Lecturer - Computer Science
Dr Elliot Mbunge
Email: embunge@uniswa.sz
Telephone: (+268) 25170344
Office: By Old Pension Fund Offices
Personal Details
Dr E Mbunge
PhD in Information Technology
MSc in Information Systems, BSc (Hons) Information Technology
Lecturer - Computer Science
Research Areas
- ICT4D
- Malaria
- Soft Computing
- Health Informatics
- Mobile Computing
Courses taught
- Web Technology and Development
- Modern Operating Systems
- Computer Skills Foundation
- Computer Applications
Research Profile
- Google Scholar: https://scholar.google.com/citations?user=Ju-NyZ0AAAAJ&hl=en
- Researchgate: https://www.researchgate.net/profile/computer_science
Published Journal Articles
- Mbunge, E., Sibiya, M. N., Takavarasha, S., Millham, R. C., Chemhaka, G., Muchemwa, B., & Dzinamarira, T. (2023). Implementation of ensemble machine learning classifiers to predict diarrhoea with SMOTEENN, SMOTE, and SMOTETomek class imbalance approaches. 2023 Conference on Information Communications Technology and Society (ICTAS), 1–6. https://doi.org/10.1109/ICTAS56421.2023.10082744
- Mbunge, E., Fashoto, S. G., & Bimha, H. (2022). Prediction of box-office success: a review of trends and machine learning computational models. International Journal of Business Intelligence and Data Mining, 20(2), 192. https://doi.org/10.1504/IJBIDM.2022.120825
- Akinnuwesi, B. A., Fashoto, S. G., Mbunge, E., Odumabo, A., Metfula, A. S., Mashwama, P., … Amusa, O. O. (2021). Application of intelligence-based computational techniques for classification and early differential diagnosis of COVID-19 disease. Data Science and Management, 4, 10–18. https://doi.org/10.1016/J.DSM.2021.12.001
- Akinnuwesi, B. A., Uzoka, F.-M. E., Fashoto, S. G., Mbunge, E., Odumabo, A., Amusa, O. O., … Owolabi, O. (2022). A modified UTAUT model for the acceptance and use of digital technology for tackling COVID-19. Sustainable Operations and Computers, 3, 118–135. https://doi.org/10.1016/j.susoc.2021.12.001
- Govender, P., Fashoto, S. G., Maharaj, L., Adeleke, M. A., Mbunge, E., Olamijuwon, J., … Okpeku, M. (2022). The application of machine learning to predict genetic relatedness using human mtDNA hypervariable region I sequence. PLOS ONE, 17(2), e0263790. https://doi.org/10.1371/JOURNAL.PONE.0263790
- Mbunge, E., Fashoto, S., Mafumbate, R., & Nxumalo, S. (2022). Diverging Hybrid and Deep Learning Models into Predicting Students’ Performance in Smart Learning Environments – A Review. 182–202. https://doi.org/10.1007/978-3-030-93314-2_12
Selected Conference Papers
- Mbunge, E., Fashoto, S. G., Muchemwa, B., Millham, R. C., Chemhaka, G., Sibiya, M. N., Dzinamarira, T., & Buwerimwe, J. (2023). Application of machine learning techniques for predicting child mortality and identifying associated risk factors. 2023 Conference on Information Communications Technology and Society (ICTAS), 1–5. https://doi.org/10.1109/ICTAS56421.2023.10082734
- Mbunge, E., Millham, R. C., Sibiya, M. N., & Takavarasha, S. (2022). Application of machine learning models to predict malaria using malaria cases and environmental risk factors. 2022 Conference on Information Communications Technology and Society (ICTAS), 1–5. https://doi.org/10.1109/ICTAS53252.2022.9744657
- Mbunge, E., Fashoto, S., Mafumbate, R., & Nxumalo, S. (2022). Diverging Hybrid and Deep Learning Models into Predicting Students’ Performance in Smart Learning Environments – A Review. 182–202. https://doi.org/10.1007/978-3-030-93314-2_12