Researcher: Shalati Baloyi, University of the Witwatersrand, Johannesburg
Supervisor: Prof. Glen Nwaila, University of the Witwatersrand, Johannesburg

Mining companies are subject to high risk and uncertainty caused by an underestimation or overestimation of mineral resources. Over the years, various techniques have been implemented to estimate mineral resources. This research compared the effectiveness of Ordinary Kriging (OK) and Sequential Gaussian simulations (SGSIM) in the estimation and quantification of uncertainty in mineral resources. Mineral grade data was transformed and used to quantify spatial continuity using variograms. Unsampled locations were estimated using OK and SGSIM was used to quantify uncertainty. The results found suggested that SGSIM is more effective in estimating mineral grade and quantifying its uncertainty compared to OK.

Researcher: Tshepiso Rangongo, University of Pretoria
Supervisor: Renate Thiede and Dr Inger Fabris-Rotelli, University of Pretoria

Remote sensing (RS) is the process of obtaining information about something without making physical contact with it.  Remote sensing images are processed to help identify areas. This process is known as land cover image classification.  There exists many algorithms that can classify land from images, and many ways of assessing their performances.

Researcher: Beauty Gama, , University of the Witwatersrand, Johannesburg
Supervisors: Prof. Rod Alence Prof. Sumaya Laher, University of the Witwatersrand, Johannesburg

Digital transformation has become an integrated part of our lives. Minimal inclusion on the impact people’s experiences have on transformation. Digital transformation cyclically interact with technology, business & society. Digital divide still poses implementation challenges. Ecological development & modernisation captures the socio-psychological influences.

Researcher: Naledi Mogane, University of the Witwatersrand, Johannesburg
Supervisors: Dr Ritesh Ajoodha and Dr Ashwini Jadhav, University of the Witwatersrand, Johannesburg

Forecasting rainfall is a critical technique for preventing climate-related risks and ensuring long-term management. This study presents the application of neural networks to predict rainfall. To accomplish this, we used a Long Short-Term Memory (LSTM), a type of recurrent neural network (RNN), and we compared the results with the Autoregressive Integrated Moving Average (ARIMA) model. The statistical effectiveness of the models reveals that the LSTM model can predict monthly rainfall in the catchment with reasonable accuracy.

Researcher: Kudzai Sibanda, University of the Witwatersrand, Johannesburg
Supervisor: Dr Tapiwa Gundu, Sol Plaatje University

In a digital world where we put our lives on our gadgets, we deserve assurance that our data is safe from any external entities. We want our gadgets to notify us of any threat to our privacy and a way to deal with these threats. We need intrusion detection systems.

Researcher: Palesa Khoza, University of the Witwatersrand, Johannesburg
Supervisor: TBA

This study reviewed 18128 discharge records issued in the department of surgery at Chris Hani Baragwanath Academic Hospital and examined 28 fields to assess the field completion rate of electronic discharge summaries.  To evaluate the quality of information in electronic discharge summaries, a word count of three clinically significant fields was conducted.

Researcher: Emma Smith, University of the Witwatersrand, Johannesburg
Supervisor: Prof. Rod Alence , University of the Witwatersrand, Johannesburg

South Africa experiences high levels of protest across various sectors. Police response to protests is varied and can lead to scenarios where excessive force is used on seemingly small or unthreatening protests.  This research report asks the following: Which protest characteristics increase the likelihood of a protest being met with police repression?

Researcher: Andre Kleynhans, University of Pretoria
Supervisor: Prof. Ritesh Ajoodha, University of the Witwatersrand, Johannesburg

Bitcoin (BTC) return prediction is a fairly difficult task. Past literature focuses on models to ensure the best return prediction. Little focus is on discovering the best features for bitcoin return prediction.  This research report attempts to bridge this gap. This research report finds the most important features from commonly used features used in the literature.

Researcher: Moloko Legodi, University of Limpopo
Supervisor: TBA

A report by World Health Organization support that cancer is the global health problem, responsible for roughly 10 million deaths in 2020. The goal of this research is to look at factors that may lead to death in people with cancer. The goals of this study is to (a) analyze the data using the Poisson regression model, Negative Binomial regression, and Zero Inflated Poisson to see which model best fit the data, and (b) look for factors that lead to death in cancer patients. Data from Statistics South Africa (StatsSA) on causes of death was analyzed, and the study looked at 22441 people who died from cancer as reported by the Department of Home Affairs in South Africa in 2015 (SA). Descriptive statistics and model fitting were used to analyze the data. The data was analyzed first using the Poisson regression model, then an detection of over dispersion in count variable Negative Binomial regression, and ZIP, where negative binomial was the best model to fit the data. The model fitted from Negative binomial revealed that the age group, place of death, province of death, and smoking status have a significant contribution on the number of cancer deaths at a 5\% significant level, while the variable gender was in significant to the model. Cancer patients should use nursery homes in the future so that they can be closely monitored while undergoing treatment. The country’s health department should strengthen cancer education for youth for the sake of the country’s cancer future state, as youth are the country’s future.

Researcher: Tshegofatso Sekgobela, University of Limpopo
Supervisor: TBA

In earlier years, deep learning, a subset of machine learning that comprises methods like convolutional neural networks, was extensively researched and used in a range of industries, including agriculture.