Browsing by Author "Bamwerinde, Wilson"

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    Content and dynamics of nutrients in the surface water of shallow Lake Mulehe in Kisoro District, South–western Uganda
    (Springer, 2023-06-13) Saturday, Alex; Kangume , Susan; Bamwerinde, Wilson
    The purpose of this study was to investigate the content and dynamics of nutrients in the shallow (max. 6 m) Lake Mulehe. We collected 54 water samples from nine sampling stations between the wet season (March–May 2020 and dry season (June–August 2020). Nutrients; ammonia–nitrogen (NH4–N), nitrate–nitrogen (NO3–N), nitrite–nitrogen (NO2–N), total nitrogen (TN), total phosphorus (TP) and soluble reactive phosphorus (SRP) were investigated in accordance with APHA 2017 standard procedures. Besides, physical parameters: Temperature, pH, turbidity, electrical conductivity and dissolved oxygen were measured in situ. The water quality index (WQI) was used to determine the water quality of Lake Muhele  using drinking water quality standards developed by the Uganda National Bureau of Standards and the World Health Organization. Results indicated that nutrients (TN, NO3–N, TP, NH4-N, NO2–N and SRP) did not difer substantially between study stations (p>0.05) but did reveal signifcant diferences (p<0.05) across study months. Besides, nutrient levels difered signifcantly between seasons (p<0.05) except for SRP and NH4–N. The WQI values varied from 36.0 to 74.5, with a mean of 58.69. The recorded overall WQI value places Lake Mulehe’s water quality into the ‘poor’ category in terms of worthiness for human consumption. The study, therefore, recommends continuous pollution monitoring and enforcement of local regulations to reduce pollution in the lake as a result of anthropogenic activities.
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    ItemOpen Access
    Content and Dynamics of Nutrients in the Surface Water of Shallow Lake Mulehe in Kisoro District, South–western Uganda.
    (Kabale University, 2023) Saturday, Alex; Kangume, Susan; Bamwerinde, Wilson
    The purpose of this study was to investigate the content and dynamics of nutrients in the shallow (max. 6 m) Lake Mulehe. We collected 54 water samples from nine sampling stations between the wet season (March–May 2020 and dry season (June–August 2020). Nutrients; ammonia–nitrogen (NH4–N), nitrate–nitrogen (NO3–N), nitrite–nitrogen (NO2–N), totalnitrogen (TN), total phosphorus (TP) and soluble reactive phosphorus (SRP) were investigated in accordance with APHA 2017 standard procedures. Besides, physical parameters: Temperature, pH, turbidity, electrical conductivity and dissolvedoxygen were measured in situ. The water quality index (WQI) was used to determine the water quality of Lake Muhele using drinking water quality standards developed by the Uganda National Bureau of Standards and the World Health Organization.Results indicated that nutrients (TN, NO3– N, TP, NH4-N, NO2–N and SRP) did not differ substantially between study stations (p > 0.05) but did reveal significant differences (p < 0.05) across study months. Besides, nutrient levels differed significantly between seasons (p < 0.05) except for SRP and NH4– N. The WQI values varied from 36.0 to 74.5, with a mean of 58.69. The recorded overall WQI value places Lake Mulehe’s water quality into the ‘poor’ category in terms of worthiness for human consumption. The study, therefore, recommends continuous pollution monitoring and enforcement of local regulations to reduce pollution in the lake as a result of anthropogenic activities.
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    ItemOpen Access
    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.