15 citations · 66 across the 32 of their papers we have counts for
34 papers
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…
Machine Learning for Socially Responsible Portfolio Optimisation
Taeisha Nundlall, Terence L Van Zyl
Socially responsible investors build investment portfolios intending to incite social and environmental advancement alongside a financial return. Although Mean-Variance (MV) models…
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…
Improving Cause-of-Death Classification from Verbal Autopsy Reports
Thokozile Manaka, Terence van Zyl, Deepak Kar
In many lower-and-middle income countries including South Africa, data access in health facilities is restricted due to patient privacy and confidentiality policies. Further, since…
Exploring the effectiveness of surrogate-assisted evolutionary algorithms on the batch processing problem
Mohamed Z. Variawa, Terence L. Van Zyl, Matthew Woolway
Real-world optimisation problems typically have objective functions which cannot be expressed analytically. These optimisation problems are evaluated through expensive physical exp…
Multi-Modal Recommendation System with Auxiliary Information
Mufhumudzi Muthivhi, Terence L. van Zyl, Hairong Wang
Context-aware recommendation systems improve upon classical recommender systems by including, in the modelling, a user's behaviour. Research into context-aware recommendation syste…
Pareto Driven Surrogate (ParDen-Sur) Assisted Optimisation of Multi-period Portfolio Backtest Simulations
Terence L. van Zyl, Matthew Woolway, Andrew Paskaramoorthy
Portfolio management is a multi-period multi-objective optimisation problem subject to a wide range of constraints. However, in practice, portfolio management is treated as a singl…
Knowledge Graph Fusion for Language Model Fine-tuning
Nimesh Bhana, Terence L. van Zyl
Language Models such as BERT have grown in popularity due to their ability to be pre-trained and perform robustly on a wide range of Natural Language Processing tasks. Often seen a…
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…
Surrogate Assisted Evolutionary Multi-objective Optimisation applied to a Pressure Swing Adsorption system
Liezl Stander, Matthew Woolway, Terence L. Van Zyl
Chemical plant design and optimisation have proven challenging due to the complexity of these real-world systems. The resulting complexity translates into high computational costs…
Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization
Mufhumudzi Muthivhi, Terence L. van Zyl
The fusion of public sentiment data in the form of text with stock price prediction is a topic of increasing interest within the financial community. However, the research literatu…
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…
Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management
Ruan Pretorius, Terence van Zyl
Traditional portfolio management methods can incorporate specific investor preferences but rely on accurate forecasts of asset returns and covariances. Reinforcement learning (RL)…
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…
AMA-K: Aggressive Multi-Temporal Allocation An Algorithm for Aggressive Online Portfolio Selection
Matthew Kruger, Terence L. van Zyl, Andrew Paskaramoorthy
Online portfolio selection is an integral componentof wealth management. The fundamental undertaking is tomaximise returns while minimising risk given investor con-straints. We aim…
Surrogate Parameters Optimization for Data and Model Fusion of COVID-19 Time-series Data
Timilehin Ogundare, Terence Van Zyl
Our research focuses on developing a computational framework to simulate the transmission dynamics of COVID-19 pandemic. We examine the development of a system named ADRIANA for th…