Researcher: Mpho Modise, University of the Witwatersrand, Johannesburg
Supervisor: Dr S. Verkijika, University of the Witwatersrand, Johannesburg

In our digital lives, smartphones have become indispensable tools, and the Google
Play Store stands as a central platform for mobile applications. User reviews on this
platform significantly influence consumer decisions. Nevertheless, the sheer volume
of reviews poses a formidable challenge. To address this issue, this study suggests a
comprehensive approach employing algorithms to assess the quality, relevance, and
credibility of reviews.

Researcher: Lerato Tlale, University of the Witwatersrand, Johannesburg
Supervisor: Helen Sarah Robertson, University of the Witwatersrand, Johannesburg

Urban safety’s correlation with holistic quality of life in South Africa’s evolving urban landscape demands comprehensive exploration and understanding.
South Africa’s population, estimated at 58 million in2019 and projected to reach 65 million by 2030 and 70 million by 2043, underscores the significance of quality of life amid rising urbanization rates.
The exploration of the relationship between elements of the built environment, like urban safety, and quality of life is pivotal in shaping present and future urban developments in South Africa.

Researcher: Small Tshithavhana, University of the Witwatersrand, Johannesburg
Supervisor: Dr. Walter Mudzimbabwe, University of the Witwatersrand, Johannesburg

Financial forecasting has become increasingly important in today’s global market due to its ability to
assess risk and inform decision-making. However, accurately forecasting financial markets is challenging
due to their stochastic nature and complexity. To address this challenge, we suggest a state space
model, namely the Hidden Markov Model, which handles dynamic time series issues involving unseen
variables or parameters that represent the development of the underlying system’s state.we test
our model on financial market information sourced from the Nasdaq online database and compare its
performance with standard forecasting machine learning models. The results under the MAPE matrix
indicate that the proposed model outperformed the Recurrent Neural Network (RNN) by 19.08% and
exhibited a superior performance of 19.09% relative to the ARIMA model. However, the proposed
model fell short in comparison to the GARCH model by a margin of 3.11%.

Researcher: Tebogo Malatsi, University of the Witwatersrand, Johannesburg
Supervisor: Assoc. Prof. YudhvirSeetharam, University of the Witwatersrand, Johannesburg

This study replicates prior research on default risk assessment using ensemble and deep learning techniques, leveraging payment data from the UCI Machine Learning Repository for Taiwan. It specifically compares boosting, random forest, and neural network models with Tanh and ReLUactivations, evaluating their predictive accuracy and classification capabilities through metrics like accuracy, AUC, ROC curve, and F-Score.
The study reveals trade-offs in model performance metrics, with heightened accuracy but nuanced reductions in AUC/F-Score. Random Forests demonstrate superior learning and generalization, while neural network models emphasize the challenge of balancing sensitivity, specificity, precision, and recall.

Researcher: Netshamutshedzi Ndivhuwo, University of Venda
Supervisor: Dr Ibidun Obagbuwa, University of the Witwatersrand, Johannesburg

This study investigates email monitoring systems using machine learning. In this study, we contribute to previous studies on spam problems to improve accuracy by utilising a variety of methods.

Researcher: Moris Madike, University of Limpopo
Supervisor: Prof Justine Nasejje, University of the Witwatersrand, Johannesburg

Researcher: Fhulufhelo Mugware, University of Venda
Supervisor: Prof C. Sigauke, University of the Witwatersrand, Johannesburg

Researcher: Dimpho Mosaka, University of the Witwatersrand, Johannesburg
Supervisor: Dr Martins Arasomwan, University of the Witwatersrand, Johannesburg

More studies have demonstrated that the DeepNeural Networks(DNN)techniques can be used to forecast the stock market price. This research project aims to investigate the effectiveness of deep neural network techniques in the SouthAfrican stock market price forecastinhusing Long-Short TermMemory(LSTM) and Convolutional Neural Network( CNN) technique, including the challenges and opportunities of this approach

Researcher: Mufunwa Nemushungwa, University of the Witwatersrand, Johannesburg
Supervisor: Dr Martins Arasomwan, University of the Witwatersrand, Johannesburg

This research compares three variations of SOM: standard SOM, SOM combined with K-means, and SOM paired with the social spider optimization (SSO) algorithm in performing customer segmentation. The results show that combined SOM with SSO algorithm outperforms the the paired SOM and K-means in
enhancing the SOM clustering performance.dress road damage problems.

Researcher: Vhahangwele Netshilonwe, Sol Plaatje University
Supervisor: Dr. Michael Olusanya , Sol Plaatje University

This study investigates how machine learning can be used to detect road damage, specifically potholes, in images of roads. The study tests a particular machine learning model called the “one-class support vector
machine (OCSVM)” to determine if it can effectively identify road damage better than random chance. The objective is to enhance our capability to detect and address road damage problems.