nepttp – National e-Science Postgraduate Teaching and Training Platform

The own-race bias effect in South Africa

Written by nepttp15 July 2024

Researcher: Inonge Lupinda, University of the Witwatersrand, Johannesburg
Supervisors: Dr Colin Tredoux, University of Cape Town and Dr Helen Robertson, University of the Witwatersrand, Johannesburg

Face recognition is critical for human connection and perceptual ability, and mistakes in face recognition can degrade the quality of social interaction. Own-race bias (ORB) in facial recognition refers to the tendency to recognize people from one’s own racial group more accurately than people from other racial groupings. This research examines the prevalence and presence of the ORB phenomena in human face recall, its various explanations, and causes, and the serious implications it has in eyewitness cross-racial identification, using a South African demographic of Black, White, and Coloured people. It explores how social inclusion or exclusion exacerbates this phenomenon, especially considering the historical context of South Africa. Embedded in ORB are social-cognitive factors such as implicit bias, racial attitudes, social motivation, and the contact hypothesis.

Good Governance is the Driving Force for Participation Amongst IGO’s & TGI’s

Written by nepttp14 July 2024

Researcher: Octavia Ndlovu, University of the Witwatersrand, Johannesburg
Supervisor: Prof. Oliver Westerwinter, University of the Witwatersrand, Johannesburg

Link to YouTube Video

This paper gives a foundation to the first of many research paper that look into testing the relationship between good governance and the participation of Intergovernmental organisations in the type of Transnational governance initiatives. I hope to achieve these results through the use of two data sets that will be merged for the best results and that is using Transnational Public-Private Governance Initiatives in World Politics Data, together with data from Worldwide Governance Indicator. This paper doesn’t hope to achieve much beyond the scales of knowing if good governance is the reason of the participation og so much Intergovernmental organisation, I hope the results of this paper will help policy makers into making policies that will help benefit the world at a global scale, where IGOs have a good governance reason for being a part of TGIs.

Academic Journals

Written by nepttp12 July 2024

Akinlaja, O. & Mosia, M., 2021. Using deep learning and sentiment analysis to identify mismatches between online courses’ reviews and ratings. In: 2021 3rd International Multidisciplinary Information Technology and Engineering Conference (IMITEC), Windhoek, Namibia, pp.1–6. doi:10.1109/IMITEC52926.2021.9714655.

Antwi, A., Kammies, E.T., Chaka, L. & Arasomwan, M.A., 2025. Forecasting South African grain prices and assessing the non-linear impact of inflation and rainfall using a dynamic Bayesian generalized additive model. Frontiers in Applied Mathematics and Statistics, 11. doi:10.3389/fams.2025.1582609.

Baloyi, N., Mellado, B. & Ruan, X., 2021. Discrimination of signal-background events with supervised and semi-supervised learning in the search for new bosons decaying to the Z + γ final state. In: SAIP Conference. [online] Available at: https://events.saip.org.za/event/144/contributions/1492/attachments/284/388/NBaloyi_SAIP_Proceedings.pdf [Accessed 21 Sep. 2021].

Bokgoshi, L., Sixhaxa, K., Jadhav, A., Nyamane, S. & Ajoodha, R., 2023. Enhancing timely graduations: An explainable AI approach to predict academic risks in South African students. In: 2023 International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, pp.1–7. doi:10.1109/ICECET58911.2023.10389444.

Bonnet, W. & Celik, T., 2021. Random sampling-based relative. IEEE Geoscience and Remote Sensing Letters, 9321136, pp.1–4.

Byamugisha, J., Saib, W., Gaelejwe, T., Jeewa, A. & Molapo, M., 2020a. Abstract PR-12: Towards verifying results from biomedical deep learning models using the UMLS: Cases of primary tumor site classification and cancer Named Entity Recognition. Association for the Advancement of Artificial Intelligence, PR-12-PR-12. Available at: https://doi.org/10.1158/1557-3265.adi21-pr-12.

Chabumba, D.R., Jadhav, A. & Ajoodha, R., 2021. Predicting telecommunication customer churn using machine learning techniques. Interdisciplinary Research in Technology and Management, 14 September, pp.625–636.

Chabumba, R., Ajoodha, R. & Jadhav, A., 2021. Predicting telecommunication customer churn using machine learning techniques. In: International Conference on Interdisciplinary Research in Technology and Management. [online] Available at: www.kaggle.com.

Daniel, L.O., Mashao, D., Olukanmi, P. & Singh, G., 2025. Network slicing for mitigating interference in dense deployments. In: 2025 IEEE 3rd Wireless Africa Conference (WAC), Pretoria, South Africa, pp.1–6. doi:10.1109/WAC63911.2025.10992603.

Daniel, L.O., Sigauke, C., Chibaya, C. & Mbuvha, R., 2020. Short-term wind speed forecasting using statistical and machine learning methods. Algorithms, 13(6).

Daniel, L.O., Singh, G., Mashao, D. & Olukanmi, P., 2024. Optimized coordinated resource allocation and power control for wireless communication networks. In: 2024 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Port Louis, Mauritius, pp.1–10. doi:10.1109/icABCD62167.2024.10645229.

Desai, P. & Harvey, R., 2023. Is the Southern African Development Community afflicted by premature deindustrialisation? The Africa Governance Papers, 1(4). Available at: https://tagp.gga.org/index.php/system/article/view/55 [Accessed 12 Aug. 2025].

Desai, P., 2023. The link between population density, developmental outcomes and perceptions of governance in sub-Saharan Africa. The Africa Governance Papers, 1(3). Available at: http://160.119.143.6/index.php/system/article/view/42.

Essa, Y. et al., 2024. Classifying single versus multiple ground strike point lightning flashes from high-speed lightning videos using deep learning. In: 37th International Conference on Lightning Protection (ICLP), Dresden, Germany, pp.297–302.

Essa, Y., Ajoodha, R. & Hunt, H.G.P., 2020. A LSTM recurrent neural network for lightning flash prediction within Southern Africa using historical time-series data. In: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE). IEEE.

Essa, Y., Hunt, H.G.P. & Ajoodha, R., 2021. Short-term prediction of lightning in Southern Africa using autoregressive machine learning techniques. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), Toronto, ON, Canada, pp.1–5. doi:10.1109/IEMTRONICS52119.2021.9422493.

Essa, Y., Hunt, H.G.P., Gijben, M. & Ajoodha, R., 2022. Deep learning prediction of thunderstorm severity using remote sensing weather data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15(June), pp.4004–4013. doi:10.1109/JSTARS.2022.3172785.

Freeborough, W. & van Zyl, T., 2022. Investigating explainability methods in recurrent neural network architectures for financial time series data. Applied Sciences (Switzerland), 12(3), pp.1–15.

Freeborough, W., Gentle, N. & Rey, M.E.C., 2021. WRKY transcription factors in cassava contribute to regulation of tolerance and susceptibility to cassava mosaic disease through stress responses. Viruses, 13(9), p.1820. Available at: https://doi.org/10.3390/v13091820.

Harvey, R., Morrison, S. & Desai, P., 2025. Is South Africa afflicted by the resource curse? The Extractive Industries and Society, 23, p.101678. Available at: https://doi.org/10.1016/j.exis.2025.101678.

Hayes, B., Ashwal, L.D., Khumalo, K.B. & Iaccheri, L.M., 2024. Major, trace element and Sr-Nd isotope evidence for a sublithospheric mantle source for the Umkondo large igneous province. Geoscience Frontiers, 15(1), p.101719. doi:10.1016/j.gsf.2023.101719.

Kgoale, T., Whata, A., Nasejje, J.B., Rad, N.N. & Mulaudzi, T., 2024. Estimating average and individual treatment effects in the presence of time-dependent covariates. In: Chen, D.G. & Coelho, C.A. (eds.) Biostatistics Modeling and Public Health Applications. Emerging Topics in Statistics and Biostatistics. Springer, Cham. doi:10.1007/978-3-031-69690-9_5.

Khumalo, K.B., Ashwal, L.D., Hayes, B., Iaccheri, L.M., Meintjes, P.G. & Webb, S.J., 2024. Neoarchean lavas of the Ventersdorp Large Igneous Province, South Africa: Sr-Nd-Hf isotopic and trace element evidence for a long-lived plume beneath a stationary African continent. Earth-Science Reviews, 252, p.104752. doi:10.1016/j.earscirev.2024.104752.

Langa, K., Wang, H. & Okuboyejo, O., 2025. Parameter-efficient fine-tuning of pre-trained large language models for financial text analysis. In: Gerber, A., Maritz, J. & Pillay, A.W. (eds.) Artificial Intelligence Research. SACAIR 2024. Communications in Computer and Information Science, vol. 2326. Springer, Cham. doi:10.1007/978-3-031-78255-8_1.

Lange, R., Lange, T. & Van Zyl, T.L., 2020. Predicting particle fineness in a cement mill. In: Proceedings of 2020 23rd International Conference on Information Fusion (FUSION).

Mabunda, J.G.K., Jadhav, A. & Ajoodha, R., 2021. A review: Predicting student success at various levels of their learning journey in a science programme. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), Toronto, ON, Canada, pp.1–5. doi:10.1109/IEMTRONICS52119.2021.9422519.

Mabunda, J.G.K., Jadhav, A. & Ajoodha, R., 2021. Sentiment analysis of student textual feedback to improve teaching. In: Interdisciplinary Research in Technology and Management. CRC Press, pp.643–651.

Magoma, P. & Chibaya, C., 2021. Towards a CIA compliant RSA hybrid built on an artificial neural network. In: 2021 3rd International Multidisciplinary Information Technology and Engineering Conference (IMITEC), Windhoek, Namibia, pp.1–9. doi:10.1109/IMITEC52926.2021.9714634.

Magoma, P. & Chibaya, C., 2021. Towards a CIA compliant RSA hybrid built on an artificial neural network. In: 2021 3rd International Multidisciplinary Information Technology and Engineering Conference (IMITEC). IEEE.

Makubyane, K. & Maposa, D., 2024. Forecasting short- and long-term wind speed in Limpopo Province using machine learning and extreme value theory. Forecasting, 6(4), pp.885–907. doi:10.3390/forecast6040044.

Malatsi, T.D. & Kara, A.H., 2022. Invariance, conservation laws and reductions of some classes of “high” order partial differential equations. Transactions of the Royal Society of South Africa, 77(3), pp.255–270. doi:10.1080/0035919X.2022.2164629.

Masangu, L., Jadhav, A. & Ajoodha, R., 2021. Predicting student academic performance using data mining techniques. Advances in Science, Technology and Engineering Systems, 6(1), pp.153–163.

Matsane, L., Jadhav, A. & Ajoodha, R., 2020. The use of automatic speech recognition in education for identifying attitudes of the speakers. In: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE).

Mawela, V. & Chibaya, C., 2020. Generation of virtual reality environments in which to evaluate swarm adherence to prescribed control rules. In: 2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC), pp.9–12.

Michael, T.N., Obagbuwa, I.C., Whata, A. & Madzima, K., 2023. A comparative modeling and comprehensive binding site analysis of the South African Beta COVID-19 variant’s spike protein structure. In: Lahby, M., Pilloni, V., Banerjee, J.S. & Mahmud, M. (eds.) Advanced AI and Internet of Health Things for Combating Pandemics. Springer, Cham. doi:10.1007/978-3-031-28631-5_18.

Mngadi, N., Ajoodha, R. & Jadhav, A., 2020. A conceptual model to identify vulnerable undergraduate learners at higher-education institutions. In: 2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC). IEEE, pp.1–8.

Mohale, V.Z. & Obagbuwa, I.C., 2024. Assessing the impact of climate variability on wheat yield in Bloemfontein wheat farms through time series analysis. Edelweiss Applied Science and Technology, 8(5), pp.1213–1234.

Mohale, V.Z. & Obagbuwa, I.C., 2024. Poverty analysis and prediction in South Africa using remotely sensed data. Applied Computational Intelligence and Soft Computing, 2024, p.5137110. doi:10.1155/2024/5137110.

Mohale, V.Z. & Obagbuwa, I.C., 2025. A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhance transparency and interpretability in cybersecurity. Frontiers in Artificial Intelligence, 8, p.1526221. doi:10.3389/frai.2025.1526221.

Mohale, V.Z. & Obagbuwa, I.C., 2025. Evaluating machine learning-based intrusion detection systems with explainable AI: Enhancing transparency and interpretability. Frontiers in Computer Science, 7, p.1520741. doi:10.3389/fcomp.2025.1520741.

Mugware, F.W., Ravele, T. & Sigauke, C., 2025. Short-term predictions of global horizontal irradiance using recurrent neural networks, support vector regression, gradient boosting random forest and advanced stacking ensemble approaches. Computation, 13(3), p.72.

Mugware, F.W., Sigauke, C. & Ravele, T., 2024. Assessing the predictive power of machine learning models for wind speed prediction under different weather conditions. [Details incomplete for full citation].

Mugware, F.W., Sigauke, C. & Ravele, T., 2024. Evaluating wind speed forecasting models: A comparative study of CNN, DAN2, Random Forest and XGBOOST in diverse South African weather conditions. Forecasting, 6(3), pp.672–699. Available at: https://doi.org/10.3390/forecast6030035.

Mulangaphuma, M.P., Chibaya, C. & Madzima, K., 2021. A dynamic nDES model for hiding datasets for machine learning. In: 2021 3rd International Multidisciplinary Information Technology and Engineering Conference (IMITEC), Windhoek, Namibia, pp.1–5. doi:10.1109/IMITEC52926.2021.9714631.

Mutavhatsindi, T., Sigauke, C. & Mbuvha, A.R., 2020. Forecasting hourly global horizontal solar irradiance in South Africa using machine learning models. IEEE Access, 8, pp.198872–198885.

Nemavhola, A., Chibaya, C. & Ochara, N.M., 2021. Application of the LSTM – deep neural networks – in forecasting foreign currency exchange rates. In: 2021 3rd International Multidisciplinary Information Technology and Engineering Conference (IMITEC). IEEE.

Nemavhola, A., Chibaya, C. & Viriri, S., 2025. A systematic review of CNN architectures, databases, performance metrics, and applications in face recognition. Information, 16(2), p.107. doi:10.3390/info16020107.

Nemavhola, A., Viriri, S. & Chibaya, C., 2025. A scoping review of literature on deep learning techniques for face recognition. Human Behavior and Emerging Technologies, 5979728, pp.1–14. doi:10.1155/hbe2/5979728.

Nengwani, M., Bhero, E. & Chibaya, C., 2024. Towards an aquatic school inspired swarm intelligence ontology. In: 2024 4th International Multidisciplinary Information Technology and Engineering Conference (IMITEC), Vanderbijlpark, South Africa, pp.147–151. doi:10.1109/IMITEC60221.2024.10851048.

Ngwenduna, K.S. & Mbuvha, R., 2021. Alleviating class imbalance in actuarial applications using generative adversarial networks. Risks, 9(3), pp.1–33.

Nhlapho, W., Atemkeng, M., Brima, Y. & Ndogmo, J.-C., 2024. Bridging the gap: Exploring interpretability in deep learning models for brain tumor detection and diagnosis from MRI images. Information, 15(4), p.182. doi:10.3390/info15040182.

Nkolele, R. & Wang, H., 2021. Explainable machine learning: A manuscript on the customer churn in the telecommunications industry. In: 2021 Ethics and Explainability for Responsible Data Science (EE-RDS), Johannesburg, South Africa, pp.1–7. doi:10.1109/EE-RDS53766.2021.9708561.

Nkolele, R., 2020. Mapping of narrative text fields to ICD-10 codes using natural language processing and machine learning. In: Proceedings of the Fourth Widening Natural Language Processing Workshop. Association for Computational Linguistics (ACL), pp.131–135.

Ntsenge, K.Y., 2022. Indigenous women moving from physical to digital fires: the evolution of methods of transmission of indigenous knowledge. In: Digital Humanities in Precarious Times. [online] Available at: https://humanities.nwu.ac.za/humanities/digital-humanities-precarious-times [Accessed 12 Aug. 2025].

Nyamane, S., Abd Elbasit, M.A.M. & Obagbuwa, I.C., 2024. Harnessing deep learning for meteorological drought forecasts in the Northern Cape, South Africa. International Journal of Intelligent Systems, 7562587, pp.1–22. doi:10.1155/2024/7562587.

Nyamane, S., Jadhav, A. & Ajoodha, R., 2023. Predicting academic success in blended learning environments: A probabilistic Bayesian approach leveraging student trajectory data. In: Proceedings of the International Conference on Information Systems and Emerging Technologies (ICISET). Available at: https://ssrn.com/abstract=4663410.

Obagbuwa, I.C., Mohale, V.Z. & Makade, M., 2023. Anti-lock braking system using Monte Carlo simulations. In: Das, A.K., Nayak, J., Naik, B., Vimal, S. & Pelusi, D. (eds.) Computational Intelligence in Pattern Recognition. CIPR 2022. Lecture Notes in Networks and Systems, vol. 725. Springer, Singapore. doi:10.1007/978-981-99-3734-9_53.

Ohamadike, N. & Orakwe, E.C., n.d. The role of education in the public perception of corruption in Sudan and Zimbabwe. Politeia, pp.1–11. doi:10.25159/2663-6689/13663.

Ohamadike, N., 2022. Measuring political accountability in Africa using a multi-item index. The Africa Governance Papers, 1(2), pp.32–47. Available at: https://tagp.gga.org/index.php/system/article/view/23.

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Ratshilengo, M., Sigauke, C. & Bere, A., 2021. Short-term solar power forecasting using genetic algorithms: An application using South African data. Applied Sciences (Switzerland), 11(9).

Sibanda, K., Gundu, T. & Whata, A., 2020. Assessing the credibility of South Africa’s anti-retroviral treatment (ART) eligibility guidelines using regression discontinuity designs. In: 2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC), Kimberley, South Africa, pp.1–5. doi:10.1109/IMITEC50163.2020.9334077.

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Thulare, E., Ajoodha, R. & Jadhav, A., 2021. An empirical analysis and application of the expectation-maximization and matrix completion algorithms for varying degrees of missing data. In: 2021 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA). IEEE.

Evaluating banking customer churn Deep Learning model (Deep Neural Network) in terms of explainability

Written by nepttp11 July 2024

Researcher: Hlayisani Khoza, University of Limpopo
Supervisor: Dr Hairong Bau, University of the Witwatersrand, Johannesburg

In the financial industry forecasting, client attrition is critical. The expense of hiring fresh clients is significantly greater than the expense of maintaining current clients. There are several Machine Learning (ML) models in use to forecast client attrition, and the results are impressive. The issue with these models is their lack of openness and interpretability. Deep Learning (DL) models are the leading models in this development because of the layered non-linear structure, which provides no insight into how they reach at their results, these strong models have been dubbed “Black Boxes”. To deal with this problem Explainable methods have been used to explain these model outcomes. In this research, Shapley Additive method has been applied to the Multilayer Perceptron model to explain its result and it has been found to explain banking customer churn better.

International Commitments and domestic enforcement against drugs

Written by nepttp10 July 2024

Researcher: Raquel Baeta, University of the Witwatersrand, Johannesburg
Supervisor: Dr Oliver Westerwinter, University of the Witwatersrand, Johannesburg

State commitment to the Single Convention on Narcotic Drugs of 1961, the Convention on Psychotropic Substances of 1971, and the Convention against Illicit Drugs and Psychotropic Substances of 1988 influence domestic regulations and the rate of drug seizures.

Climate Change and Agricultural Production in Developing Countries

Written by nepttp9 July 2024

Researcher: Tshepiso Segone, University of the Witwatersrand, Johannesburg
Supervisor: Prof. Rod Alence, University of the Witwatersrand, Johannesburg

Agricultural development in developing countries is essential for alleviating poverty. Compared to developing countries, the rate of agricultural development in developing countries is drastically delayed. Climate-related threats to agricultural production can disturb economic processes and welfare.

Application of Machine Learning Algorithms for Rainfall Forecasting in KwaZulu Natal Province, South Africa

Written by nepttp8 July 2024

Researcher: Jeremiah Ogunniyi, Sol Plaatje University
Supervisor: Dr Ibidun Obagbuwa , Sol Plaatje University

This study applied three machine learning algorithms namely Linear Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) to predict precipitation in KwaZulu Natal Province, South Africa. The result shows that SVM had the best performance followed by RF and finally LR.

Assessing Financing for Climate Change Mitigation and Adaptation for Developing Countries

Written by nepttp7 July 2024

Researcher: Puseletso Maile, University of the Witwatersrand, Johannesburg
Supervisor: Prof. Rod Alence, University of the Witwatersrand, Johannesburg

In 2010 leaders from developed nations pledged to contribute a $100 billion year on year towards climate finance for developing countries. The reason for this is because developed countries are the biggest polluters and because developing countries are most vulnerable to climate change and because they do not have the resources to respond to extreme weather conditions. This study assesses trends in climate finance for developing countries looking at whether: There was a significant increase in climate finance after 2010. Trends in climate finance at regional level and income group level. If climate finance provided for mitigation was higher than for adaptation. And what are determinants for climate finance.

This study applied three machine learning algorithms namely Linear Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) to predict precipitation in KwaZulu Natal Province, South Africa. The result shows that SVM had the best performance followed by RF and finally LR.

Prediction of breast cancer using machine learning algorithms

Written by nepttp6 July 2024

Researcher: Sedzani Ndou, University of Venda
Supervisors: Prof. O. Olugbenga

Breast cancer is a deadly disease mostly in women’s health all over the world, as it is considered the second most deadly cancer after lung cancer. To predict breast cancer we implement six different algorithms. We try to pick the best algorithm for this purpose by comparing their performance accuracy. What is/are the most effective machine learning algorithms for predicting and diagnosing breast cancer?

Issuing of loans is associated with risks

Written by nepttp5 July 2024

Researcher: Merriam Thoka, University of Limpopo
Supervisor: Dr Hairong Bau, University of the Witwatersrand, Johannesburg

Tree-based machine learning (ML) models are non-linear predictive models utilized today due to their accuracy and efficiency, but understanding their decisions has received very little attention. Recently, banks are adopting ML to compute credit score because utilizing ML or AI in credit scoring is attentive to real-time signs of a potential borrower’s creditworthiness. The goal of the study is to create an interpretable credit scoring model that borrowers and banks can utilize to anticipate if a lendee will be able of paying back their debt, as well as to comprehend the logic behind the model’s prediction.

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