Masters of Science in Information Systems
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- ItemDevelopment of Models For Non-Word Error Detectionfor and Correction System for Kiswahili Language(Kampala International University, 2023-11) Mulingi, YonoThe development of effective spellcheckers for Kiswahili, such as the JAMBO SPELL CHECKER (2004), has marked significant progress; however, a noticeable research gap persists in the field of non-word error detection and correction systems tailored specifically for the Kiswahili language. This study addresses this gap by proposing and implementing enhanced models, SwaDetect and SwaCorrect, designed to adeptly detect and rectify non-word errors in Kiswahili. With a focus on addressing limitations in scope, speed, and accuracy prevalent in existing solutions, our research aims to pioneer a more comprehensive and efficient system for non-word error detection and correction in Kiswahili. Notably, experimental results demonstrate SwaDetect's exceptional accuracy of 99% in Kiswahili word detection, operating at a processing speed of 65 Hz (65 words per second), while SwaCorrect proficiently corrects erroneous words with an average accuracy of 82% for Edit Distance One (ED1) through ED3, maintaining an overall correction speed of 58 Hz (58 words per second). Our study encapsulates the development of models crucial for advancing the accuracy and speed of non-word error detection and correction systems dedicated to the Kiswahili language
- ItemSimulation study on the performance of robust outlier labelling methods(Kampala International University, 2023-10) Abdiweli, Ahmed JamaThe identification and labeling of outliers play a crucial role in data analysis and modeling tasks. Robust outlier labeling methods aim to accurately identify observations that deviate significantly from the majority of the data points while being resilient to noise, measurement errors, and data corruption. In this simulation study, we evaluate the performance of various robust outlier labeling methods using synthetic datasets. To conduct the study, we defined the simulation setup by specifying the characteristics of the datasets, including the number of variables, sample size, distributional assumptions, and proportion of outliers. Synthetic datasets were generated based on these specifications, incorporating both normal observations and outliers with known characteristics. A set of robust outlier labeling methods was selected for evaluation. These methods were designed to effectively handle outliers and provide reliable labels. Implementation of the selected methods was carried out using a programming language, ensuring proper application to the generated datasets. Performance metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) were defined to assess the effectiveness of the outlier labeling methods. Each method was applied to the synthetic datasets, and the results were recorded. The performance metrics were calculated based on the known labels of the synthetic outliers. The collected results were analyzed and compared to identify the strengths and limitations of each robust outlier labeling method. The performance metrics were used to assess accuracy, robustness, and computational efficiency. To ensure the reliability of the findings, the simulation study was repeated with different simulation setups and datasets, validating the consistency of the results across multiple iterations. Based on the findings, conclusions were drawn regarding the performance of the evaluated robust outlier labeling methods. The most effective methods for the specific characteristics of the datasets used in the study were identified. These findings provide valuable insights for researchers, practitioners, and data analysts in choosing appropriate outlier labeling methods for their data analysis and modeling tasks. In summary, this simulation study contributes to the understanding of the performance of robust outlier labeling methods and provides a systematic evaluation framework for comparing and selecting suitable methods in the presence of outliers.
- ItemPerformance Evaluation Model of Thin Client Technology For Latency Sensitive Applications In Higher Institutions Of Learning(Kampala International University, 2023-11) Eliot, ArinaitweThin-client technology gained popularity in higher institutions of learning due to its costeffectiveness, scalability, and ease of management. However, it faces significant performance challenges, particularly in latency-sensitive applications such as streaming multimedia, motion graphics, and animations. This study, conducted at KIU, aimed to address these issues by designing a performance evaluation model of thin-client technologies for latency sensitive applications. The research objectives included identifying factors influencing thin-client performance, evaluating thin-client performance at KIU, and designing an efficient model for implementation.A controlled experiment procedure was conducted basing on ISO and IEEE standards. Exact measurements were collected and recorded in continuous numerals and a statistical analysis was done. Also, subjective test with questionnaire were employed and quantitative data collected for the same. The developed model, based on a 100MBS switch and CAT5 network media, revealed a logarithmic relationship between the number of clients and system latency. Initially, latency increases slowly as clients are added, but this escalation becomes more pronounced beyond a certain threshold of approximately five clients. This finding highlights the importance of optimizing network capacity and resources to minimize latency and enhance system performance. This study provided valuable insights for institutions like KIU to enhance the performance of thin-client technologies in latency-sensitive applications, ultimately benefiting both teaching and student activities.
- ItemTime series analysis on effect of infrastructural investments on ugandan GDP growth rate(2023-08) Yahye, Abdikadir OsmanTo study aimed at analyzing the impact of infrastructural investments in transport, education and health on Ugandan GDP growth rate using time series analysis from 1985-2021. The study objectives were to determine the trend of infrastructural investments variables withGDP growth rate, secondly to assess the relationship between infrastructural investments variables onGDP growth and to determine the magnitude and direction of the relationship infrastructural and GDP growth. The study was a longitudinal time series data analysis for the period of 37 years. The study analysis was based on descriptive, inferential and model checks to determine the reliability of the analysis. The study findings show that the average of investment rate in transport was 9.270, health had 4.356, and education was 2.969, their respective median values were 8.420, 4.70 and 2.890 respectively. GDP growth rate had 6.0656 and its median was 6.300. These two values are close to each other indicating minor symmetry with the variable. The trend of infrastructural investments and that of GDP growth rates are generally not high in the study. Secondly the study found that infrastructural investments is significantly is related to GDP growth rate 1985-2021. In the findings, its indicated that the presence of infrastructural development is deemed to significantly generate the GDP growth rate. Thirdly findings indicate that infrastructural investments had a moderate and statistically significant relationship on GDP growth rate in Uganda’s economy (1985-2021). In this case therefore, it’s pivotal to argue that the occurrence of the infrastructural investments has had a moderate effect on the GDP. First the study conclude that the trend of Infrastructural investments in Uganda since 1985 to date has generally been increasing with low increment, the status of the investments of infrastructures is limited in the existence of the environment of controls. Secondly, the study found that infrastructural investments have significantly is related to GDP growth rate 1985-2021. In the findings, its indicate that the presence of infrastructural development is deemed to significantly generate the GDP growth rate. The study found that infrastructural investments is related and can be used to attain the improvement in the GDP growth rate in the country over time.Thirdly, the findings indicate that infrastructural investments had a moderate and statistically significant relationship on GDP growth rate in Uganda’s economy (1985-2021). In this case therefore, it’s pivotal to argue that the occurrence of the infrastructural investments has had a moderate effect on the GDP. The results indicate that the state of infrastructural investments in the country has a direct effect and contribute to the GDP growth rate in Uganda; there is need for them to concentrate on the infrastructural investments for their countries. The first objective recommend that there has been some investments in transport compared to health and education which remain lagging behind, it’s pivotal therefore that the infrastructural investments be improved in performance functionality, the infrastructures such as health and education need to be seriously invested in to generate the coherence to their performance. Secondly it’s recommended that infrastructure investments be improved through investing more in the health systems in order to increase the system functionality. There is need for streamlining the policy on education to make it more skillful so as to encourage job creators for economic development. The budget of the education systems was low and hence need to be increased if human development can be improved for generating the country growth. Thirdly the study recommend that there is need for increased design on work connected to enable the performance of the infrastructures of the communities in health and education through allocating significant resources to the education sector development.
- ItemSolow-swan Model For the Analysis of the Effect Of Foreign Direct Investments in ugandan economic growth(Kampala International University, 2023-02) Mohamed, Nur AbdiThe tittleof the dissertation was Solow-Swan Model for the Analysis of the effect of Foreign Direct Investments in Ugandan Economic Growth.