Theses and Dissertations

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Browsing Theses and Dissertations by Subject "Uganda"

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    A blockchain-based CCTV data verification framework for Uganda police force: a case study of Kampala metropolitan area
    (Muni University, 2025-10-23) Adebo, Thomas
    The deployment of Closed-Circuit Television (CCTV) systems is increasingly critical to enhancing public safety and crime investigations worldwide. In Uganda, despite significant investments by the Uganda Police Force (UPF) in the Kampala Metropolitan Area, existing CCTV systems face major challenges related to centralized data storage vulnerabilities, including risks of tampering, unauthorized access, and data manipulation. These challenges undermine the evidentiary integrity of surveillance footage, negatively impacting judicial processes and public trust in law enforcement. This study aimed to design and implement a Blockchain-based CCTV data verification framework to address these issues by securing and authenticating surveillance footage. Guided by the Design Science Research Methodology (DSRM), the research involved identifying current challenges and designing a Blockchain-based Framework using Multichain Blockchain technology. This framework was developed to include smart contracts for automated verification and validated through simulation using a dataset of 35 video footage clips from Kampala Metropolitan. A test of the Blockchain-based CCTV video verification prototype demonstrated its effectiveness, successfully uploading 30 correctly tagged video footages linked to active police case files while accurately rejecting 5 tampered, unreferenced clips. This confirms the system's ability to verify and accept only valid, case-linked footage, enhancing data integrity. The findings also revealed key weaknesses in current CCTV data management: 65% reported unauthorized access, 35% raised concerns about authenticity, trust in existing verification is low (22.5%), 55% rely on manual checks, and 60% lack proper backups. These issues underscore the pressing need for Blockchain verification, automated integrity checks, secure access, and reliable storage to enhance system trust, security, and resilience. This study contributes to the application of Blockchain technology in public sector digital forensics, offering a practical and scalable solution for law enforcement agencies. The findings underscore the potential of Blockchain to enhance data integrity, foster public trust, and streamline criminal investigations. Future research should focus on integrating artificial intelligence-driven video analytics with Blockchain systems, expanding the framework’s applicability to national levels, and addressing regulatory and operational challenges for broader adoption.
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    Utilizing a long short-term memory model for the prediction of refugee arrivals in Uganda
    (Muni University, 2025-11-26) Abusa, Michael
    Uganda, currently hosting over 1.95 million refugees as of August 2025 (UNHCR/OPM), faces mounting challenges in forecasting refugee influxes, particularly from South Sudan, Sudan, and the Democratic Republic of Congo. Traditional estimation methods, such as registration, mass verification, and census, are reactive, resource-intensive, and lack predictive foresight. This study examines the application of Long Short-Term Memory (LSTM) deep learning models to predict refugee arrivals using time-series data from Rhino Camp Refugee Settlement, spanning the period from 2015 to 2024. Adopting a hybrid research design, the study modeled five influx scenarios, including emergency-only, protracted-only, and mixed, to evaluate the LSTM model’s predictive performance against a linear regression baseline. The RMSE, R2, and MSE metrics were used to assess the model accuracy. Testing the four hypotheses yielded the following insights: Ho1: The availability of reliable and high-quality time-series data enabled effective predictive modeling, leading to the rejection of Ho1. Ho2: The LSTM model demonstrated superior reliability and precision, especially under protracted influx conditions, thereby rejecting Ho2. Ho3: Significant differences emerged in model performance across scenarios. The protracted-only scenario achieved the highest accuracy (MSE = 0.0191, RMSE = 0.1276), while the emergency-only scenario recorded the highest error (MSE = 0.0251), confirming the rejection of Ho3. Ho4: A significant relationship was found between refugee arrivals and influx timing, particularly in structured displacement settings, leading to the rejection of Ho4. Overall, the LSTM model consistently outperformed the linear regression baseline, capturing complex temporal dependencies in refugee arrival patterns. Training times ranged from 6 to 20 minutes, underscoring computational efficiency. The study concludes that LSTM-based forecasting, when grounded in structured and temporally stable datasets, offers a scalable and ethically sound approach to enhancing early warning systems and humanitarian planning. These findings contribute to the growing body of knowledge on AI applications in displacement contexts and offer a framework for future research and operational deployment.