School of Computing and Informatics Technology (CIT) Collection
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ItemA framework for supporting information sharing and reuse in e-government service delivery in Uganda(Makerere University, 2025)Uganda’s public sector continues to face significant challenges in information sharing and reuse among Ministries, Departments, and Agencies (MDAs), despite notable investments in ICT infrastructure and e-government systems. Fragmented data ecosystems, limited interoperability, and redundant data management processes hinder effective service delivery. This study addresses these challenges by proposing a context-specific framework for supporting secure and scalable information sharing and reuse across MDAs. Grounded in a thorough literature review and a comprehensive analysis of Uganda’s institutional context, the study adopts the Design Science Research (DSR) methodology. Through iterative design, evaluation, and refinement cycles, the study integrates findings from document reviews and empirical data collected via a national survey targeting ICT professionals and data managers. Key challenges and requirements were categorized using the PESTEL (Political, Economic, Social, Technological, Environmental, and Legal) framework. The resulting framework SIRAM (Supporting Information Reuse Among MDAs) is an adapted version of Estonia’s X-Road framework, extended to suit Uganda’s unique governance and operational environment. SIRAM incorporates components such as policy harmonization, metadata standards, legal alignment, stakeholder trust-building, and capacity development. Visual modeling was conducted using Visual Paradigm. To evaluate the framework, structured walkthroughs were conducted with 15 experts from URA and peer MDAs. Evaluation results confirmed the framework’s conceptual relevance, usability, and institutional applicability. The study concludes by emphasizing the need for coordinated legal reform, investment in capacity-building, and centralized governance structures to unlock the full benefits of e-government interoperability in Uganda. Future studies may extend the framework by testing its scalability across MDAs, integrating with UGHUB, and examining policy adoption challenges. Future work will pilot SIRAM across additional MDAs and refine governance, legal, and technical guidelines by sector. It will also evaluate capacity needs and the costs and benefits of nationwide adoption.
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ItemAn artificial intelligence-enabled farmer knowledge mining and integration framework for E-Extension Systems(Makerere University, 2025)This research proposes and evaluates a conceptual Artificial Intelligence-Enabled Farmer Knowledge Mining and Integration Framework designed to address a longstanding limitation of existing e-extension systems, the inability to systematically capture and utilize tacit knowledge generated by farmers and extension workers. Unlike traditional top-down platforms that primarily disseminate expert-derived content, the proposed framework establishes a structured, technology-agnostic architecture comprising three modules: a Knowledge Acquisition Module for eliciting experiential insights through conversational interactions; a Knowledge Processing Module for transforming unstructured inputs into structured, interpretable knowledge; and a Knowledge Integration Module that embeds processed knowledge into e-extension platforms to deliver localized, context-aware advisory services. To assess the framework’s feasibility, a prototype application -Farmwise was developed to operationalize selected components of the architecture using conversational AI and natural language processing techniques. The prototype was evaluated through user testing, expert review, and performance analysis to determine its utility, usability, and efficiency. Results demonstrate that the framework can significantly enhance the relevance, responsiveness, and participatory nature of e-extension systems by enabling bi-directional knowledge flows and supporting continuous learning from farmer experiences. The study contributes a novel architectural model for integrating informal, experience-based knowledge into digital agricultural systems, offering valuable insights for software engineering, ICT4D solution design, and future AI-enabled innovations in agriculture. Keywords: Artificial Intelligence, Natural Language Processing, Chatbot, Automatic Query Response Model, Rasa.
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ItemExplainable ensemble machine learning for SQL injection attack detection(Makerere University, 2025)SQL injection (SQLi) remains a major cybersecurity threat that exploits weaknesses in database-driven web applications to gain unauthorized access to sensitive data. Existing detection systems often rely on static rule sets and opaque machine learning models that lack interpretability, adaptability, and robustness against new attack variations. To address these limitations, this study developed an explainable hybrid ensemble machine learning model for SQL injection detection. The proposed framework integrates transformer-based semantic understanding with statistical query profiling to enhance both accuracy and interpretability. A dataset of 22,470 SQL queries collected from two production systems at Makerere University, namely the Makerere University E-Learning Environment (MUELE) and the Electronic Human Resource Management System (EHRMS) was used for model development and evaluation. The dataset included six major SQLi categories: tautology-based, union query, piggy-backed, comment-based, illegal/logically incorrect, and blind SQLi, allowing for comprehensive performance analysis across diverse attack types. Feature engineering played a central role in the model’s success. Contextual features were extracted using Bidirectional Encoder Representations from Transformers (BERT), capturing the semantic meaning of SQL syntax and revealing obfuscated injection patterns undetectable by traditional methods. These semantic embeddings were combined with handcrafted statistical indicators such as query length, special-character frequency, and keyword density, enabling detection of structural anomalies indicative of SQL injection behavior. This hybrid representation provided a multidimensional understanding of both syntactic and semantic query characteristics, improving model sensitivity and interpretability. Multiple classifiers including Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Naïve Bayes, Gradient Boosting, LightGBM, and CatBoost were trained and evaluated. Ensemble techniques such as bagging, boosting, and voting were applied to enhance generalization performance. Therefore, the proposed boosting-based ensemble model achieved an accuracy of 99.49%, with balanced F1-scores of 96.87% for benign queries and 99.72% for malicious queries. Explainability was incorporated through SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations). SHAP analysis revealed that BERT embeddings contributed approximately 45% of the model’s predictive power, while features such as tautological conditions and comment-based patterns were key indicators of SQLi attacks. The final model was deployed as a RESTful FastAPI microservice, capable of processing over 10 queries per second with average response times of 150–200 ms. The study demonstrates that combining semantic embeddings with statistical features in an explainable ensemble framework yields a robust, interpretable, and production-ready solution for SQL injection detection. Keywords: Machine learning, SQL Injection Attack Detection
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ItemAn interpretable machine learning approach to predict loss to follow-up among people living with HIV(Makerere University, 2025)The advancement of AI and machine learning has led to wide adoption in different fields and sectors such as education, engineering, and health. Given this adoption, multiple countries have adopted different machine-learning techniques and algorithms to improve patient care and health service delivery. Uganda, as a country, has piloted the use and adoption of different machine learning models to especially in health, to detect conditions such as cancer of the cervix, and screening for malaria, among others. Lost to follow-up is one of the major challenges affecting service provision for HIV, especially in low- and middle-income countries such as Uganda. HIV clients in Uganda get lost to follow up due to a number of reasons, among them is the high mobility of clients who keep moving from one location to another, some treatment centers are located far away from the clients who may not have transport facilitation to and from facilities, and the adverse psychosocial issues affecting these clients without the necessary support. These reasons are unfortunately only known after the client is lost to follow-up, thus a reactive approach. A prediction algorithm that predicts the client’s likelihood of dropping out will help improve patient care treatment outcomes so that the client is followed up with before they get lost. This research implements an interpretable predictive algorithm that predicts the patient outcome and provides insight/explanation as to why the outcome has been made. This work differs from the existing implementation by explaining the traditional black box models implemented for a prediction. Longitudinal client-level data has been collected and used in this research including social demographic information as well as patient medical history data, to find patterns that inform the prediction outcome. The collected data was augmented using three major data augmentation techniques to eliminate class bias typical of medical data. These techniques included random under-sampling, which randomly reduces the instances of the majority class. Random oversampling is another technique that was employed, where new samples were added to the minority class, thereby balancing the dataset. Synthetic samples were added to the dataset through the Synthetic Minority Over-sampling technique (SMOTE) as another technique to balance the dataset. The models were trained on all three datasets from the augmentation techniques, including the original dataset. Interpretability using LIME was then added, and the results are presented. The research shows XGBoost model using an over-sampled dataset produced the best results for the classification of clients who are lost to follow-up. This research provides an interpretable machine learning model that predicts clients likely to drop out with an explanation or insight into why they are likely to drop out of care. This research provides a new standard for the use and adoption of Artificial intelligence by providing justifications for the outcome.
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ItemA computer vision approach towards glare mitigation and image quality enhancement in license plate recognition(Makerere University, 2025)License Plate Recognition (LPR) systems play a crucial role in Intelligent Transportation Systems (ITS), facilitating automated vehicle identification for applications such as traffic monitoring, law enforcement, and toll collection. However, these systems often suffer from glare-induced distortions caused by intense light sources such as sunlight, vehicle headlights, and reflections. These distortions obscure license plate details, leading to reduced Optical Character Recognition (OCR) accuracy and compromised system reliability. This research addresses this critical challenge by developing a unified computer vision framework that integrates Autoencoders (AE) and Noise2Clean Generative Adversarial Networks (N2C-GAN) to mitigate glare and improve image quality. The study aimed to achieve four key objectives: access and utilize an existing dataset of glare-induced license plate images, image pre-processing, model implementation, and rigorous model evaluation. The proposed model demonstrated significant advances in glare mitigation, achieving a Peak Signal-to-Noise Ratio (PSNR) of 38.8 dB, a Structural Similarity Index Measure (SSIM) of 0.987, and a Visual Information Fidelity (VIF) of 0.8896. Furthermore, the model improved the accuracy of OCR to 99.9% using Google Cloud Vision OCR, underscoring its effectiveness in restoring license plate readability under glare conditions. Computational efficiency was a key focus, with a compact model size of 298 kB and a runtime of 0.7263 s, making it scalable for real-world deployment. Despite encountering limitations such as dataset bias and computational constraints, this research provides valuable insights and lays the groundwork for future advances in glare mitigation, image processing, and machine learning-based LPR enhancements. The findings have broad implications for transportation management, public safety, and automated enforcement, offering a robust solution to improve the performance and reliability of LPR systems in diverse real-world applications.