Researcher: Rambevha Vhukhudo, University of Venda
Supervisor: Dr Wilbert Chagwiza, University of the Witwatersrand, Johannesburg

Volatility is an essential factor to consider when one is trading or investing in cryptocurrency Ethereum.
Cryptocurrency as a digital currency in which transactions are verified and records make use of a decentralized system. This study focuses on forecasting the volatility of the price of Ethereum.

Researcher: Nnaemeka Ohamadike, University of the Witwatersrand, Johannesburg
Supervisor: Dr Ekeminiabasi Eyita-Okon, University of the Witwatersrand, Johannesburg

This study ascertained whether education aid to West African countries facilitates educational development. This was undertaken given the increased aid to the region since the Millennium Declaration was signed; the shift in development policy at the new Millennium; and the inadequacy of literature on the subject matter in West Africa.  To do this, two research questions were formed:

What impact has education aid had on educational development?

What has been the state of education sectors in West Africa?

Researcher: Mutobvu Ronewa, University of Limpopo
Supervisor: TBA

Mental health problems encompass our overall well-being.  These are the most formidable problems than any other health condition and remain widely under-reported.  This study focuses on performing differential gene expression secondary analysis on Schizophrenia.  Schizophrenia is a mental health disorder that can be characterized by hallucinations, delusions, and abnormal social behavior (Xu et al., 2012).  RNA-seq is a technology that is used to study the complex disease associated with genes expression variations.

Researcher: Tshilisanani Mudau, University Venda
Supervisor: Dr Michael Klipin and Prof. Hima Vadapalli, University of the Witwatersrand, Johannesburg

Machine Learning models have become the modern way to protect patient health information. Which Machine Learning model can best perform this task?

The Conditional Random Fields model easily recognized sensitive Health Information when given more or less information. Random Forest only recognized sensitive Health Information when more information is given. The patient sensitive information learnt by models is replaced with fake but meaningful information to protect the owner of the information from being traced.

Researcher: Gift Mahlatse Mphahlele, University of the Witwatersrand, Johannesburg
Supervisor: TBA

This study aimed to investigate the nature of the relationship between the global news sentiment with the local news sentiment and exchange rates for two countries, South Africa and Nigeria.  The transformer architecture was used to do sentiment analysis through a sentiment analysis pipeline.  The Pearson’s correlation test showed that there is no significant relationship between the global news sentiment with the local news sentiment and exchange rates for both the countries News Bias and Fairness in Emerging Markets.

Researcher: Christina Meletakos, University of the Witwatersrand, Johannesburg
Supervisors: Prof. Rod Alence and Dr Ekeminiabasi Eyita-Okon, University of the Witwatersrand, Johannesburg

Questions were asked as to the role corruption plays in the building and production of the following renewable energies (RE) across the world: Hydropower, Biopower, Wind power, & Solar energy. Multiple regression was used to understand this relationship.

Researcher: Nkosenhle Mdluli, University of the Witwatersrand, Johannesburg
Supervisors: Rudzani Mulaudzi and Dr Ritesh Ajoodha, University of the Witwatersrand, Johannesburg

As of quarter 2 2021, South Africa officially became the country with the highest unemployment rate in the world. The rate currently sits at 34, 4% and is the highest it has ever been in the recorded history of the country. To better inform policy decisions that can reduce this rate, it is important that the structure of the South African labor market be understood.

Bayesian networks, a type of probabilistic graphical model, were used to construct a structure showing how the unemployment rate is related to other macro-economic variables. Three models were constructed using the hill climbing algorithm with the best scoring model attaining an auc score of 0.895. These models were used to perform inferences on the unemployment rate. An increase in FGE was found to have the most desirable impact as it decreased the UR the most.

This research thus provided valuable insight into the labor market structures and will help act as a reference point for researchers and policy makers in South Africa.

Researcher: Neo Matsobane, University of the Witwatersrand, Johannesburg
Supervisor: Dr Wilbert Chagwiza, University of the Witwatersrand, Johannesburg

The idea of predicting stock prices has always appealed to both financial investors and researchers. The stock market is unforeseeable in nature whereby financial Investors consistently enquire if the cost of a stock will increase or not. New technologies like data mining, machine learning and deep learning helps to examine large information and build up a model that keeps away from human mistakes during stock predictions. The purpose of this study is to build a recurrent neural network (RNN), specifically long-short-term memory model (LSTM) that predict stock market.

Researcher: Rosa Manoim, University of the Witwatersrand, Johannesburg
Supervisor: Dr Michelle Small, University of the Witwatersrand, Johannesburg

According to the South African government, “the advancement of human rights and the promotion of democracy are pillars on which South Africa’s foreign policy rests”. Yet as the statistical results here show, this commitment towards respecting and promoting human rights is not reflected in South Africa’s arms exports.

Researcher: Rinae Tshivhidzo, University of the Witwatersrand, Johannesburg
Supervisor: Dr Wilbert Chagwiza, University of the Witwatersrand, Johannesburg

Researchers have devoted their focus to studying stock market prediction. However, predicting the stock trend is still an open question. With the development of the advanced period, the prediction of stock market has moved to the field of machine learning. In many kinds of research that have been conducted, machine learning techniques have proven to be robust in predicting stock market. This research aimed to train a model that accurately predicts stock market based on fundamental analysis using a random forest algorithm. The model is trained on the US stock market using fundamental analysis. The results show that the random forest model succeeds in the prediction of stock market. Hence random forest was able to predict stock market with a 0.93 degree of accuracy.