School of Computing and Informatics Technology (CIT) Collection
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ItemExplainable machine learning for antimalarial activity prediction in drug discovery(Makerere University, 2025)Malaria remains a significant global health burden, causing substantial morbidity and mortality, particularly in tropical and subtropical regions. While effective antimalarial drugs exist, such as quinine, chloroquine, antifolates and artemisinin, the emergence of drug-resistant strains of Plasmodium falciparum emphasises the need for ongoing drug discovery efforts. One of the primary challenges in drug discovery is the high failure rate, with over 90% of candidate drugs failing to reach clinical trials. To address these challenges, the pharmaceutical industry and research institutions have explored alternative approaches to drug discovery, including artificial intelligence (AI) and machine learning (ML) techniques. Despite the growing array of ML methods for drug discovery, these techniques often demand expertise. Furthermore, there is a limited exploration into the rationale behind predictions, which is essential for understanding why a specific compound shows potential as an antimalarial agent. Understanding the types of molecular representations and relationships between chemical structure and activity prediction is necessary for researchers to refine molecules and design more effective drugs. This dissertation explored the application of ML models for predicting antimalarial activity in chemical compounds, with a focus on enhancing the interpretability of these models through Explainable Artificial Intelligence (XAI) techniques. A key contribution of this work is the development of the XAI4Chem tool, which integrates interpretability into the ML workflow for cheminformatics, allowing researchers to better understand the factors influencing predictions. Using data from the ChEMBL database, models were trained on molecular descriptors (RDKit, Datamol, and Mordred) and fingerprints (RDKit and Morgan) to predict the percentage inhibition and classify compounds as active or inactive. Models trained on RDKit descriptors with 64 selected features achieved a higher performance in regression (R² of 0.563), outperforming Morgan Fingerprints (R² of 0.5012). Both RDKit descriptors and Morgan fingerprints achieved 97% test accuracy in classification. SHAP (Shapley Additive exPlanations) value analysis identified key molecular features such as the compound’s lipophilicity (MolLogP), polar surface area (TPSA), number of amide functional groups (fr_amide), and the estimated drug-likeness (QED) as significant drivers of predictions.
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ItemMulti objective adaptive task offloading at the edge using fuzzy logic for time sensitive applications in industry 4.0(Makerere University, 2025)The rapid growth of Industry 4.0 has increased the demand for real time data processing and ultra low latency communication to support time sensitive Industrial Internet of Things (IIoT) applications. While edge computing reduces latency by executing tasks closer to data sources, the mixture of devices and dynamic workloads often leads to imbalanced task distribution, making efficient offloading a persistent challenge. Traditional static strategies such as Priority Based Preemptive Scheduling (PPS), Minimum Completion Time (MCT), and Round Robin (RR) fail to address multiple conflicting objectives including latency, resource utilization, and task priority under strict time constraints. To overcome these limitations, this research proposes FAST EDGE, a multi objective adaptive task offloading algorithm based on a Fuzzy Expert System (FES). The FES evaluates device suitability using key metrics like processing power, bandwidth, load and distance from the server while modeling uncertainties through fuzzy inference. The system is implemented in EdgeCloudSim and evaluated across diverse IIoT workloads and device configurations. Performance is compared against PPS, MCT, RR, and a non adaptive baseline using latency, completion time, makespan, accuracy, and overhead as metrics. Experimental evaluation in EdgeCloudSim shows that FAST EDGE reduces latency by 28.8 % (3.46 at 2.46 ms) and completion time by 27 % (4.13 to 3.01 ms) while lowering makespan by 29 %, improving decision accuracy by 15%, and reducing overhead to 0.010 equal to 25 % drop compared with the best baseline (PPS). These quantitative gains demonstrate the model’s practical ability to meet 10 ms Industry 4.0 timing requirements under heterogeneous IIoT loads.
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ItemA framework for supporting the accessibility of e-learning platforms for students with visual impairements in higher education institutes in Uganda: a case study of Kyambogo University(Makerere University, 2025)E-learning is a rapidly growing trend in education, but it is not fully accessible to people with visual impairments, despite their increased enrolment in higher education. It has the potential of reaching of reaching out many students in an accessible way, including those with disabilities if it designed with the needs of learners with disabilities in mind; which is not the case for Ugandan Universities. This study set out to develop a framework for supporting the accessibility of e-learning platforms for This study was defined by the following main objective; To design a framework for supporting the accessibility of e-learning platforms for students with visual impairments in HEIs in Uganda and to achieve this the following specific objectives were formulated; To determine the requirements for a framework for supporting accessibility in e-learning platforms for students with visual impairment, to design a framework that supports the accessibility of e-learning platforms for students with visual impairment and to evaluate the designed framework for environmental utility using. This study adopted an abductive approach and used a design science research methodology to guide the research. Adobe Illustrator CC was used for to visualize the framework proposed by this study. The key findings were that necessary guidelines, best practices and assistive technologies to enhance the accessibility of e-learning platforms are available. However, the e-learning administrators responsible for implementing these learning systems possess insufficient expertise in this domain. The developed framework was evaluated in terms of its environmental utility and accessibility. The evaluation aimed to assess the framework's usefulness within the given context and its alignment with the established accessibility guidelines and company best practices pertinent to visually impaired learners. The scholarly contribution of this study was threefold: providing insights into addressing the issue of limited accessibility in e-learning, documenting a framework to support accessibility of e learning for students with visual impairments, and sharing the experiences from problem definition to artifact evaluation.
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ItemA knowledge management portal to support the sharing of best practices in community-based obesity prevention and advocacy in Uganda(Makerere University, 2025)Obesity has gradually become a silent epidemic in Uganda, contributing to rising rates of diet-related non-communicable diseases (NCDs) such as hypertension, type 2 diabetes, cardiovascular disease, and certain cancers leading to increased mortality. This alarming trend has placed considerable strain on Uganda’s health system and highlights critical gaps in community-level knowledge about effective practices for preventing and managing obesity. With limited focus by the government on actions that specifically target the prevention and management of obesity, there is a growing need for increased advocacy, particularly through knowledge sharing to engage communities in physical activity and promote diet modifications for healthier lifestyles. Knowledge Management (KM) has been proven to be a valuable approach to addressing health knowledge gaps by enabling access to, sharing of, and application of evidence-based practices within communities. However, the effective implementation of KM in the obesity care continuum remains largely untapped in Uganda. This study addresses that gap by developing a Knowledge management portal for Obesity Prevention and Advocacy (KOPA), a digital platform intended to serve as a single point of access to evidence-based best practices for obesity prevention and advocacy, targeting both healthcare professionals and community members. Using an Action Research approach, this study identified key challenges and user requirements that informed the portal’s design and implementation. KOPA combines a searchable library of best practices on healthy eating and physical activity, collaboration tools, content for different user groups, simple obesity-tracking, and clear Uganda-specific guidance on healthy lifestyle change. Evaluation with healthcare professionals and community members found it relevant, easy to use, and helpful for finding, organising, and sharing practical information that supports behaviour change. KOPA shows how a centralized, knowledge-driven platform can strengthen obesity prevention and advocacy in resource-constrained settings. Future work includes expanding multilingual content, introducing lightweight mobile access, connecting with Ministry of Health resources, and running a multi-site evaluation to confirm gains in knowledge, activity, and diet quality.
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ItemExplainable multi-model predictive modeling of antimicrobial resistance in urinary tract infections(Makerere University, 2025)Antibiotics are among the most commonly used medications to treat Urinary tract infections (UTIs) globally. Despite their importance, the sustained effectiveness of antibiotics is endangered by the development of resistance. While predictive modeling offers a promising path toward early antimicrobial resistance (AMR) detection, single-strategy learning approaches often struggle to generalize given the complex, nonlinear, and high-dimensional nature of AMR data. To address these limitations, multi-model predictive modeling was explored. This combined both traditional machine learning methods (Support Vector Machines, Random Forest, and XGBoost) and deep learning architectures (Convolutional Neural Network, Long Short-Term Memory, Autoencoders, and hybrid CNN-LSTM and CNN+Decision Tree). Both undersampling (Tomek Links, Edited Nearest Neighbors) and oversampling (Synthetic Minority Over-sampling Technique, Borderline-SMOTE, and Adaptive Synthetic Sampling) approaches were examined to address the severe class imbalance. Oversampling was preferred over undersampling due to its more consistent class distributions across the four target antibiotics: Nitrofurantoin (NIT), Trimethoprim-Sulfamethoxazole (SXT), Ciprofloxacin (CIP), and Levofloxacin (LVX). The baseline XGBoost model proved to be the optimal model. Although the XGBoost model did not outperform other models, it consistently offered the best balance between predictive performance and real-world applicability, particularly in the context of minimizing false predictions. Explainable Artificial Intelligence (XAI) techniques, including SHapley Additive exPlanations (SHAP) and Integrated Gradients, were used to enhance transparency and enable feature-level insights. Results from the XGBoost model using SHAP revealed that prior resistance to the target antibiotic, history of Escherichia infections, and broad antibiotic class exposures were the most influential predictors across all four target antibiotics. In contrast, resistance history within a short time window and specific time-bound prescriptions, such as prior resistance SXT 30, contributed minimally to model predictions. Finally, the optimal model (XGBoost) was deployed as a Flask-based RESTful API containerized with Docker and continuously integrated using GitHub Actions, then hosted on Render. This API was designed to allow CSV-based or real-time predictions and can be integrated into Electronic Medical Record (EMR) systems. This was aimed at helping clinicians identify AMR risk earlier, potentially guiding evidence-based clinical decision making and reducing inappropriate antibiotic use, thus contributing to global efforts to mitigate AMR.