Masters Degrees (DECE)

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    Dialogical system design across cultural boundaries
    (2001) Winschiers, Heike; ; ;
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    Capacity for public-private sectors to implement e-governance (CP-PSIe): Enhancing information and service delivery in Namibia.
    (2014) Funda, Semba
    e-Governance is being used by many governments around the world to improve information and service delivery to their citizens. This is achieved mainly through the use of Information and Communication Technology (ICT) solutions. As a result, the capacity for Public and Private Sectors to implement e-Governance plays a pivotal role in making e-Governance a success. Through the use of qualitative and quantitative research methods, this study took an in-depth look at the current levels of capacity in Namibia. The study found that the lack of available capacity in the country is a critical barrier in many e-Government initiatives in Namibia causing them to move at a very slow pace. Furthermore, it was also found that the majority of the Namibian citizens do not know what e-Governance is and are not aware of any e-Government projects that are already underway. It is evident that the development of capacity in the country would impact positively on e-Government projects and would also reduce Namibia’s dependence on foreign and usually expensive consultants to pilot them. In this vein, the study proposed ways in which the available capacities could be utilised. It further made suggestions on how the missing capacities could be acquired and also suggested how the Public and Private Sectors could collaborate with each other for the efficient delivery of e-Governance in Namibia. Lastly, but not the least, the study concludes by emphasizing that the Namibian Government urgently needs to prioritise the development of e-Government capacity to ensure that e-Governance implementation becomes a success.
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    Echo state network approach for radio signal strength prediction applied to cellular communication frequency bands in northen Namibia
    (University of Namibia, 2018) Gideon, Kenneth
    Reliance on mobile connectivity has led to demands for wireless spectrum capacity to grow on a daily basis resulting to congested networks. Ensuring acceptable levels of Quality of Service (QoS) for users in wireless communication systems, through continuous wireless network analysis using simulation tools based on radio propagation models has become increasingly prominent. To provide automated analytical model building, the use of machine learning methods has been considered to predict characteristics of the wireless channel. Thus, in this work, a method for predicting radio signal strength using Echo State Networks (ESNs) is proposed and applied to three different locations in Northern Namibia. This method aims at providing a better approach for radio signal strength prediction, which leads to improvements in wireless communication planning, design and analysis. Its performance is compared with the Support Vector Regression (SVR) method optimized for radio propagation modeling. Simulations are conducted in Python using propagation data measured from the three locations based on the following four performance metrics: goodness of fit criteria; error measures; computation complexities; and F-Test for statistical model comparison. Simulation results show that the ESN gives a better prediction accuracy in terms of the goodness of fit criteria and the error measures (i.e. average R2 = 0.82 and average mean absolute error (MAE) = 0.0312 for ESN compared to 0.648 and 0.0624 for SVR), but it is inferior to the SVR in terms of computation complexities (i.e. average training complexity of 410 ms and average testing complexity of 79.0 ms for ESN compared to 8.19 ms and 1.04 ms for SVR). In addition, results from the F-Test also indicates that the ESN provides a significantly better fit than the SVR.
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    Assistive technology for students with dyslexia at Eros Girls School
    (University of Namibia, 2019) Veiko, Veiko
    Assistive Technology for Children with Learning Disabilities (ATCLD) was developed in response to the language and arithmetic challenges faced by learners with dyslexia of Eros Girls School (EGS). This development followed from requirement elicitation and is intended for grades 5 - 7. Having discussed the above, Assistive Technology (AT) is any item, piece of equipment or product system, whether acquired commercially off the shelf, modified, or customised, that is used to increase, maintain, or improve the functional capabilities of children with disabilities (“Assistive Technology Device”, 2004). As a counterexample to numerous schools in Namibia, EGS enrols and educates pupils diagnosed with learning difficulties in regular teaching and learning environment. Equally, the Ministry of Education (2009) states that Namibian classes have a wideband mixed ability range of learners, and learners with special educational needs are often included in mainstream school. In response to the dilemma outlined, this study developed ATCLD which is characterised with text to speech features to enable compensatory learning that emphasises repetition. The endeavour of ATCLD pursued the following methods; the Initial and final stage utilised qualitative; case study and quantitative; experimental techniques respectively. Mixed methods state the blend of these approaches. The inferential statistics of the ATCLD, a part of the text – speech assistive technologies of Namibia now, associate younger children with most improved mark. This implies that younger children have the capacity to create new schemas for information. It is reasonable to acquaint them with basic knowledge, since doing this at a later stage may implicate. Furthermore, the widespread input and output text to speech and speech to text assistive technology would expand this research in the future.
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    An intrusion detection system using recurrent neural network with a real-world dataset
    (University of Namibia, 2020) Aludhilu, Hilma Ndapewa
    Computer network technologies have grown speedily in the last decade and they are susceptible to numerous intrusions. Recently, Deep Leaming methodologies for example Recurrent Neural Networks have been the new trend for building Intrusion Detection System (IDS). However, Recurrent Neural Network (RNN) IDS can be biased, as datasets from real-world production networks are mostly not used during the training of IDSs. The purpose of this study is to develop an IDS using RNN with two datasets from real-world networks and one synthetic dataset. The study aims to create a dataset from real-world network traffic, train an RNN model and test the corresponding IDS using several datasets: from real-world networks (Kyoto and UNAM datasets) and also synthetic (NLS-KDD dataset). Finally, the performance of the RNN-IDS is evaluated. A quantitative research design, with the experimental approach, is used for this study. An experiment is carried out to compare several RNN architectures: basic RNN, LSTM and GRU, offering insight into how the RNN architectures perform when trained and tested with real-world and synthetic datasets. The results of the study show that the GRU model outperformed other RNN architectures with an accuracy score of 95% using the real-world datasets and 97% using the synthetic dataset. The study concludes that the GRU model performs well with real-world datasets. Furthermore, the study recommends finding methods to solve the imbalance of classes in real-world datasets, without turning the data into synthetic data. It is also recommended to consider the environment in which the IDS will work in, before choosing the best model to be used.