance the management of hostel records and processes. The project aimed at providing effective solutions for streamlining hostel operations, improving data accuracy, and facilitating better communication among stakeholders. Existing hostel management practices often rely on manual processes, which are prone to errors and inefficiencies. These traditional methods can lead to mismanagement of resources, delayed response to student needs, and difficulties in tracking payments and maintenance requests. The proposed HIMS offers a robust and scalable alternative that addresses these shortcomings. The system was developed using HTML and PHP for the front-end and MySQL for the database management. The development process included designing system architecture, user interfaces, and database schema to ensure seamless operation and user-friendly experience. Upon completion, the Hostel Information Management System for Muteesa I Royal University was successfully implemented. The system facilitates efficient room allocation, accurate fee management, and streamlined communication between hostel administrators and students, ultimately enhancing the overall hostel experience Najjemba Catherine; Najjemba Catherine

2024-07-01T00:00:00Z Derick, Mugabi http://dissertations.umu.ac.ug/xmlui/handle/123456789/1180 2025-01-20T06:51:02Z 2024-06-01T00:00:00Z

Web based cake ordering and reservation booking system for flam bakes Derick, Mugabi Ssemwezi Andrew; Ssemwezi Andrew

2024-06-01T00:00:00Z Bogere, Mark http://dissertations.umu.ac.ug/xmlui/handle/123456789/625 2024-04-09T08:39:01Z 2023-09-01T00:00:00Z

Improving stock price prediction using machine learning: a comparative study of LSTM, CNN and traditional methods. Bogere, Mark The stock market is known for its extreme complexity and volatility, and people are always looking for an accurate and effective way to guide stock trading. The accurate prediction of stock prices is of paramount importance in the financial market where returns and risks fluctuate wildly, and both financial institutions and regulatory authorities pay close attention to it. Stocks have always been favoured by investors as a method of asset allocation due to their higher returns. In recent years, researchers have been studying various methods to effectively predict stock market price and machine learning algorithms have emerged as one of the most promising techniques. This paper proposes different methods for predicting stock market prices using machine learning architectures, with a focus on identifying latent dynamics in the data. Traditional methods, such as artificial neural networks, are also explored. The objective of the project is to improve the quality of the output of stock market predictions by using stock value as a predictor. The paper presents a comparative study of machine learning architectures, including Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), and traditional methods like Arima, Random forest, logistic regression and K- nearest neighbors. The study analyzes historical stock data and compares the performance of each method based on various evaluation metrics. The results of the study demonstrate that LSTM and CNN outperform traditional methods in terms of accuracy, providing valuable insights for traders and investors. Overall, this paper contributes to the existing literature on stock price prediction by providing a comprehensive analysis of machine learning methods for accurate stock price prediction. By identifying the best-performing machine learning architecture, this study can help traders and investors make more informed decisions and minimize financial risks in the volatile stock market. Sanya Rahman; Kasozi Brian

2023-09-01T00:00:00Z