15 citations · 66 across the 32 of their papers we have counts for
19 papers · 1 filter
Longitudinal Learner Participation Profiling in Online Learning: Heterogeneous Consensus Clustering under Unequal Observation Windows
David Otoo-Arthur, Terence L. van Zyl
Learning management system traces are clustered to identify participation profiles, but unequal observation windows may cause profiles to reflect duration rather than participation…
A Time-Synchronous Prequential Evaluation Framework for Next-Week Student Disengagement Prediction from Moodle LMS Logs
David Otoo-Arthur, Terence L. van Zyl
Retrospective evaluation of early-warning models may not reflect information available when predictions are issued and outcomes become observable. We developed a time-synchronous p…
A Learnheuristic Approach to A Constrained Multi-Objective Portfolio Optimisation Problem
Sonia Bullah, Terence L. van Zyl
Multi-objective portfolio optimisation is a critical problem researched across various fields of study as it achieves the objective of maximising the expected return while minimisi…
Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting
Terence L. van Zyl
Meta-learning, decision fusion, hybrid models, and representation learning are topics of investigation with significant traction in time-series forecasting research. Of these two s…
Volatility forecasting using Deep Learning and sentiment analysis
V Ncume, T. L van Zyl, A Paskaramoorthy
Several studies have shown that deep learning models can provide more accurate volatility forecasts than the traditional methods used within this domain. This paper presents a comp…
Towards a methodology for addressing missingness in datasets, with an application to demographic health datasets
Gift Khangamwa, Terence L. van Zyl, Clint J. van Alten
Missing data is a common concern in health datasets, and its impact on good decision-making processes is well documented. Our study's contribution is a methodology for tackling mis…
Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary Features in the Analysis of Deaths from Hyperglycaemia
Thokozile Manaka, Terence Van Zyl, Alisha N Wade +1
Lower-and-middle income countries are faced with challenges arising from a lack of data on cause of death (COD), which can limit decisions on population health and disease manageme…
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion
Pieter Cawood, Terence van Zyl
Techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigat…
Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection
Thabang Mathonsi, Terence L van Zyl
Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at both forecasting tasks, and at quantifying the uncertainty associated with those for…
Investigating Transfer Learning in Graph Neural Networks
Nishai Kooverjee, Steven James, Terence van Zyl
Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces. Transfer learning has proven extremely successful for tradition…
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling
Thabang Mathonsi, Terence L. van Zyl
Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at forecasting tasks and quantifying the associated uncertainty with those forecasts (p…
Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learning
Thabang Mathonsi, Terence L. van Zyl
It has been shown that deep learning models can under certain circumstances outperform traditional statistical methods at forecasting. Furthermore, various techniques have been dev…
Incremental Class Learning using Variational Autoencoders with Similarity Learning
Jiahao Huo, Terence L. van Zyl
Catastrophic forgetting in neural networks during incremental learning remains a challenging problem. Previous research investigated catastrophic forgetting in fully connected netw…
Surrogate Assisted Strategies (The Parameterisation of an Infectious Disease Agent-Based Model)
Rylan Perumal, Terence L van Zyl
Parameter calibration is a significant challenge in agent-based modelling and simulation (ABMS). An agent-based model's (ABM) complexity grows as the number of parameters required…
Feature-weighted Stacking for Nonseasonal Time Series Forecasts: A Case Study of the COVID-19 Epidemic Curves
Pieter Cawood, Terence L. van Zyl
We investigate ensembling techniques in forecasting and examine their potential for use in nonseasonal time-series similar to those in the early days of the COVID-19 pandemic. Deve…
Deep Similarity Learning for Sports Team Ranking
Daniel Yazbek, Jonathan Sandile Sibindi, Terence L. Van Zyl
Sports data is more readily available and consequently, there has been an increase in the amount of sports analysis, predictions and rankings in the literature. Sports are unique i…
Surrogate Assisted Methods for the Parameterisation of Agent-Based Models
Rylan Perumal, Terence L van Zyl
Parameter calibration is a major challenge in agent-based modelling and simulation (ABMS). As the complexity of agent-based models (ABMs) increase, the number of parameters require…
Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling
Rylan Perumal, Terence L van Zyl
Wildfire modelling is an attempt to reproduce fire behaviour. Through active fire analysis, it is possible to reproduce a dynamical process, such as wildfires, with limited duratio…
Inter- and Intra-domain Knowledge Transfer for Related Tasks in Deep Character Recognition
Nishai Kooverjee, Steven James, Terence van Zyl
Pre-training a deep neural network on the ImageNet dataset is a common practice for training deep learning models, and generally yields improved performance and faster training tim…