Browsing by Author "Mijwil, Maad M."

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    Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance.
    (Kabale University, 2024) Hussein, Alkattan; Alhumaima, Ali Subhi; Oluwaseun, Adelaja A.; Abotaleb, Mostafa; Mijwil, Maad M.; Pradeep, Mishra; Sekiwu, Denis; Bamwerinde, Wilson; Turyasingura, Benson
    The study deals with the use of data mining techniques to build a classification model to predict students' academic performance. The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispensable tools in the field of education analysis. Special emphasis was placed on the use of algorithms such as decision trees. The study includes an analysis of factors that affect students' academic performance such as previous academic achievement in educational activities, as well as social and psychological factors. Classification models were applied using the KNIME platform and the WEKA tool to analyze students' performance in three courses: database technology, artificial intelligence, and image processing in the ICT degree program. The results showed that the use of decision trees can effectively classify students' performance and determine the success and failure rates. The cruel outright mistakes, RMS errors, and relative supreme mistakes all showed 0% whereas the kappa esteem obtained from the analysis extended between 0.991 and 1.00 which significantly concurs with most statistical values.
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    Foreign Direct Investment and Environmental Challenges: A Case Study of Uganda With Analytical Perspective.
    (Kabale University, 2024) Byanyima, Faustino; Mayanja, Edison; Kadengye, Damazo T.; Arineitwe, Shine; Mijwil, Maad M.; Gaballa, Moustafa; Cherakkara, Veedu Rajeev; Turyasingura, Benson
    This chapter examines the connection between foreign direct investment (FDI) and environmental degradation in Uganda from 1990 to 2022. It includes control variables such as GDP growth, trade openness, urbanization, industrialization, and agricultural activity, utilizing data from the World Bank's World Development Indicators. Using the Autoregressive Distributed Lag (ARDL) model to tackle endogeneity, the study finds a significant long-term non-linear relationship between FDI and CO2 emissions, aligning with the Environmental Kuznets Curve (EKC) hypothesis. The results indicate an inverse U-shaped relationship, where carbon emissions initially increase with FDI before declining over time. The research highlights the influence of GDP growth, urbanization, agriculture, and industrialization on environmental outcomes. The findings stress the need for policymakers to balance attracting FDI with maintaining environmental sustainability, supporting the pollution haven theory in Uganda.