Deep learning for supernovae detection by Amar, Gilad

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Levels of thought in geometry of pre-service mathematics educators according to the van Hiele model

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Kinetic characterisation and mathematical modelling of the coenzyme A biosynthesis salvage pathway in Mycobacterium tuberculosis by Mocke, Leanie, Mocke, Leanie

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Learning to Coordinate Efficiently through Multiagent Soft Q-Learning in the presence of Game-Theoretic Pathologies by Danisa, Siphelele

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Partial end-to-end reinforcement learning for robustness towards model-mismatch in autonomous racing’ by Murdoch, Andrew

Subjects: “…Partial end-to-end; reinforcement learning; robustness towards model-mismatch; autonomous racing…”
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Teaching mathematics to oral hearing impaired learners in an inclusive environment

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An integrated framework modelling susceptibility to tuberculosis in homogeneous and admixed populations by Gebremariam, Zoe Zerihun

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Modelling the control of tsetse and African trypanosomiasis through application of insecticides on cattle in Southeastern Uganda by Kajunguri, Damian

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Computational modelling of cardiac function and myocardial infarction by MBewu, James

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Link prediction in knowledge graphs using latent feature modelling and neural tensor factorisation by Magangane, Luyolo

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A framework for intraday ensemble trading on the foreign exchange market by Koegelenberg, Dirk Johan Coetzee

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'Bridging discourses' in a bilingual South African mathematics classroom with a collaborative teaching model by Tyler, Robyn

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The defensive role of ultrasonic moth clicks against bat predation : a mathematical modeling approach by Ribeiro, Daniel De Lemos

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The concurrent development of mathematical modelling and engineering technician competencies of first-year engineering technician students by De Villiers, Lidamari

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Time series forecasting using neural networks without recurrent connections by Masunungure, Takudzwa Trevor

Subjects: “…Prediction theory -- Mathematical models…”
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Biologically motivated reinforcement learning in spiking neural networks by Rance, Dean

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Optimizing COVID-19 control measures using multi-objective deep reinforcement learning by Folarin, Arinze Lawrence

“…The goal of this study is to improve COVID-19 control measures through the use of multi-objective deep re- inforcement learning techniques. The results of two case studies, one using a Pareto conditioned network on COVID-19 data from Belgium and the other using a Deep Q-Network, Goal-DQN, and Non-dominated Sorting Genetic Algorithm (NSGA-II) on COVID-19 data from France, are evaluated using both binomial (Stochastic) and Ordinary Differen- tial Equation mathematical models. …”
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Informing the facilitation of Mathematics in the senior phase using Herrmann’s Whole Brain® theory

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Modelling multi-species co-occurrence patterns and processes by Lagat, Vitalis Kimutai

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