Track 1: AI and Data-Driven Decision Making

Mtegha, H.D., Zvarivadza, T. and Genc, B., 2022. Review of machine learning-based mineral resource estimation. Journal of the Southern African Institute of Mining and Metallurgy, 122(11), pp.655–662. O'Shea, K. and Nash, R., 2015. An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458. Sims, D.A., 2023. An estimation error. In: Proceedings of the Mineral Resource Estimation Conference 2023, pp.246–249. Melbourne: The Australasian Institute of Mining and Metallurgy. Zhang, G. and Glacken, I., 2023. Best practice in Multiple Indicator Kriging (MIK)—importance of post-processing and comparison with Localised Uniform Conditioning (LUC). In: Proceedings of the Mineral Resource Estimation Conference 2023, pp.76–85. Melbourne: The Australasian Institute of Mining and Metallurgy. Zuo, R. and Xu, Y., 2023. Graph deep learning model for mapping mineral prospectivity. Mathematical Geosciences, 55, pp.1–21.

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