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Item Predicting Lapse Rate in Life Insurance Using Machine Learning Algorithms: A Case Study of Sanlam Namibia(Namibia University of Science and Technology, 2025-10-07) Kamati, Nelson Mitchell.The prediction of lapse rate in the life insurance industry holds significant importance for insurers aiming to maintain profitability and retain a strong customer base. Accurate lapse prediction will enable insurers to deploy personalised retention efforts, effective risk management, and financial planning depending on the model’s results. The study explored the use of Machine Learning (ML) techniques to predict lapse rates in life insurance. The dissertation's main contribution was to empirically compare and benchmark 4 machine learning classifier models (Logistic Regression, Gradient Boost, Random Forest, Support Vector Machine). The study utilised secondary data that was obtained from Sanlam Namibia’s database, including policyholder demographics, policy features, premium amounts, payment frequency, lapse status, and other relevant information that could influence the lapse rate. Different types of machine learning algorithms were used on the acquired data to assess their performance in forecasting lapse rate. The study's findings showed that machine learning methods can accurately predict the lapse rates in life insurance. The findings reveal the ensemble model’s high predictive capacity. Gradient Boosting is the best overall classifier, with a 94% and F1-Scor of 94% respectively. Furthermore, some key policy and clients’ characteristics were found to have substantial predictive potential for the lapse rate. However, the limitations of the study must be considered. Finally, the study recommends using ensemble models rather than single model classifiers because they are more effective at predicting life insurance lapses.Item Examining the effects of ChatGPT on Television News Production at the Namibian Broadcasting Corporation(Namibia University of Science and Technology, 2025-01-28) Kadhila, Anna TashiyaThere is limited research on the specific effects and challenges of using Artificial Intelligence (AI) technology, particularly ChatGPT, in newsrooms in Africa, and Namibia in particular. From preliminary research, the Namibian Broadcasting Corporation (NBC) makes use of ChatGPT and other chatbots in the production of television news as an AI tool, but it has been impossible to determine the extent thereto from casual observations. Through qualitative research through conducting face-to-face interviews with fifteen (15) journalists as the population of the study, this thesis aimed to examine the challenges presented by ChatGPT and potentially other chatbots on television news production at NBC, while evaluating their potential to enhance news-gathering processes and their effects from the African context. The study highlights the dual impact of ChatGPT in Namibian television newsrooms, where it enhances efficiency but struggles with cultural and linguistic representation. While AI streamlines news production, its limitations in processing indigenous languages and reflecting local contexts raise concerns about audience alienation and content authenticity. Ethical challenges, including job security and editorial oversight, further emphasize the need for responsible AI integration. Ultimately, the findings call for policies that balance AI’s benefits with Namibia’s socio-cultural realities, ensuring its use strengthens rather than diminishes journalistic integrity.Item Assessing The Efficacy of Artificial Intelligence Chatbots for Delivering Public Information: A Case Study of the “Being A Public Servant in Namibia - The Pocket Guide 2.0 Pocket Bot”(Namibia University of Science and Technology, 2025-01-31) Nassauw, C.C.The research evaluated the Namibian government's "Being a Public Servant in Namibia – The Pocket Guide 2.0 Pocket Bot" artificial intelligence chatbot which serves as a digital self-service tool for civil servants. The research aimed to determine whether the chatbot fulfils its intended purpose to deliver precise and easily accessible public information to its users. The research employed an exploratory qualitative case study methodology. Twenty civil servants including administrative staff, technical experts, and management personnel participated in semi-structured interviews to gather data. The interview data analysis through thematic methods showed that the users appreciated the chatbot because it provided quick access at all times. The users also highlighted three main concerns with the chatbot system which included its user interface design ,its natural language processing capabilities, and the accuracy of its policy-aligned information. Recommendations were drawn from the research findings for the purpose of improving the chatbot interface, expanding its subject matter coverage, enhancing its response speed, and accuracy to fulfil civil servants’ needs. The recommendations provide guidance to policymakers and government agencies for enhancing chatbot services, enabling them to deliver public information more efficiently while improving user satisfaction.Item ASSESSING THE ADAPTIVE CAPACITY OF SELECTED NAMIBIAN PRINT AND BROADCAST MEDIA IN RESPONSE TO CHANGES IN PRACTICE IMPOSED BY COVID-19(Namibia University of Science and Technology, 2025-01) Lamyaa, LinusThe COVID-19 pandemic significantly disrupted the global media landscape, posing unprecedented challenges for journalists and media organisations. This study examines how media and journalists in Namibia responded to changes in journalism practices imposed by COVID-19. Using an exploratory research design and adopting a qualitative research approach, this study assessed changes to journalism practice as a result of the Covid-19 pandemic, and how journalists and media organisations responded to the changes. Data was collected through in-depth interviews with journalists and media practitioners, from selected print and broadcast media, to understand their experiences and adaptations during the pandemic. Findings reveal that the Namibian media industry underwent substantial transformations, including a shift from traditional newsroom operations to remote and digital reporting. The study also found that misinformation was a major challenge, necessitating rigorous fact-checking and verification processes to maintain credibility and public trust. Financial constraints further exacerbated the difficulties, with many media organisations experiencing significant revenue losses, leading to salary cuts, job redundancies, and shifts in business models. Additionally, the pandemic took a toll on journalists' mental health, with many reporting stress and burnout due to increased workloads, job insecurity, and exposure to distressing news. Despite these challenges, journalists demonstrated resilience by embracing digital transformation, collaborative reporting, and innovative storytelling techniques. Alternative revenue streams, including digital subscriptions and external funding sustained media operations. The study concludes that while COVID-19 accelerated digital transformation and innovation in journalism, it also exposed vulnerabilities in financial sustainability and journalists’ well-being. Strengthening institutional support, investing in digital infrastructure, and implementing mental health initiatives are recommended to enhance media resilience in future crises. These findings contribute to the broader discourse on media adaptation in crisis situations.Item AN EXAMINATION OF THE SAFETY AND WELFARE STRATEGIES OF PRINT JOURNALISTS REPORTING ON COVID-19 IN NAMIBIA(Namibia University of Science and Technology, 2025-01) PAHEJA, SIRIRIKA.The role of the media during a crisis cannot be over emphasised. During the outbreak of the Covid-19 pandemic, the media assumed a central position in sharing health information during this unprecedented time. In recognition of the significance of journalism, the Namibian government classified journalists as essential workers, allowing them to collaborate freely with healthcare workers during lockdowns. However, being frontline workers raised pertinent issues about the safety and welfare of journalists as they faced challenges, including job losses, mental health problems arising from poor psychosocial care and challenges of working in far-flung regions with meagre resources. Using a qualitative approach and the Protection Motivation Theory, this study examined the safety and welfare strategies employed by selected print journalists in Namibia while reporting on Covid-19. The study assessed selected Namibian print journalists’ perception of their vulnerability to Covid-19, the safety and welfare strategies employed during the COVID-19 pandemic and the effectiveness of the safety and welfare strategies employed when reporting during the COVID-19 pandemic. The Namibian, New Era and Namibian Sun newspapers participated in the study. The study found that journalists felt vulnerable, and experienced emotional and psychological challenges while reporting on Covid-19. The study further observed that various safety and welfare strategies were used by journalists. These included, among others, self-talk and introspection, innovation, escapism, institutional support and adapting and adhering to established health protocols. The study found that while basic support for journalists was provided, mental health support was notably absent despite its crucial role in ensuring that journalists work in a conducive environment. The study concluded that the lack of proper and adequate institutional support for journalists, particularly covering COVID-19 pandemic, compromised both their physical and emotional safety. This undermined their ability to perform their professional duties effectively.Item A MACHINE LEARNING-DRIVEN APPROACH FOR ACCIDENT PREDICTION AND TRAFFIC SAFETY ANALYSIS IN NAMIBIA(Namibia University of Science and Technology, 2024-12) Ngolo, MariaGlobally, road traffic accidents contribute a large portion of injuries, fatalities, and significant economic losses and ongoing research has projected that by 2030, car crashes would be the 5th top reason for loss of life around the world. The key cause of traffic accidents is hard to determine nowadays because of a complex mix of factors, such as road conditions, weather conditions, and the mental condition of the drivers, to list a few. Without a thorough understanding of the characteristics and causes, intelligence-led countermeasures to decrease crashes cannot be created or implemented. Therefore, if traffic accident characteristics can be better understood, it might be easier to take some mitigative action. Nowadays, the utility of machine learning methods in the field of road traffic crashes is gaining traction. The objective of this dissertation is to analyse historical data for a five-year period (2018-2023) and to understand the patterns in accident occurrences by making use of machine learning methods. Machine learning models as such Random Forest, Support Vector Machine, K-Nearest Neighbours, Association Rule Algorithm (AARA), and k-Clustering were employed on the dataset. The Apriori Association Rule algorithm explored the rules with high lift and high support, respectively. The research shows that the Random Forest model is the reliable model in predicting crash severity, reaching an accuracy of approximately 81%. Factors such as junction type, poor road sign conditions, uncontrolled traffic, weather, lighting, road surface, vehicle type, and driver behaviours were identified as the significant variables influencing road accidents. Pedestrian, rollovers, and collision are the leading crash causes of the road accidents, and they are associated with uncontrolled traffic and daylight. Additionally, the research shows notable differences in accident rates by region, month, year, day of the week, and hour of the day, underscoring the impact of geographical features, seasonal trends, and commuting habits on accident rates. The findings indicate that high traffic volumes and urban congestion are the main causes of the greatest accident rates in metropolitan areas, especially in the Khomas Region of Namibia. Further, more accidents are happening more toward the weekend as compared to weekdays and during night hours.Item 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 PaavoFinancial 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.Item 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 KiigaIn 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.Item CO-DESIGNING A CYBERSECURITY PRACTICES FRAMEWORK FOR UNDERSERVED RURAL COMMUNITIES(Namibia University of Science and Technology, 2024-06-30) Nhinda, Gabriel TuhafeniThe 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.Item Predicting International Tourist Arrivals in Namibia Using Machine Learning(Namibia University of Science and Technology, 2024-08) Shivute, Selma.The lack of accurate and timely predictions for international tourist arrivals in Namibia remains an open problem, leading to inefficiencies in tourism planning and resource allocation. Traditional methods, primarily based on seasonal trends and historical data, dominate the forecasting landscape. Although traditional approaches are good at capturing seasonal patterns, there is always a lack of accounting for more dynamic and non-linear interactions between the predictive variables and more reliable results. In recent years, machine learning models like Seasonal Autoregressive Integrated Moving Average (SARIMA), Random Forest, and Prophet have gained increasing support for their ability to handle complex, non-linear data and provide more accurate tourist arrival forecasts. SARIMA is particularly good at modelling seasonal time series data, Random Forest excels in capturing non linear relationships, and Prophet is designed to handle time series data as well as irregular and missing data. However, attempts to implement these three models in predicting international tourist arrivals in the Namibian context have exposed limitations such as a constant prediction with Random Forest and the need for extensive tuning in SARIMA and Prophet, which may result in their prediction accuracy showing little improvements in Namibia’s tourism sector. This study aimed to develop and test the three models: SARIMA, Random Forest, and Prophet, to predict international tourist arrivals in Namibia more accurately. Accurate forecasts can improve decision-making within the tourism sector, infrastructure planning and resource allocation. The methodology used involved data preparation and data exploratory strategies to determine the relationship between exploratory variables and dependent variables. It also included training and validating these models on historical data obtained from the Ministry of Home Affairs, Immigration, Safety and Security. The models were hyper-tuned to overcome the limitations of accuracy by improving accurate predictions. It is expected that these models will overcome the limitations of non-accuracy predictions of tourist arrivals. The effectiveness of these models' accuracy was evaluated using Root Mean Square Error and Mean Absolute Error and comparing their performance against each other to determine the preferred model for Namibia. The results indicated that SARIMA achieved the most accurate prediction, followed by Random Forest, and Prophet performed poorly in predicting international visitor arrivals in Namibia. The two models are anticipated to contribute significantly to more efficient tourism management and planning in Namibia.Item Exploring Machine Learning on Geochemistry Data For Efficient Prediction of Metal Concentrations in Copper Deposits(Namibia University of Science and Technology, 2024-01-25) Joel, Lydia.Naturally occurring ore bodies like Copper often occur in compound form with other useful metals such as Silver, Lead and Zinc. Due to the cost, mining companies find it difficult to pay for analysis of various metals in their samples and end up focusing on analysing one metal or a few, leaving out a bunch of other associated metal concentrations in the deposit. Additionally, analysing different metals in samples can take time, and this increased turnaround time of receiving results from the laboratory can negatively affect production. The research used a geochemistry dataset comprising of 3,282 samples from the Kombat Copper deposit area in Namibia to predict copper (Cu) concentrations from zinc (Zn) and lead (Pb) concentrations. In addition to the metal concentrations, the dataset had sample coordinates and grid names features. The four machine learning algorithms used were Random Forest (RF), K-Nearest Neighbour (KNN), Decision Tree (DT), and Support Vector Machine (SVM). These models were used because they were the commonly employed models for similar purposes, in the literature reviewed. The learning task was a regression problem, therefore, the primary metric utilised to assess the machine learning model and draw performance conclusions was the regression score (R-squared), which quantifies how well the model explains the variance in the data. The R squared score represents the percentage of variance in the dependent variable (target) that can be predicted from the independent variables (features). It ranges on a scale of 0 to 1, where 1 indicates a perfect fit. In addition Mean Squared Error (MSE), Root means squared error (RMSE), mean absolute error (MAE), Adjusted R-squared, and explained variance metrices were also looked at. Based on the R-squared metric, the KNN model outperformed the other three models, predicting 57% of the relationship between the dependent and independent variables. K-NN was followed by RF with 0.55 score, DT with a 0.49 score and the SVM with a 0.44 score. KNN model appeared to be the best choice among the four models for making predictions for the dataset. Further optimisation of the models improved their prediction accuracy, with the KNN model still with a superior performance of R-squared at 70% (0.70) with n-estimators set at 4 and the test size set to 10%. Predicting metal contents from geochemistry data with machine learning can iv help mining companies reduce costs by supplementing lab-based analyses with model-based predictions in determining grades.Item Recommending a Machine Learning Model to Detect the Fatigue State for Employees at Namdeb(Namibia University of Science and Technology, 2024-04) Nakale, S.N.Workplace fatigue is one of the major risk factors across different industries and it negatively impacts productivity and workplace safety. Thus, fatigue detection and monitoring is essential to promote occupational health and safety. The advancements in data collection technologies have made it possible for industries to develop data driven solutions by developing Artificial Intelligence (AI) and Machine Learning (ML) based fatigue monitoring and detection systems. The Advanced Driver Assistance Systems (ADAS) is one of the technologies that has been adopted in various industries to improve safety for drivers. Namdeb implemented the ADAS in recent years and they have identified the need for a system to detect and classify employees’ fatigue state using data from the ADAS. In this study, an ML based fatigue detection system was proposed. Facial behavioural fatigue features were used to detect fatigue. The proposed system deployed some of the commonly used ML classification algorithms and it was evaluated on a simulated dataset, the Yawning Detection Dataset (YawDD), and a real-world dataset, data from the Namdeb ADAS. The results showed that most of the supervised ML classifiers achieved a fatigue prediction accuracy above 90% for both datasets. The Random Forest (RF) based fatigue detection-based model was found to be the best model. The k-Means which is an unsupervised ML classifier exhibited the worst performance. However, the reliability and generalisability of the results based on the real-world dataset can be improved by using a larger dataset. The major challenge to developing behavioural based fatigue detection systems for real world setting like the mining environment is face detection accuracy which is affected by factors such as low image resolution due to poor and variable lighting conditions, face orientation to camera and proximity of face to the camera. The significant contribution of this study is the use of real-world dataset to test the proposed fatigue detection system. Overall, the study contributes to the promotion of the eighth Sustainable Development Goal (SDG) of promoting safe working environments.Item ASSESSING THE STRATEGIC ROLE OF INTERNAL COMMUNICATION AT THE MINISTRY OF HOME AFFAIRS, IMMIGRATION, SAFETY AND SECURITY IN NAMIBIA(Namibia University of Science and Technology, 2023-05-04) KADHIKWA, SAKEUS IITA.Internal communication has long been the lifeblood of successfully and effectively managed organizations, especially when such communication is strategically managed. Though literature has established the benefits of internal communication, especially when it comes to employee engagement, commitment, and improved corporate reputation, research assessing the strategic role of internal communication in government departments is limited. The aim of the study was to assess the strategic role of internal communication at the Ministry of Home Affairs, Immigration, Safety and Security in Namibia. To achieve this objective, four secondary objectives were pursued. The first secondary objective focused on examining the challenges faced by the Department of Home Affairs and Immigration in implementing the internal communication strategy. The second objective focused on analyzing the impact of internal communication on service delivery, while the third focused on internal communication and employee morale. The last objective examined the alignment between internal communication and the department’s strategy. Data for the study were collected using a structured interview guide. Face-to-face online were conducted with a conveniently selected sample of 25 participants. The methodology that was employed was a qualitative research approach that followed a case study research design. Several challenges affecting the effective implementation of the internal communication strategy were identified. Furthermore, it was noted that participants had mixed views about the role of internal communication and service delivery, as well as its strategic role in the organization. Based on these findings, the study makes recommendations and suggestions for future research.Item DESIGNING A BRING YOUR OWN DEVICE SECURITY AWARENESS MODEL FOR MOBILE DEVICE USERS IN NAMIBIAN ENTERPRISES(NAMIBIA UNIVERSITY OF SCIENCE AND TECHNOLOGY, 2023-05-30) Shihepo, EsterThe phrase Bring Your Own Device (BYOD) also known as Dual-Use Devices is a mutual practice which has increased employees’ access to new mobile technologies and a rising trend within many organisations. The concept refers to employers allowing their employees to bring their personal mobile devices to workplaces and use them as their workstations. Enterprises are enjoying the benefits of BYOD, which allows them to cut operational costs as they do not need to purchase computers for their employees. Employees are enjoying the comfort and convenience offered by BYOD; however, this exposes organisations to security breaches. There is currently a lack of security awareness among mobile device users within enterprises against BYOD cyber threats. The situation has made it difficult for organisations to monitor the usage of resources among the mobile users towards protecting the confidentiality, integrity, and availability of corporate data. Moreover, cyber attackers see more potential with mobile devices as company and personal data get mixed up on such devices. Although the BYOD concept has not been formally implemented within the enterprise, it does not mean that the enterprise data is not prone to attackers. This study presents a BYOD Security Awareness Model designed following Design Science Research methods based on findings of a single case study conducted in one of the enterprises in Windhoek, Namibia. Qualitative research following the interpretivism philosophy was used. To select participants, purposeful random sampling method was used for this study. Data was collected using interviews, a questionnaire and through literature review. Furthermore, the study implemented the qualitative content analysis as the data analysis technique. The study identified malware and network spoofing as some of the BYOD related threats affecting the case enterprise. The researcher observed lack of end user awareness on BYOD security as a cause of BYOD related security threats. The study identified four main components of the model namely: BYOD threats, security awareness, policy and access control. The BYOD security awareness model will be a guideline to Namibian enterprises in creating BYOD security awareness among their mobile devices with the aim to safeguard the organisational data. Furthermore, the findings will also contribute to the new technology horizon of Namibia’s future BYOD security awareness by motivating enterprises to implement mechanisms that will protect the enterprise confidential information. Since Namibia is reported as one of the least ranked countries in Africa in terms of cyber security, the model is a guideline on how enterprises can create BYOD security awareness among users within their enterprises and improve their security posture as well as that of the nation. Additionally, the model will also contribute to the BYOD security awareness knowledge to researchers and practitioners through conference papers and thesis publication.Item Efficient hybrid deep reinforcement learning mechanism for distributed denial of service attack detection in software defined networks(NAMIBIA UNIVERSITY OF SCIENCE AND TECHNOLOGY, 2022-05) Musa, Gabriel, PreciousThe Internet architecture remains fixed since its invention but the Software Defined Network (SDN) comes with more flexibility, innovation, and programmability aspects being a very promising network architecture. However, the centralized control architecture in SDN represents a single point of failure. This vulnerability is prone to Distributed Denial of Service attack (DDoS) which remains a common and sophisticated attack on computer networks. With the controller faced with DDoS attacks while already overloaded with decision making, it raises a major security concern for SDN and therefore necessitates an efficient DDoS attack detection mechanism. This study aimed at designing a mechanism that accurately detects DDoS attacks while using minimum computational resources. We introduced a Hybrid Deep Reinforcement Learning Mechanism (HDRLM) for the SDN at the controller. An evaluation of literature was conducted to identify DRL algorithms that are accurate at the same time efficient. Double Deep Q-Network and Deep Q Network (DQN) were identified, and Deep Q-Network (DQN) was adopted in the study. To confirm the performance, simulated experimentation was used. Using the Design Science approach, a hybrid mechanism using the Deep Q Network algorithm that combines two different Deep Learning Neural networks for value approximation was designed. The HDRLM was demonstrated through experimentation in which the CICIDS2017 dataset was used to train and evaluate its performance. Detection accuracy of 98.16% was obtained and an 8% on CPU usage during detection, an improvement of the resource usage of the state-of-the-art detection mechanism. A positive upward trajectory of the accumulated rewards demonstrated that the mechanism was able to learn the environment by itself. Despite not achieving the highest accuracy, the HDRLM achieved a reasonably higher detection rate without consuming more computational resources compared to available mechanisms. This study provides a mechanism and an approach to designing mechanisms that reduce the cost of detectionItem FRAMING OF THE COVID-19 PANDEMIC IN NAMIBIAN NATIONAL NEWSPAPERS: AN ANALYSIS OF THE NAMIBIAN, NAMIBIAN SUN AND NEW ERA(NAMIBIA UNIVERSITY OF SCIENCE AND TECHNOLOGY, 2022-01) MUSHAYAVANHU, KUDAKWASHEMedia plays a central role in communicating risks to the public during outbreaks of infectious diseases. Since Covid-19 was first discovered, media has played a critical role in providing health information and people have relied on the media for information about Covid-19. While much of what the public knows about COVID-19 and ways to prevent infection has come through various media platforms, the framing or how such messages were presented, to some extent, influenced public’s understanding, perception and behaviours in light of the pandemic. The aim of this study was to determine how newspapers in Namibia reported on the COVID-19 pandemic. A total of three (3) national daily newspapers were selected for the study namely, The Namibian, New Era and Namibian Sun newspapers. The study was anchored on the framing theory and a qualitative research design was used for the study. The study focused on selected articles which were written between 13 March 2020 and 31 December 2020. Findings of the study reveal that newspapers used different frames to report on COVID-19. Specifically, the frames that were employed by the Namibian print media include, among others, the alarming frame, the social frame, the recovery and the assurance frame. It was found that newspapers mainly utilised war terminology and pessimistic language in their reporting. The themes that emerged in the framing of the COVID-19 pandemic focused on crime-related issues, the impact of the pandemic and the medical-related issues. The study argues that media frames, which were used by the three newspapers can influence public’s understanding and response to Covid-19 interventions. It is therefore imperative for the media to consider the frames or ways in which messages are packaged as frames of media messages could have serious implications on how messages are received and acted upon.Item A RECEPTION ANALYSIS OF SELECTED CARTOON MESSAGES ON COVID-19 BY UPPER PRIMARY SCHOOL LEARNERS AT HIGHLANDS CHRISTIAN SCHOOL IN WINDHOEK(NAMIBIA UNIVERSITY OF SCIENCE AND TECHNOLOGY, 2022-01-31) MUNDODZI, FAITHThis study investigated how cartoon message interpretation on Covid-19 influenced learners understanding and dealing with the Covid-19 pandemic and to establish learners’ knowledge of Covid-19 and exposure to Covid-19 cartoon messages. The study also sought to assess how Covid-19 cartoon messages influenced learners’ perception towards observing Covid 19 protocols. To investigate how cartoon message interpretation on Covid-19 influenced learners understanding and dealing with the Covid-19 pandemic, the research used the qualitative approach. A non-probability purposive sampling technique was employed intending to get information from 18 learners at Highlands Christian School. Data was collected through focus group discussions and recorded with the aid of a mobile device to record primary data as voice data and was transcribed data to verbatim. The researcher used a reductionist approach in that only summaries of responses were put into text and tabulated making use of the thematic analysis approach. The study revealed that all learners were aware of Covid-19 and what it entails through the word of mouth from parents and teachers at school, news, radio. Some leaners knowledge was acquired through watching Covid-19 cartoon messages on Namibian Broadcast and Corporation and YouTube channels. The majority of learners interpreted the cartoon messages they watched as intended by the producer. They found the videos useful, informative, interesting and educational. However, a smaller number of learners found the videos to be repetitive and lack adequate information they need. The findings from the study also revealed that Covid-19 cartoon messages influenced learners into changing their perceptions about Covid-19 and observing Covid-19 protocol. As a result, the study then recommended that the government of Namibia need to create effective, interesting, engaging, age appropriate and culturally sensitive content that will be disseminated to all urban and village learners. The cartoon videos should also be translated into several languages.Item Investigating Stakeholder Engagement in Public Relations Practices of two Namibian Universities (The International University of Management and Namibia University of Science and Technology(NAMIBIA UNIVERSITY OF SCIENCE AND TECHNOLOGY, 2021-04) Iita, NdamonakoThe purpose of the study was to establish the effectiveness of Public Relations Departments of IUM and NUST in stakeholder engagement. The main objective was to investigate the extent of stakeholder engagement by IUM and NUST’s public relations departments. The study was qualitative, with a population of all universities in Namibia. Purposive sampling was used to select two Universities namely IUM and NUST. Represented by the Marketing and Communication Department at NUST and the Marketing, Communication and Stakeholders Engagement Department at IUM. Interview guides were used to collect data. The findings of the study indicated that PRO’s roles are innumerable for each organisation but the most common areas are dealing with media, stakeholders and marketing the institution’s brand through event planning and social media as well as maintaining the image and reputation of the two Universities. It was also indicated that Public Relations Departments are of benefit to stakeholders of the two Universities because they act as custodians of the universities' brand and ensure that the institution's images are well positioned in the public eye. They are also the ears and eyes on the ground. Based on these findings, the following recommendations were made: Email and social media platforms are the most preferred forms of communication among the two Universities. The academic staff and the university management in general of the two universities need to take advantage of this, to make use of email and social media platforms to engage and preserve relationships with their stakeholders. The study also noted that to keep the effectiveness of the PRO of the two universities each communicated message should be carefully crafted for the various audiences to ensure maximum engagement. Information should also be made simple, summarizing the programs they offer to make it easier for the learners to understand.In managing stakeholders of the two Namibian universities, PRO’s of the two Namibian Universities should always fulfil their promises to their stakeholders as they are nothing without their stakeholders and always give correct information to meet stakeholder’s needs.Item ASSESSING THE USE OF SOCIAL MEDIA IN THE PUBLIC RELATIONS PRACTICES OF THE GOVERNMENT INSTITUTION PENSION FUND (GIPF) OF NAMIBIA(NAMIBIA UNIVERSITY OF SCIENCE AND TECHNOLOGY, 2022-01) KAPENDA, JAIRUS JULIUSSocial media have a significant role as a communication tool used by public relations practitioners in disseminating and sharing information as well as obtaining feedback from clients. When used effectively as a communication tool, social media can help address complaints, do market research to identify the needs of the clients and can help any organisation identify new market niches. Despite the essential role of social media, many organisations, including Government Institution Pension Fund (GIPF) Namibia are still not using social media to their maximum and are still opting for traditional methods of communication by the public relations practitioners. This study was conducted using a mixed-method approach. This study assessed the use of social media in public relations practices of the GIPF. The study adopted a quantitative approach, using a case study research design. The study population comprised GIPF staff members and clients. Hence, the sample consisted of 200 participants. A convenient sampling method was used to select a sample. A structured questionnaire with mixed questions collected both qualitative and quantitative data. The major findings of the study were that social media has a great impact on public relations practices and traditional social media platforms, notably Facebook, Instagram and Twitter remained popular. The recommendations were based on the research findings. Hence, the study recommends that various strategies can be used to enhance social media use and visibility. It was also recommended that public relations practitioners should be trained so that their awareness and use of social media is improved.Item AN EXAMINATION OF ETHICAL ISSUES CONFRONTING OPERATIONS OF SELECTED HYBRID MEDIA ORGANISATIONS IN THE DIGITAL AGE IN NAMIBIA(NAMIBIA UNIVERSITY OF SCIENCE AND TECHNOLOGY, 2021-12) ZVIYITA, ITAIThis qualitative study examined ethical issues confronting operations of selected hybrid media organisations in the digital age in Namibia. It specifically used the cases of Namibia Media Holdings and The Namibia, which are the biggest hybrid media organisations in Namibia in terms of readership and circulation thus far. The overall research question was: what contextual factors have shaped ethical dilemmas experienced by full-time journalists and news editors working for the Namibia Media Holdings (NMH) and The Namibian? It located itself with the interpretivism philosophical underpinning, in which a case study research design was used to as it provides room for observing multiple actors within specific contextual parameters. A total of sixteen (16) full-time employed journalists and five (5) news editors were selected using purposive sampling technique. Qualitative data were collected through the administration of focus group discussions and interviews. Thematic analysis was used to analyse data, in which emerging themes were categorised, labelled and interpreted in response to each research question. Key findings indicate that a number of contextual factors responsible for shaping ethical dilemmas encountered by professional journalists working for selected hybrid media organisations in the digital age in Namibia. These include: the ever-changing technological landscape; the immediacy of internet; media sustainability; the integration of social media platforms in the news work; the absence of specific ethical framework for hybrid media organisations; conflicts of interest; and the notion of public interest. It came out clear that traditional media ethics such as accuracy, truthfulness and impartiality are still relevant to inform operations of hybrid newsrooms and are also cornerstones without, which there is no professional journalism. In order to inform the operations of hybrid media organisations in the digital age in Namibia, the following media accountability ethical frameworks and policies were cited: revision of the Code of Ethics for the Namibian Print, Broadcast and Online Media; strengthening of digital fact-checking mechanisms; and additional journalistic training.
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