Automated payment fraud detection using logistic regression and support vector machines by Thetard, Heinrich Mathias

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Analysis of the behaviour of flexibility parameters in intralogistics systems by Reinhart, Marvin Markus

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Application of statistics and machine learning in healthcare by Van der Merwe, Schalk Gerhardus

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Application of machine learning to retirement fund preservation : identifying significant variables in retirement fund preservation decisions

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A learning design framework for active learning using audience response systems by Faasen, Marinda

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Non-cooperative games on networks by Van der Merwe, Martijn

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Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model by Makhaba, Limpho Mapulane

“…Traditional systems used in wildfire spread prediction are often limited in accounting for the dynamic and complex factors influencing fire behaviour. To address this challenge, this research uses a multidisciplinary approach that combines Machine Learning techniques, remote sensing data, and wildfire science to develop an advanced wildfire prediction system. …”
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Innovation capability and adoption of innovation in Namibia's road freight transport industry by Kangombe, Anna Tusiloshenda

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Prediction of hepatitis B in adult patients in the Gambia using machine learning algorithms. by Asare, Dorcas

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Caregiver, child and family characteristics associated with parenting stress in rural KwazuluNatal by Mitchell, Joanie

“…Two factors were linked to a reduced likelihood of PS, namely the child being helped to learn shapes and sizes at home and maternal participation in a breastfeeding study. …”
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Modelling highly imbalanced credit card fraud detection data using statistical learning by Moodley, Revesa

“…Whilst different sampling techniques are generally used to reduce the imbalance, minimal studies have focussed on the effect the level imbalance has on the predictive capabilities of various statistical learning techniques. This study investigates the effect of three factors on model performance: 1) sampling technique, 2) supervised learning method, and 3) prevalence rate, also known as imbalance ratio (IR), which refers to the proportion of majority class samples compared to that of the minority class. …”
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An investigation into table grape risk factors that affect quality along the export supply chain by Rossouw, Christopher Guillaume

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Cape Town road traffic accident analysis: Utilising supervised learning techniques and discussing their effectiveness by Du Toit, Christo

“…In this thesis, multinomial logistic regression, classification trees (CT), random forests (RF), gradient boosted machines (GBM) and artificial neural networks (ANN) are used to model the potentially non-linear relationships between accident-related factors and injury-severity. …”
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Thuto: Depth Analysis of South African and Sierra Leone School Outcomes using Machine Learning

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Development of a human digital twin for human-centric dispatching for an assembly process by Kneissl, Anja

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Public understanding of global climate change in Malawi : an investigation of factors influencing perceptions, attitudes and beliefs about global climate change by Bakuwa, Japhet

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An analysis of prognostic, endoscopic and transjugular intrahepatic portosystemic salvage shunting factors influencing rebleeding and death in portal hypertensive patients with lif... by Krige, Jacobus

“…The three salvage TIPS studies identified factors influencing in-hospital mortality in cirrhotic patients with recalcitrant variceal bleeding after failed endoscopic intervention, as well as intermediate and long-term outcomes and predictive risk factors. …”
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A Predictive Model for Geographical Location Using Microbiome Composition and COVID-19 Based Analysis by Aboushanab, Ahmed Adel

“…This data was then used in training and testing machine learning models (Random Forest and L2-logistic Regression Classifiers) for the prediction of the geographical location based on the microbiome data. …”
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Unsupervised Anomaly Detection of Healthcare Providers using Generative Adversarial Network

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Investigation of Environmental Influences on Multiple Sclerosis: A Study for Egypt’s Health by Ali, Noha EzzAldin Alziny

“…Statistical analysis included univariate comparisons, age-adjusted logistic regression, and advanced machine learning algorithms (LASSO, Random Forest, XGBoost) for classification and 6 feature importance. …”
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