| Predicting seagrass decline due to cumulative stressors MP Adams, EJY Koh, MP Vilas, CJ Collier, VM Lambert, SA Sisson, ... Environmental Modelling & Software 130, 104717, 2020 | 36 | 2020 |
| Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy EJY Koh, E Amini, GJ McLachlan, N Beaton Minerals Engineering 173, 107230, 2021 | 28 | 2021 |
| Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations EJY Koh, E Amini, GJ McLachlan, N Beaton Minerals Engineering 170, 107026, 2021 | 12 | 2021 |
| An Automated Machine Learning (AutoML) Approach to Regression Models in Minerals Processing with Case Studies of Developing Industrial Comminution and Flotation Models EJY Koh, E Amini, S Gaur, M Becerra Maquieira, C Jara Heck, ... Minerals Engineering 189, 107886, 2022 | 5 | 2022 |
| A mineralogy characterisation technique for copper ore in flotation pulp using deep learning machine vision with optical microscopy EJY Koh, E Amini, CA Spier, GJ McLachlan, W Xie, N Beaton Minerals Engineering 205, 108481, 2024 | 1 | 2024 |
| Scenario-based Evaluation of Potential Value Chain Gains Using Integrated Extraction Simulator–Mt Keith Nickel West Case Study F Faramarzi, E Amini, L Bolden, N Beaton, E Koh | 1 | 2022 |
| Improving predictability of minerals processing models by developing a methodology based-on machine learning techniques EJY Koh | | 2023 |
| Development of an optical sorting algorithm to utilise digital images for the rapid discrimination of target minerals from gangue EJY Koh, E Amini, GJ McLachlan, N Beaton Australasian Institute of Mining and Metallurgy (AusIMM) Preconcentration …, 2020 | | 2020 |
| From computer vision to minerals processing: using a convolutional neural network for parameter estimation of first-order froth flotation models EJY Koh, E Amini, GJ McLachlan Proceedings of the XXX IMPC Congress, 2020 | | 2020 |
