School of Computing and Informatics Technology Collection
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ItemInterpretable foot and mouth disease detection(Makerere University, 2025)Foot and Mouth Disease (FMD) is a highly contagious viral infection that hinderslivestock production, resulting in severe economic losses in Uganda[21]. This study investigates an interpretable machine learning approach to FMD detection by using two distinct datasets: a numerical dataset sourced from Uganda for early detection, and an image based dataset collected from the internet for visual diagnosis. Exploratory Data Analysis (EDA) was conducted to assess feature distributions, identify class imbalances, and uncover correlations among epidemiological and environmental factors such as rainfall, temperature, livestock density, and geographic proximity to national parks and borders[5]. A total of six models were developed—four trained on the numerical dataset and two on the image dataset. For early detection, models including Random Forest, Support Vector Machine (SVM), Logistic Regression, and Gradient Boosting were evaluated. The XGBoost model, when combined with the Synthetic Minority Oversampling Technique (SMOTE), achieved the highest accuracy of 82%. However, performance decreased in scenarios involving dynamic or imbalanced data distributions, underscoring the need for adaptive learning strategies.[15] In the image-based classification task, deep learning models comprising a custom Convolutional Neural Network (CNN) and ResNet50 were implemented. Among these, ResNet50 achieved the highest accuracy of 97%, demonstrating strong potential for visual FMD symptom detection. To enhance transparency and model trustworthiness, SHAP was employed to explain feature importance in numerical models, while Grad-CAM was used to generate class activation maps for CNN-based image models[14]. This report emphasizes the value of integrating explainable artificial intelligence (XAI) and adaptive machine learning in livestock disease diagnostics. The proposed approach provides a foundation for developing robust, data-driven decision support systems to strengthen early warning and surveillance mechanisms for FMD in Uganda.
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ItemMobile-based poultry product sales and marketing application (MPPSAMA)( 2024)Poultry farming, a critical component of Uganda's agricultural sector, significantly contributes to economic development and food security. Despite its importance, the poultry industry faces challenges, including inefficient market coordination and limited access to profitable markets. This study proposes the development of a Mobile-Based Poultry Product Sales and Marketing Application (MPPSAMA) designed to address these challenges by connecting farmers directly with buyers, providing real-time market information, and streamlining the sales process. The research adopted stratified sampling to gather data from poultry farmers and buyers in the Kampala and Wakiso regions. Data collection methods included interviews and questionnaires, enabling the collection of comprehensive insights into the current marketing practices, challenges, and user preferences for a mobile-based solution. The Agile System Development Life Cycle (SDLC) was employed to develop the application, ensuring a user-centric and iterative approach. The literature review highlights the significance of poultry farming in Uganda and the potential of digital solutions to enhance market efficiency. It also underscores the challenges of technology adoption in rural areas due to limited internet access and inadequate infrastructure. The study's findings emphasize the need for a centralized platform to improve market coordination, reduce product waste, and increase profitability for farmers. The mobile application aims to bridge the gap between poultry farmers and buyers, providing a reliable and efficient platform for marketing poultry products. By leveraging technology, the application seeks to transform the poultry sector in Uganda, promoting sustainable growth and enhancing food security. The study concludes that the successful implementation of this digital solution can significantly impact the livelihoods of poultry farmers and contribute to the overall development of the poultry industry in Uganda.
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ItemFire fighting system : embedded system(Makerere University, 2017)In the world today, firefighting is one of first priorities to any country or government for its survival. Fire outbreak is one of the biggest setbacks due to the enormous losses associated with it, the losses can be property or lives or monetary. In well developed countries, for example the United States, China and several European countries, etc. there exist advanced mechanisms of firefighting like, AFT portable water mist and CAFS systems. These give fire fighters a bigger advantage in the fight against fire. However, in low developed countries like Uganda and many African countries, without the above mentioned firefighting mechanisms, firefighting is very difficult and a very big challenge to these countries. Given the fact that they have gotten poor infrastructure i.e., Poor road networks for the movement of the fire brigade trucks to places that have caught fire, poor communication means of network, without advanced firefighting tools fire accidents remain a big problem to many places of these countries. Firefighting system is an embedded system with GSM capabilities that uses sensors like the smoke detectors and fire sensors to detect fire, activate an alarm and also set off the water sprinklers of the building and then send a message to the nearby fire brigade department and other people responsible like the local authorities to rapidly respond, the message contains the location of the building, the time the fire started, how to switch off the system and an option to forward the message to different people. The system sends a message every after six hours to responsible people to counter reliability. The system automatically goes off when the temperature reduces to ‘normal’.
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ItemCarbon dioxide emission monitoring and information dissemination system (CEMIDS)( 2024)This project addresses the critical issue of monitoring Carbon dioxide emissions to combat climate change. The specific objectives included reviewing literature and conducting field studies to identify system requirements, designing a system based on these requirements, and implementing a web-based platform. The developed Carbon dioxide Emission Monitoring and Information Dissemination System (CEMIDS) utilizes an ESP32 microcontroller and MH-Z19 Carbon dioxide sensor to collect real-time data, which is then processed by a Django-based backend and visualized through a React.js web application. Integrations with Firebase, Twilio, and ThingSpeak enhance the system's functionality by ensuring secure authentication, real-time alerts, and robust data logging. Comprehensive testing validated the system's performance, usability, and security. Despite limitations such as budget constraints, the project effectively meets its objectives, providing valuable data for policymakers, raising community awareness, and promoting sustainable industrial practices. Future work will expand sensor coverage, enhance analytics, and improve user engagement features, demonstrating the system's potential to contribute significantly to climate action efforts.
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ItemSquint eye detection system(Makerere University, 2023)This book begins by showing the Github repository and the blog used by the team to develop the system. It describes the software Design Document (SDD) for our project that guided us to implement the system. These are the sections included in the SDD, Introduction to the project purpose and scope, system overview, system architecture, Data design, component design, Human interface design and requirements matrix for the system. It also describes the report of our system after the implementation. Which includes, introduction, system specifications, Design output, inspection and testing, installation and system acceptance, performance, servicing, maintenance and phase out, conclusions and recommendations. This book also contains the user manual guide, which will help users of the system (Squint Eye Detection System) on how to interact with it.