Ounongo Repository
The Ounongo Repository (OR) is the institutional repository of Namibia University of Science and Technology. Ounongo means "knowledge. in the Oshiwambo and Otjiherero languages. The OR is administered by the Library, with technical assistance from DICT, and its aim is to collect, organize, manage, store, preserve, publish and make accessible worldwide, the knowledge assets or intellectual output of the University's researchers, staff and post-graduate students. Users may set up RSS feeds to be alerted to new content.
Communities in Ounongo
Select a community to browse its collections.
Recent Submissions
AUTOMATED FRAUD DETECTION IN NAMIBIA’S PUBLIC INSTITUTIONS' FINANCIAL TRANSACTIONS USING MACHINE LEARNING: A DEEP LEARNING APPROACH
(Namibia University of Science and Technology, 2024-12) Johannes, Pandeni Paavo
Financial fraud continues to be a significant concern in public-sector financial operations, undermining
the credibility of financial statements and eroding public trust. Traditional methods used by financial
experts, such as auditing, are frequently ineffective in addressing the growing complexity of fraudulent
activities and effectively mitigating associated risks. This study aimed to tackle this issue by creating an
automated fraud detection system based on deep learning designed for Namibia's public sector
financial transactions. The Ministry of Finance provided the primary data for the study through the
Office of the Auditor-General, which included accounts payable records from public entities with large
transaction volumes for the fiscal years 2021/2022 and 2022/2023. The task of fraud detection is
framed as a classification problem. The study explored three common deep learning models:
Autoencoders, Generative Adversarial Networks (GAN) and Convolutional Neural Networks (CNN).
These models' performance was evaluated using historical and simulated financial data, focusing on
accuracy, inference time, and resource utilisation. A comparative analysis revealed that the CNN model
performed exceptionally well, with the highest accuracy (0.95), F1-score (0.98), and lowest false
positive rate (0.038). In contrast, the GAN model excelled in inference time (7.17 ms per transaction)
and precision (0.99). This study proposes a scalable, data-driven approach to improving fraud detection
in large public-sector financial datasets, thereby increasing accountability in Namibia's public financial
systems.
Leveraging Machine Learning for Enhanced Efficiency in Mineral Processing Through Silica Estimation at Rosh Pinah Zinc Mine
(Namibia University of Science and Technology, 2025-04-18) Angula, Tuhafeni Kiiga
In mineral processing, real-time silica estimation in ore samples remains a significant challenge
which influences operational inefficiencies, increased costs and recovery rate. Traditional methods
depend on periodic laboratory sample tests, which, while accurate, introduce delays that exacerbate
issues related to delays on changes of the blending ratio to stabilise the silica entering the plant
circuit. These conventional methods provide valuable insights into mineral content patterns but
fail to timely deliver the information needed for optimal mineral processing efficiency. This study
addresses these challenges by introducing machine learning techniques to estimate silica content
in the ore feeds.
Four commonly used Machine Learning (ML) models were considered, namely Multiple Linear
Regression, Support Vector Regression, Random Forest, and Extreme Gradient Boosting. A
dataset consisting of 5967 entries and 10 features collected from mining operations at Rosh Pinah
Zinc Mine in Namibia for the past five years (2019-2023) was used to train and test the ML models.
The study identifies the key features that significantly influence silica content estimation. Pearson
correlation analysis was applied, which shows elements such as Sulphur (-0.54), Zinc (-0.49) and
Manganese (-0.49) exhibiting strong negative correlation. Among the four ML models, Random
Forest Regression emerged as the highly performing algorithm due to its ability to capture complex
and non-linear trends, hence superior performance. The evaluation was obtained using standard
machine learning performance metrics, namely Root Mean Squared Error (RMSE), Mean
Absolute Error (MAE), and R-squared (R²) scores. The Random Forest model demonstrated 85.4%
estimation accuracy, with low error rates of MSE at 0.3821 and MAE at 0.2848.
This study demonstrates the capability of machine learning to offer a timely data-driven approach
to enhance ore blending and improve overall mineral processing efficiency. In addition, this
enhances decision-making processes in mineral processing, reducing operational costs and
increasing recovery rates.
CO-DESIGNING A CYBERSECURITY PRACTICES FRAMEWORK FOR UNDERSERVED RURAL COMMUNITIES
(Namibia University of Science and Technology, 2024-06-30) Nhinda, Gabriel Tuhafeni
The increasing ubiquity of digital technologies and the Internet has resulted in societies
becoming heavily dependent on them for communication, healthcare, education, business
operations, and social interaction. While digital transformation promises many benefits, it also
presents significant challenges, particularly in ensuring safe and secure online participation.
Cybersecurity, as a critical enabler of digital trust, is essential for inclusive and resilient digital
societies. However, in underserved rural communities, especially in the Global South,
cybersecurity practices remain largely unfamiliar due to limited telecommunications
infrastructure, socio-economic inequality, and low digital literacy. This study addresses this
gap by co-designing a cybersecurity practices framework with and for underserved rural
communities. Rooted in a multidisciplinary approach, the research integrates elements of
human-computer interaction (HCI), community engagement, and behavioural insights. A
qualitative methodology was employed, using co-design sessions, focus group discussions, and
interviews, to understand community-specific perceptions and practices of cybersecurity. The
study draws on indigenous philosophies of Ubuntu and Uushiindaism to contextualise security
as a communal, ethical, and relational construct. The framework was evaluated through a two-pronged process: expert focus groups (ex-post) and community-based co-design sessions (exante and ex-post). Evaluation centred on ecological utility—ensuring contextual relevance,
cultural fidelity, sustainability, comprehensibility, and local ownership. The study further maps
key actors and relationships influencing cybersecurity practices in rural contexts, including
interactions with national agencies, community policing units, local radio, and ministries
responsible for awareness and legislation. This research contributes to a more inclusive
understanding of cybersecurity by recognising the socio-cultural dynamics that shape digital
safety in underserved communities. It bridges the gap between globally dominant
cybersecurity paradigms and the lived realities of rural African users. The framework
empowers communities to engage confidently with technology, facilitates digital access to
essential services, promotes skills transfer through seasonal resident engagement, and
supports the preservation of indigenous languages. For policymakers, developers, and
educators, it offers actionable insights into designing secure, contextually appropriate
technologies for the margins while ensuring broader applicability across user groups.
The State of Integrated Reporting in Namibia: Perspectives of Report Preparers
(Namibia University of Science and Technology, 2024-07-30) Kamotho, Daniel; Moloi, Tankiso; Halleen, Simon
The study aims to determine the perspectives of annual integrated report preparers for publicly traded companies in Namibia on the decision usefulness of those reports based on their preparation experiences and the challenges encountered in producing high-quality decision-useful integrated reports through interview of 12 NSX-listed integrated report preparers. Using thematic interpretative analysis, the usefulness of Namibian annual integrated reports was evaluated. The results demonstrate a divide in Namibia between those who prepare annual integrated reports and those who receive them. Listed companies in Namibia may not meet the informational needs of their users, rendering their integrated reports ineffective for decision-making. The unwillingness of preparers to alter their compliance-
heavy report preparation processes makes it challenging to develop an integrated thinking strategy. Despite these findings, the annual integrated reporting process is viewed as an improvement over Namibia's traditional reporting practices, with a focus on producing more balanced corporate reports that include social, governance, environmental, and sustainability performance measures to meet the needs of the various stakeholders. The lack of IIRC framework awareness and demand for integrated reports from stakeholders may hinder Namibia's adoption. IIRC-defined superior corporate reporting, which combines financial and non-financial information, could boost Namibia's capital flows and the economic and social well-being of the Namibian society.
Development of a framework for the protection of quarry and allied workers from occupational respiratory infections in Namibia.
(Namibia University of Science and Technology, 2025) Saima Shihepo
Background: Workplace frameworks ensure compliance with fundamental Occupational Health and Safety (OHS) legal provisions, effectively reducing workplace accidents and illnesses. Namibia lacks a comprehensive respiratory framework for protecting high-risk workers in quarry and allied industries. This study aimed to develop a respiratory protection framework that safeguards quarry and allied workers who are susceptible to occupational respiratory diseases in Namibia.
Methodology: A phased methodological approach was adopted in the study, which involved mixed methods (qualitative and quantitative) experimental techniques and secondary data review. Phases 1 and 2 assessed existing respiratory protection practices by quarry and allied workers and their knowledge, attitudes, and practices on occupational respiratory protection, respectively. This was conducted through self-administration of 320 questionnaires to workers, using a multi-tier sampling technique, as well as interviewing the managerial staff. Phase 3 involved a laboratory assessment of the Particle Filtration Efficiency (PFE) of N95 masks used by the workers. Phase 4 reviewed the Namibian legal provisions aligned with respiratory protection. Premised on the study findings, a respiratory protection outline was proposed, addressing gaps in policy and practice in phase 5.
Results: Characteristics and variables such as age, gender, work experience, beards, level of education, knowledge (training) of respiratory protection hazards, and risk perceptions of respiratory protection exacerbate the workers' risk of respirable dust inhalation. Results revealed that about 69.7% of the quarry and allied workers currently experience respiratory protection. However, 66.8%, 57.2%, and 69.7% have inadequate respiratory protection knowledge, negative attitudes, and poor protective practices, respectively. Three (3) themes were identified: One (1). Employee knowledge of respiratory protection. Two (2) Employee attitudes aligned with respiratory protection and three (3) Employee practices on respiratory protection. The Particle Filtration Efficiency (PFE) gravimetric weighing had minimal dust retention, with higher dust exposure at Sites C, D, F, and G. Particle size analysis revealed that most masks failed to effectively filter smaller particles below PM10. Reduced mask efficacy due to membrane saturation was also recorded after prolonged use. Sites A, B, and C demonstrated PM10 particle retention at 100%, while Site D showed only 60%. The review of Namibian legal provisions protecting quarry and allied workers showed fragmentation and no clarity on implementation, thereby ineffectively protecting the workers. Guidelines to operationalise the framework were developed based on the study findings obtained through questionnaires from workers, interviews from management, Particle Filtration Efficiency (PFE) experiment results, and the review of Namibian legal provisions. The framework was evaluated by experts in academia, occupational health professionals, and industry stakeholders to assess each framework component's relevance, clarity, and completeness.
Conclusion: This study underscores substantial gaps in respiratory protection, knowledge, attitudes, and practices among quarry and allied workers in Namibia, contributing to increased exposure to
16
respirable dust and associated health risks. The inefficacy of the N95 masks used by workers in filtering fine particulate matter of Particulate Matter PM10 further emphasised the critical need for a robust respiratory protection framework. Furthermore, the fragmented and unclear legal provisions fail to offer adequate protection for high-risk workers. Based on these findings, the proposed respiratory protection framework provides a comprehensive approach to improving workplace safety through enhanced policies, effective training programmes, and the adoption of higher-efficiency respiratory protective equipment. Implementing this framework is crucial for safeguarding workers' health and ensuring long-term occupational safety in Namibia’s quarry and allied industries.