Bachelor of Science in Computer Science
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Item AI-Powered Robotics for Post-Disaster Survivor Detection and Rescue Path Mapping(Uganda Christian University, 2025-05-06) Diana NansubugaNatural and man-made disasters often highlight the weaknesses in traditional emergency response efforts, especially in countries with limited resources like Uganda. Challenges such as slow response times, difficulty reaching affected areas, and a lack of real-time information frequently result in lost lives and misused resources. This study introduces a robotic dog system enhanced by artificial intelligence, designed to improve the effectiveness of search and rescue missions following disasters. The system incorporates real-time human detection using AI, SLAM for creating environmental maps, and the A* algorithm to plan efficient rescue routes. The robot is powered by a Raspberry Pi and managed through a Flask-based web platform. It includes multiple sensors, such as GPS, ultrasonic detectors, a night vision camera, and directional microphones, allowing it to navigate independently, avoid obstacles, and communicate wirelessly with remote rescue teams. Testing the system in a controlled, disaster-like setting confirmed its ability to locate victims, generate accurate maps, and determine safe paths with high reliability. The results suggest that. This low-cost, locally adaptable solution could play a vital role in speeding up rescue efforts and minimising risks to human responders. By addressing key limitations in current practices, the project adds valuable insights to the field of disaster robotics, particularly In settings where advanced tools are not readily available.Item Gandabert: Transfer Learning With Mbert for Luganda News Classification(Uganda Christian University, 2025-05) Seth MbashaLuganda, spoken by over 21 million Ugandans, is significantly under‐resourced in Natural Language Processing (NLP), lacking effective tools like news classifiers. This gap hinders digital information access and contributes to the digital language divide. This research project addressed this challenge by developing GandaBERT, a model for Luganda news classification. The methodology involved fine‐tuning the multilingual BERT (mBERT) model on a novel multi‐source dataset comprising 2,609 native, translated, and synthetic Luganda news articles across five categories (Politics, Business, Sports, Health, Religion). Evaluation on a held‐out test set showed GandaBERT achieved an overall accuracy of 85.7%. While demonstrating strong performance in certain categories like Politics, challenges and variations across topics were observed, partly linked to overfitting during training. This study confirms the viability of applying transfer learning with mBERT for practical Luganda NLP tasks, provides a valuable classification tool, and contributes towards enhancing digital resources for this low‐resource language.Item An Embedded and Machine Learning Based Early Flood Monitoring and Warning System, the Case of River Manafwa(Uganda Christian University, 2025-05-06) Daniel Lukyamuzi WavamunnoFlooding remains a serious threat in many parts of Uganda, especially in regions with limited access to early warning systems. This project introduces a practical solution that combines embedded hardware and machine learning to monitor and predict flood events in real time. Using a flow sensor and an ultrasonic sensor connected to an ESP32 device, the system captures data on water movement and levels. These readings are automatically logged to Google Sheets, allowing for easy data management and access. A backend built with FastAPI processes this information, using a trained Random Forest algorithm to forecast potential flood risks. The results, along with past records, are displayed on an interactive dashboard developed in React. By merging simple electronics with predictive analytics, the system provides an affordable and adaptable tool to support timely flood response efforts in vulnerable areas.Item AI Image-based System for Lumpy Skin Disease Detection in Cattle(Uganda Christian University, 2025) Amos MugabiLumpy Skin Disease (LSD) remains a significant threat to cattle health across Uganda, with conventional disease detection methods being slow, centralized, and reliant on clinical expertise that is often unavailable in field settings. This project proposes an innovative solution through an AI-powered, image-based detection system capable of identifying LSD from cattle images. The system employs a twostage deep learning architecture: a YOLOv8 object detection model locates individual cattle within images, followed by a convolutional neural network (CNN) that classifies each animal as either healthy or infected based on visible skin lesions. Trained on a diverse dataset of annotated cattle images, the integrated model achieved a high detection precision and classification accuracy, demonstrating strong reliability in recognizing signs of LSD. Furthermore, the system offers real-time feedback via an interactive web interface, enabling farmers and veterinary personnel to quickly assess cattle health with images. This approach not only enhances detection and control measures but also sets the stage for broader adoption of AI in livestock health management within low-resource environments. The system’s design aligns with global goals of smart agriculture, offering a scalable tool that supports both food security and disease resilience.Item SMART-AG : A Precision Agriculture AI-Powered Edge Computing System(Uganda Christian University, 2025-05-06) Totit Kabuya BushenyulaFood insecurity is a critical issue in Africa, with over 296 million people affected by hunger. Despite efforts to increase food production, food loss remains a major contributor to this crisis, particularly in low-income countries where inefficiencies in farming and post-harvest handling are common. While 42% of Africa's workforce is employed in agriculture, there is a low adoption of modern agricultural technologies, primarily due to lack of internet access, technical skills, and high costs. This results in many farmers continuing to rely on traditional methods, which limits their productivity and exacerbates food insecurity. Existing solutions to improve farming practices are often too complex or require constant internet access, leaving many farmers unable to benefit from them. Therefore, a practical, affordable, and internet-independent solution is needed to help farmers increase yields and reduce food losses. This project proposes SMART-AG, an AI-powered edge computing system that provides actionable farming advice via SMS without requiring internet access. SMART-AG aims to empower farmers with insights on soil health, crop selection, and nutrient management, improving productivity and contributing to food security in Africa.Item Potato Disease Diagnosis Using YOLOv5 and Web-Based Deployment(Uganda Christian University, 2025-05-06) James Alala MunjwokAgriculture plays a central role in Uganda’s economy, and potatoes are among the most important staple crops. However, potato yields are significantly threatened by diseases such as early blight and late blight. Timely detection of these diseases is critical to reduce losses, minimize pesticide misuse, and enhance food security. This project presents a web-based potato leaf disease diagnosis system using YOLOv5, a state-of-the-art deep learning object detection model. The system classifies potato leaves as healthy, early blight, or late blight. The backend is implemented using FastAPI and deployed to Render, while the frontend is built in React and hosted on Vercel, ensuring accessibility via modern web browsers. The model was trained on the PlantVillage dataset. Evaluation results show that the system achieved high accuracy and fast inference times, making it suitable for use by farmers and agricultural officers. This report details the system design, methodology, model training, deployment, and perfor- mance evaluation. Limitations such as environmental noise, internet dependency, and limited disease coverage are acknowledged, and future work includes expanding the disease scope, offline deployment, and integrating treatment recommendations. This work contributes to the growing field of AI-powered agriculture in Uganda and aligns with the Sustainable Development Goals for food security and smart farming.Item A Digital Predictive Healthcare Management System for Sickle Cell Disease Using Machine Learning and Data Visualization Techniques: A Case Study of Uganda(Uganda Christian University, 2025-05-06) Tirza AtwiineThis project presents the design and development of a Digital Predictive Healthcare Management System for Sickle Cell Disease (SCD) using Machine Learning and Data Visualization techniques, with a focus on Uganda. Sickle Cell Disease remains a significant public health challenge in Uganda, with limited access to timely diagnosis, treatment monitoring, and personalized care. The proposed system leverages machine learning algorithms; Random Forest Classifier and LSTM(Long-Short-Term-Memory), to predict potential health risks, analyse and predict future patient data, and support early interventions. Interactive dashboards and visual tools created using React provide healthcare professionals and patients with actionable insights for better disease management. This project aims to enhance decision-making, improve patient outcomes, and support national efforts in digital health transformation, particularly in under-resourced settings.Item A Machine Learning Based Web Application for Pre-Eclampsia Risk Prediction, Awareness and Management(Uganda Christian University, 2025-05-06) Angela Nina Twine MukiizaPre-eclampsia is a critical condition affecting pregnant women that is characterized by high blood pressure and potential damage to vital organs. This research focuses on developing a machine learning-based web application designed to predict the risk of pre-eclampsia, enhance awareness and provide management strategies. Utilizing patient data, the application aims to offer accurate predictions and a recommendation. The project involves data collection, model training and application deployment emphasizing the integration of user- friendly interfaces and real-time data processing. The research underscores the importance of early detection and intervention potentially reducing the adverse outcomes associated with pre-eclampsia. By leveraging machine learning algorithms and web technologies, this application aspires to empower healthcare providers and expectant mothers with actionable insights fostering better health outcomes and informed decision-making. This work represents a significant stride towards improving maternal health care through innovative technological solutions.Item For the Sake of Food: A Comprehensive Nutrition Platform for Promoting Healthier Lifestyles in Uganda(Uganda Christian University, 2024-05-10) Aguma Destiny Kampumure; Tracy Majorie Najjoba; Rochelle Katukunda; Moses AyebareThis document provides a comprehensive overview of the development process behind the "For the Sake of Food" nutrition recipe web application. For the Sake of Food’s primary aim was to empower individuals to make healthier dietary choices by offering convenient access to nutritious recipes tailored to their preferences. For the Sake of Food embarked on a methodical journey, employing a variety of research techniques to gain insights into user needs and behaviors. Through interviews and surveys, For the Sake of Food delved into the diverse dietary habits and technological proficiency levels of our target audience, ensuring the app's design catered to a broad spectrum of users. User testing played a pivotal role in refining the application's usability and functionality, with feedback from real users guiding iterative improvements. The team’s findings illuminated key user demographics, dietary preferences, and technological inclinations, informing the development of features such as personalized recipe recommendations and intuitive interfaces. Ethical considerations remained paramount throughout the project, with stringent measures in place to safeguard user privacy and ensure data security. “For The Sake of Food” also acknowledged inherent limitations, such as sample representativeness and resource constraints, which shaped the scope and depth of our research efforts. Ultimately, the systematic approach culminated in the creation of an application poised to positively impact users' dietary habits and overall well-being. By harnessing technology to promote healthier lifestyles, we endeavor to contribute to a healthier society.Item Project MindPeace Report(Uganda Christian University, 2024-04-26) Isaiah Mukisa ; Desire Namanya; Soul Solomon Sekamatte ; Peter Paul Jamugisa ; Conrad William Mabira1 in 5 people experience mental health challenges, the prevalence of mental health issues globally remains a pressing concern, with a significant proportion of affected individuals not receiving the necessary treatment due to barriers such as stigma, cost, and a shortage of skilled professionals. This report presents the MindPeace project, an online application designed to address these challenges by providing a user-friendly platform that connects working-class individuals aged 20 to 65 with skilled mental health practitioners. MindPeace aims to bridge the gap in access to mental health care, focusing on specific demographics and offering an alternative to informal support and self-medication. The project encompasses a comprehensive suite of features, including booking of sessions, an emergency helpline for crisis support, and a compatibility quiz for personalized counseling experiences. By leveraging technologies such as Cal.com integration for efficient appointment management, secure video chat for confidential sessions, and a robust emergency response system, MindPeace seeks to reduce the reliance on self-medication and improve mental health outcomes for its users. This report outlines the project's objectives, scope, and the functionalities it offers, highlighting its potential to significantly impact the accessibility and quality of mental health care for underserved populations.Item Pillpop Final Year Group Project Report(Uganda Christian University, 2024-05-10) Sarah Nsereko ; Daphine Kamusiime; Nkata Joshua Luyombya; Mukisa Hassan Bahati; Kasagga Gordon Kimera; Allan Smith NiringiyeA web application aimed at reminding users to take their medications which is a major problem in Uganda also called medication non-adherence called Pillpop was developed to solve this problem with additional features to further solve this problem, The application was developed due to a recent study on the health sector, which displayed that medication non-adherence is one of the problems troubling the health sector. Moreover, the problem was reflected in my lifestyle when it came to taking medications. The development of the application was done based on using email as a format of reminding users and all the other features within rotated based on the user’s data. Using different technologies e.g Nextjs and Django to develop the application, which relies on the data storage or backend a lot, and displays through the frontend hence the need for an interface that is easy to navigate for the users. During the application development, a lot of hostility was found especially when it came to collecting data and the effectiveness of reminding a user which is hard to achieve 100% because it all draws back to user integrity and I found that it not possible to solve all the problems within the one application but instead one problem at a time is better. The significance of the findings is that investment in technology can help or improve the efficiency of health since the digital generation/age is now the common or the norm meaning with the help of technology, the health of the users can improve, and it also a call to find ways of utilizing the available technology to improve one’s life quality .Item Help Annonymous Project Report(Uganda Christian University, 2024-04-26) Joshua Jasper Ashaba ; Nahum Okello ; Nabil Sengooba ; Gensi Collin Ikiriza; Karongo Keron Kansiime ; Elizabeth AmandaNamaleThe "Help Anonymous" project, developed by a team from Uganda Christian University, represents a transformative approach to mental health support. Our application creates a safe space for anonymous dialogue, offering peer support, professional advice, and educational resources, thereby making mental health care accessible to a broader audience. This community-driven platform has shown significant user engagement and an increase in mental health awareness, demonstrating the effectiveness of our strategies. As a collective, we are proud to contribute to the United Nations' Sustainable Development Goals, specifically SDG 3 (Good Health and Well-being) and SDG 10 (Reduced Inequalities), by providing a platform that fosters open conversations on mental health and supports inclusivity. Our project underscores the importance of community and technology in breaking down barriers to mental health care and destigmatizing the pursuit of help. Moving forward, we are committed to enhancing our platform's reach and impact, driving positive change in the realm of mental health.Item Automated Diagnosis and Classification of Coffee Plant Diseases Using Deep Learning(Uganda Christian University, 2023-05-08) Isaac Arou Mayol; Wilson Nyumbe; Momodou Jallow; Brian Mung’oka MutuaThis project addresses the critical challenge faced by coffee farmers in the early detection and management of diseases affecting coffee plants. Late identification often leads to significant crop loss, exacerbating poverty and food insecurity among farmers. The project: the Automated Diagnosis and Classification of Coffee Plant Diseases Using Deep Learning, provided an innovative solution. This solution utilizes a mobile application equipped with a trained deep learning model to enable farmers to detect diseases in their coffee crops early on. Through the use of image recognition technology, the application, powered by a deep learning model of 95.83% accuracy score, is able to accurately identify and classify diseases, empowering farmers to take timely and appropriate measures to prevent the spread of diseases and minimize crop loss. The project helps mitigate economic loss, promote responsible pesticide use, and improve the livelihoods of coffee farmers. By leveraging technology to address a pressing agricultural issue, this project contributes to sustainable farming practices and food security in coffee-producing regions.