Researcher: Wandile Lesejane, University of the Witwatersrand, Johannesburg
Supervisor: Prof Nukri Komin, University of the Witwatersrand, Johannesburg

The H.E.S.S is one of the best IACTs and is crucial for studying cosmic particles, particularly gamma induced particles. It is able todetect particles with energies ranging from tens of GeV to TeV. The challenge stems from the influx of the hadronic air showers which are more common and can obscure the detection of gamma particles. Deep Neural Networks were employed to discriminate the gamma events from hadron events using data that was simulated using KASKADE and SMASH softwares. The model had a performance accuracy of 97.42% and a loss of 7.21% at its best and an accuracy of 53% at its poorest.

Researcher: Ndamulelo Innocent Nelwamondo, University of the Witwatersrand, Johannesburg
Supervisor: Dr Farai Mlambo, University of the Witwatersrand, Johannesburg

The cryptocurrency market is recognized for its intense uncertainty and instability, and people are still searching for a reliable and convenient way to direct cryptocurrency trading. An overall of 4 models, namely ARIMA, LSTM, attention-based LSTM, and hybrid attention based LSTM-ARIMA were used to forecast the prices Bitcoin in which the hybrid attention-based LSTM-ARIMA model on wavelet denoised Bitcoin prices with MSE = 42816.51, RMSE = 206.92, MAE = 167.34 and R2 = 0.8172 was found to be the best fitting model.

Researcher: Ronewa Nemalili, University of the Witwatersrand, Johannesburg
Supervisor: Professor Bruce Mellado, University of the Witwatersrand, Johannesburg

One of the good ways in which the government can provide better services to its people is through surveying informal settlements, which is why an improvement is always needed on the techniques of surveying them. Fortunately in this project through DSI-NICIS NEPTTP funding the classification of informal settlements were improved through surveying all kinds of dwellings instead of only focusing the areas which were known to be consisted of informal settlements.

Researcher: Ayrton Altorio, University of the Witwatersrand, Johannesburg
Supervisor: Prof. Nicole de Wet-Billings, University of the Witwatersrand, Johannesburg

Climate change, as we know it, has been a key point of academic discussion for many decades. Throughout these years there has been scarce research interest directed towards the relationship between climate change and human fertility outcomes. This study aims to investigate the relationship between climate change and human fertility by employing Poisson regression analyses to longitudinal cross national data for 109 countries over the fifteen year time period spanning 2000 to 2015. Our dependent variable is Total Fertility Rate. The two main independent variables are associated with climate change and measure (1) annual precipitation levels and (2) percentage of arable land. The control variables span a range of demographic indicators that are known to be accurate predictors of TFR, such as GDP per capita. The results of our Poisson regression models indicate that both of our climate change indicators are significant in predicting change in TFR, with changes in arable land having the largest estimate of all our predictor variables.

Researcher: Lucas Banda, University of the Witwatersrand, Johannesburg
Supervisor: Prof. Nicole de Wet-Billings, University of the Witwatersrand, Johannesburg

There is limited and not so latest information from nationally representative data that exist about the prevalence of hypertension among the youth 15-34 ‘years olds’ in South Africa, though the prevalence is over 30% among the adult population 15+ (Seedat, Rayner, and Veriava, 2014). Youth in South Africa are faced with challenges such as unemployment and environments that expose them to poor diet habits, to alcohol and sub-stance abuse (Peltzer and Phaswana-Mafuya, 2013). Thus, understanding the probability of hypertension among the youth, 15-34 years in South Africa, conditional to demographic, socioeconomic and behavioral factors is critical.

Researcher: Babalwa N.C. Dingiswayo, University of the Witwatersrand, Johannesburg
Supervisors: Mr Michael Jana and Prof. Rod Alence, University of the Witwatersrand, Johannesburg

The aim of this research is to assess how adequate is access to antenatal care (ANC) for South African women, particularly the provision of ANC with South African women living with HIV, in public hospitals. The objective of the study would be to define the relationship ANC in public health care in SA as well testing of HIV as a part of ANC to pregnant patients.

• Assessing the adequacy of ANC according to the World Health Organisation (WHO) standards.
• How accessible is ANC in public health care for South African women?
• What is the relationship between selected socio-economic factors, HIV status and ANC attendance by South African women?

Researcher: Ruan Jean du Randt, University of Pretoria
Supervisor: Dr. Ritesh Ajoodha, University of the Witwatersrand, Johannesburg

Mapping chords from the magnitude spectrum to symbolic-notation makes way for a plethora of advances in algorithmic music. This will allow chord in musical accompaniment to be recognised and described in a symbolic way. It also makes great advances toward algorithmic music composition. This research shows various methods to map the magnitude spectrum to symbolic-notation using algorithmic chord recognition.

This research also evaluates the importance of various features within the magnitude spectrum when considering algorithmic chord recognition. The results show that Mel Frequency Cepstral Coefficients are the most important features, and that the Fuzzy Lattice Reasoning classifier obtain the highest accuracy with 99.0417%. This provides an effective way of mapping chords from the magnitude spectrum to symbolic notation and gives a foundation for future research.

Researcher: Olatomiwa Akinlaja, Sol Plaatje University
Supervisor: Dr M.S Mosia, Sol Plaatje University

Reviews and ratings influence us every day. We have all experienced reviews and ratings in one form or the other, usually from the applications and online platforms that require us to leave a review based on our experience. Many authors have asked the question; should we analyze the writer’s intentions or perceptions. Multiple studies have been conducted within the area of Natural language processing (NLP) in order to extract value from text. We use deep learning and sentiment analysis to extract value from reviews in order to justify its respective rating.

Researcher: Yasmin Sizwe, University of the Witwatersrand, Johannesburg
Supervisors: Professor Rod Alence, University of the Witwatersrand, Johannesburg

This research will be testing the theory of group threat which hypothesizes that economic conditions and immigrant populations are the main influences of negative perceptions towards immigrants in host countries. This analysis will be specifically be looking at Gross domestic product (growth) and Net migration as the main predictors, which a perception index will be regressed against these predictors. This analysis specifically uses a multiple regression model as a means of determining this relationship. In using this model, it is concluded that Net migration is more influential on perceptions than economic conditions in shaping perceptions of immigrants. This relationship is further explored below.

Researcher: Nompumelelo Sibiya, University of the Witwatersrand, Johannesburg
Supervisors: Mr Rendani Mbuvha, University of the Witwatersrand, Johannesburg

Due to increasing popularity associated with artificial intelligence, machine learning and data availability, most banks are renewing their business models. Credit risk predictions is one of the most vital keys to evaluation measure and decision making. This study establishes two binary classifiers based on machine learning models namely; Support Vector Machines and Gradient Boosting Machines as well as the classic Logistic Regression on real credit card data in predicting lo an default probability. Results reveal that class distribution has a major effect on the performance of the models, how ever, gradient boosting can cater to this and produce robust performance.