| A combined multivariate approach analyzing geochemical data for knowledge discovery: the Vazante–Paracatu Zinc District, Minas Gerais, Brazil IS Cevik, GR Olivo, JM Ortiz Journal of Geochemical Exploration 221, 106696, 2021 | 11 | 2021 |
| On the use of machine learning for mineral resource classification IS Cevik, O Leuangthong, A Cate, JM Ortiz Mining, Metallurgy & Exploration 38 (5), 2055-2073, 2021 | 6 | 2021 |
| Machine Learning Enhancements for Knowledge Discovery in Mineral Exploration and Improved Mineral Resource Classification SI Cevik Queen's University (Canada), 2020 | 3 | 2020 |
| Machine learning in the mineral resource sector: An overview SI Cevik, JM Ortiz Queen's University, 2020 | 3 | 2020 |
| Proterozoic carbonate-hosted Morro Agudo sulfide Pb-Zn district, Brazil: Mineralogical and geochemical evidence of fluid mixing during the ore stage C Aldis, GR Olivo, JAAC Arruda, IS Cevik Ore Geology Reviews 141, 104592, 2022 | 2 | 2022 |
| Predictive Geometallurgy and Geostatistics Lab-Annual Report 2019 S Avalos, M Bolgkoranou, SI Cevik, W Kracht, W Midkiff, GR Olivo, ... Predictive Geometallurgy and Geostatistics Lab, 2019 | 1 | 2019 |
| Machine Learning in mineral exploration: a tutorial SI Cevik, JM Ortiz Queen's University, 2019 | 1 | 2019 |
| Predictive Geometallurgy and Geostatistics Lab: Annual Report 2020 M Altinpinar, S Avalos, D Casson, J Castro, SI Cevik, F Faraj, M Garrido, ... Queen's University, 2020 | | 2020 |
| Knowledge Discovery from Geochemical Data with Supervised and Unsupervised Methods SI Cevik, GR Olivo, JM Ortiz Queen's University, 2019 | | 2019 |
Julián OrtizCamborne School of Mines, University of ExeterVerified email at exeter.ac.uk
