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.

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.

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.

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.

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?

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.

Researcher:  Percival Shimange, University of the Witwatersrand, Johannesburg
Supervisor: Dr R Maluta, University of Venda

Our environment’s climate change has effects on energy consumption, generation, systems, and infrastructure. In the last decade, the demand for alternative energy conversion and storage devices has increased significantly.  This project uses organic solar cells from the Harvard Clean Energy Project to predict energy band gaps using machine learning models to accelerate power supply. The HOMO-LUMO GAP was predicted using a machine learning regression model trained on HOMO, LUMO, power conversion efficiency, open circuit potential, and short circuit density.

Researcher: Malahlane Komane, University of Limpopo
Supervisor: Dr Alphonce Bere, University of Venda

The study uses a recursive partitioning approach. To investigate the age at first marriage for women living in South Africa. There are well known methods of recursive partitioning which includes CART and random forest. The aim of the research is to construct a discrete survival tree for the identification of determinants of age at first marriage for SA women.

Researcher:  Lulamile Mtebeni, University of the Witwatersrand, Johannesburg
Supervisor: Prof. Rod Alence, University of the Witwatersrand, Johannesburg

This research analyses the assumption that the root cause of terrorism is poverty and other forms of socioeconomic deprivation. By analyzing all terrorist incidents between 1997 to 2020, this research sought to contribute further to a field of study that has already enjoyed its fair share of consideration from policymakers, scholars, and the general public. Through a sequence of multiple regressions, the research ultimately found that the relationship between the said variable is statistically insignificant – thereby meaning that any sort of relationship between the said variables is indirect.

Researcher:  Kola Ijasan, University of the Witwatersrand, Johannesburg
Supervisor: Dr Babatunde Oluwayomi, University of the Witwatersrand, Johannesburg

Increase in Land Value – conventional urban land economics theories argue that transportation costs are a major element in land value.  The availability of transportation infrastructure such as motorways or train lines will dramatically increase access to services, jobs, and amenities.  According to Du and Mulley (2006), with businesses centred in particular areas and residences in another, decreasing transportation costs becomes a determining factor in the choice and demand for residential houses; and hence their value.
Demand and Supply – There is a two-way link between transportation infrastructure and land value.  There are two types of relationships: demand driven and supply driven. The supply-driven relationship states that the provision of additional transportation infrastructure will result in increased land value surrounding the enabled infrastructure. According to the demand-driven relationship, an increase in land value leads to the provision of additional transportation infrastructure.