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ORCIDTerence L. van Zyl
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- affiliation: University of Johannesburg, South Africa
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2020 – today
- 2024
[j10]Thokozile Manaka, Terence L. van Zyl, Deepak Kar, Alisha N. Wade:
Multi-step Transfer Learning in Natural Language Processing for the Health Domain. Neural Process. Lett. 56(3): 177 (2024)- 2023
- [c29]Terence L. van Zyl:
Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting. FUSION 2023: 1-8 - [c28]Taeisha Nundlall
, Terence L. van Zyl:
Machine Learning for Socially Responsible Portfolio Optimisation. ISMSI 2023: 1-6 - [c27]Sonia Bullah, Terence L. van Zyl:
A Learnheuristic Approach to A Constrained Multi-Objective Portfolio Optimisation Problem. ISMSI 2023: 58-65 - [i30]Terence L. van Zyl:
Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting. CoRR abs/2303.11000 (2023) - [i29]Sonia Bullah, Terence L. van Zyl:
A Learnheuristic Approach to A Constrained Multi-Objective Portfolio Optimisation Problem. CoRR abs/2304.06675 (2023) - [i28]Taeisha Nundlall, Terence L. van Zyl:
Machine Learning for Socially Responsible Portfolio Optimisation. CoRR abs/2305.12364 (2023) - 2022
- [j9]Mohamed Zayyan Variawa, Terence L. van Zyl, Matthew Woolway:
Transfer Learning and Deep Metric Learning for Automated Galaxy Morphology Representation. IEEE Access 10: 19539-19550 (2022) - [c26]Nicholas Baard, Terence L. van Zyl:
Twin-Delayed Deep Deterministic Policy Gradient Algorithm for Portfolio Selection. CIFEr 2022: 1-8 - [c25]Andrew Paskaramoorthy, Terence L. van Zyl, Tim Gebbie:
An Empirical Comparison of Cross-Validation Procedures for Portfolio Selection. CIFEr 2022: 1-10 - [c24]Mufhumudzi Muthivhi, Terence L. van Zyl:
Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization. FUSION 2022: 1-8 - [c23]Thokozile Manaka, Terence L. van Zyl, Deepak Kar:
Improving Cause-of-Death Classification from Verbal Autopsy Reports. SACAIR 2022: 46-59 - [c22]Mufhumudzi Muthivhi, Terence L. van Zyl, Hairong Wang:
Multi-modal Recommendation System with Auxiliary Information. SACAIR 2022: 108-122 - [c21]Gift Khangamwa, Terence L. van Zyl, Clint J. van Alten:
Towards a Methodology for Addressing Missingness in Datasets, with an Application to Demographic Health Datasets. SACAIR 2022: 169-186 - [i27]Nishai Kooverjee, Steven James, Terence L. van Zyl:
Investigating Transfer Learning in Graph Neural Networks. CoRR abs/2202.00740 (2022) - [i26]Thabang Mathonsi, Terence L. van Zyl:
Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection. CoRR abs/2202.12720 (2022) - [i25]Pieter Cawood, Terence L. van Zyl:
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion. CoRR abs/2203.03279 (2022) - [i24]Mufhumudzi Muthivhi, Terence L. van Zyl:
Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization. CoRR abs/2203.05673 (2022) - [i23]Ruan Pretorius, Terence L. van Zyl:
Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management. CoRR abs/2203.11318 (2022) - [i22]Thokozile Manaka, Terence L. van Zyl, Alisha N. Wade, Deepak Kar:
Using Machine Learning to Fuse Verbal Autopsy Narratives and Binary Features in the Analysis of Deaths from Hyperglycaemia. CoRR abs/2204.12169 (2022) - [i21]Liezl Stander, Matthew Woolway, Terence L. van Zyl:
Surrogate Assisted Evolutionary Multi-objective Optimisation applied to a Pressure Swing Adsorption system. CoRR abs/2204.12585 (2022) - [i20]Nimesh Bhana, Terence L. van Zyl:
Knowledge Graph Fusion for Language Model Fine-tuning. CoRR abs/2206.14574 (2022) - [i19]Terence L. van Zyl, Matthew Woolway, Andrew Paskaramoorthy:
Pareto Driven Surrogate (ParDen-Sur) Assisted Optimisation of Multi-period Portfolio Backtest Simulations. CoRR abs/2209.13528 (2022) - [i18]Mufhumudzi Muthivhi, Terence L. van Zyl, Hairong Wang:
Multi-Modal Recommendation System with Auxiliary Information. CoRR abs/2210.10652 (2022) - [i17]V. Ncume, Terence L. van Zyl, Andrew Paskaramoorthy:
Volatility forecasting using Deep Learning and sentiment analysis. CoRR abs/2210.12464 (2022) - [i16]Mohamed Zayyan Variawa, Terence L. van Zyl, Matthew Woolway:
Exploring the effectiveness of surrogate-assisted evolutionary algorithms on the batch processing problem. CoRR abs/2210.17149 (2022) - [i15]Thokozile Manaka, Terence L. van Zyl, Deepak Kar:
Improving Cause-of-Death Classification from Verbal Autopsy Reports. CoRR abs/2210.17161 (2022) - [i14]Gift Khangamwa, Terence L. van Zyl, Clint J. van Alten:
Towards a methodology for addressing missingness in datasets, with an application to demographic health datasets. CoRR abs/2211.02856 (2022) - 2021
- [j8]Zainoolabadien Karim, Terence L. van Zyl:
Deep/Transfer Learning with Feature Space Ensemble Networks (FeatSpaceEnsNets) and Average Ensemble Networks (AvgEnsNets) for Change Detection Using DInSAR Sentinel-1 and Optical Sentinel-2 Satellite Data Fusion. Remote. Sens. 13(21): 4394 (2021) - [j7]Nkosikhona Dlamini, Terence L. van Zyl:
Comparing Class-Aware and Pairwise Loss Functions for Deep Metric Learning in Wildlife Re-Identification. Sensors 21(18): 6109 (2021) - [j6]Terence L. van Zyl, Turgay Çelik:
Did We Produce More Waste During the COVID-19 Lockdowns? A Remote Sensing Approach to Landfill Change Analysis. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 14: 7349-7358 (2021) - [c20]Siddeeq Laher, Andrew Paskaramoorthy, Terence L. van Zyl:
Deep Learning for Financial Time Series Forecast Fusion and Optimal Portfolio Rebalancing. FUSION 2021: 1-8 - [c19]Timilehin Ogundare, Terence L. van Zyl:
Surrogate Parameters Optimization for Data and Model Fusion of COVID-19 Time-series Data. FUSION 2021: 1-7 - [c18]Andrew Paskaramoorthy, Tim Gebbie, Terence L. van Zyl:
The efficient frontiers of mean-variance portfolio rules under distribution misspecification. FUSION 2021: 1-8 - [c17]Bryce Engelbrecht, Terence L. van Zyl:
Comparing CNN Architectures for Land Cover Classification on Multispectral Images. IGARSS 2021: 5378-5381 - [c16]Terence L. van Zyl:
Full Rotation Hyper-ellipsoid Multivariate Adaptive Bandwidth Kernel Density Estimator. SACAIR 2021: 287-303 - [i13]Daniel Yazbek, Jonathan Sandile Sibindi, Terence L. van Zyl:
Deep Similarity Learning for Sports Team Ranking. CoRR abs/2103.13736 (2021) - [i12]Terence L. van Zyl, Matthew Woolway, Andrew Paskaramoorthy:
ParDen: Surrogate Assisted Hyper-Parameter Optimisation for Portfolio Selection. CoRR abs/2107.02121 (2021) - [i11]Pieter Cawood, Terence L. van Zyl:
Feature-weighted Stacking for Nonseasonal Time Series Forecasts: A Case Study of the COVID-19 Epidemic Curves. CoRR abs/2108.08723 (2021) - [i10]Rylan Perumal, Terence L. van Zyl:
Surrogate Assisted Strategies (The Parameterisation of an Infectious Disease Agent-Based Model). CoRR abs/2108.08809 (2021) - [i9]Timilehin Ogundare, Terence L. van Zyl:
Surrogate Parameters Optimization for Data and Model Fusion of COVID-19 Time-series Data. CoRR abs/2109.04207 (2021) - [i8]Matthew Kruger, Terence L. van Zyl, Andrew Paskaramoorthy:
AMA-K: Aggressive Multi-Temporal Allocation An Algorithm for Aggressive Online Portfolio Selection. CoRR abs/2109.13508 (2021) - [i7]Jiahao Huo, Terence L. van Zyl:
Incremental Class Learning using Variational Autoencoders with Similarity Learning. CoRR abs/2110.01303 (2021) - [i6]Thabang Mathonsi, Terence L. van Zyl:
Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learning. CoRR abs/2110.03393 (2021) - [i5]Thabang Mathonsi, Terence L. van Zyl:
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling. CoRR abs/2112.08618 (2021) - 2020
- [c15]J. Atherfold, Terence L. van Zyl:
A Method for Dissolved Gas Forecasting in Power Transformers Using LS-SVM. FUSION 2020: 1-8 - [c14]Jiahao Huo, Terence L. van Zyl:
Unique Faces Recognition in Videos. FUSION 2020: 1-7 - [c13]Rowan Lange, Tony Lange, Terence L. van Zyl:
Predicting Particle Fineness in a Cement Mill. FUSION 2020: 1-8 - [c12]Habeebullah Manack, Terence L. van Zyl:
Deep Similarity Learning for Soccer Team Ranking. FUSION 2020: 1-7 - [c11]Liezl Stander, Matthew Woolway, Terence L. van Zyl:
Data-Driven Evolutionary Optimisation for the design parameters of a Chemical Process: A Case Study. FUSION 2020: 1-8 - [c10]Mohamed Zayyan Variawa, Terence L. van Zyl, Matthew Woolway:
A rules-based and Transfer Learning approach for deriving the Hubble type of a galaxy from the Galaxy Zoo data. FUSION 2020: 1-7 - [c9]Terence L. van Zyl, Matthew Woolway, Bryce Engelbrecht:
Unique Animal Identification using Deep Transfer Learning For Data Fusion in Siamese Networks. FUSION 2020: 1-6 - [c8]Shivaar Sooklal, Terence L. van Zyl, Andrew Paskaramoorthy:
DRICORN-K: A Dynamic RIsk CORrelation-driven Non-parametric Algorithm for Online Portfolio Selection. SACAIR 2020: 183-196 - [i4]Nishai Kooverjee, Steven James, Terence L. van Zyl:
Inter- and Intra-domain Knowledge Transfer for Related Tasks in Deep Character Recognition. CoRR abs/2001.00448 (2020) - [i3]Rylan Perumal, Terence L. van Zyl:
Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling. CoRR abs/2005.13040 (2020) - [i2]Jiahao Huo, Terence L. van Zyl:
Unique Faces Recognition in Videos. CoRR abs/2006.05713 (2020) - [i1]Rylan Perumal, Terence L. van Zyl:
Surrogate Assisted Methods for the Parameterisation of Agent-Based Models. CoRR abs/2008.11835 (2020)
2010 – 2019
- 2018
- [j5]Bolelang Sibolla, Serena Coetzee, Terence L. van Zyl:
A Framework for Visual Analytics of Spatio-Temporal Sensor Observations from Data Streams. ISPRS Int. J. Geo Inf. 7(12): 475 (2018) - 2013
- [c7]Terence L. van Zyl, Graeme McFerren:
Applying Sensor Web strategies to Big Data earth observations. IGARSS 2013: 798-799 - 2012
- [j4]Terence L. van Zyl, Anwar Vahed, Graeme McFerren, Derek Hohls:
Earth Observation Scientific Workflows in a Distributed Computing Environment. Trans. GIS 16(2): 233-248 (2012) - 2010
- [j3]Terence L. van Zyl, Elizabeth Marie Ehlers:
Signal-regulated systems and networks. Complex. 15(6): 50-63 (2010) - [j2]Liping Di, Karen Moe, Terence L. van Zyl:
Earth Observation Sensor Web: An Overview. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 3(4-1): 415-417 (2010)
2000 – 2009
- 2009
- [j1]Terence L. van Zyl, Ingo Simonis, Graeme McFerren:
The Sensor Web: systems of sensor systems. Int. J. Digit. Earth 2(1): 16-30 (2009) - [c6]Terence L. van Zyl, Anwar Vahed, Graeme McFerren, Petrus Shabangu, Bheki Cwele:
Using SensorML to Describe Scientific Workflows in Distributed Web Service Environments. IGARSS (5) 2009: 375-377 - [c5]Terence L. van Zyl, Elizabeth Marie Ehlers:
Self-organising Sensor Web using Cell-Fate Optimisation. IGARSS (5) 2009: 461-464 - 2008
- [c4]Terence L. van Zyl:
GEOSS From Orbit, A Sensor Web Approach. IGARSS (1) 2008: 134-137 - [c3]Graeme McFerren, Terence L. van Zyl, Marna van der Merwe, Martella du Preez:
User Requirements for Sensor Web based Scientific Workflows in the Cholera Research Domain. IGARSS (5) 2008: 136-139 - [c2]Wabo Majavu, Terence L. van Zyl, Tshilidzi Marwala:
Classification of web resident sensor resources using Latent Semantic Indexing and ontologies. SMC 2008: 518-523 - 2007
- [c1]Terence L. van Zyl, Elizabeth Marie Ehlers:
A Need for Biologically Inspired Architectural Description: The Agent Ontogenesis Case. PRIMA 2007: 146-157
Coauthor Index
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last updated on 2024-10-07 22:07 CEST by the dblp team
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