Machine Learning For Ore Sorting

Machine Learning for Ore Sorting is a computational methodology that employs machine learning algorithms to automatically identify, classify, and segregate ore from waste based on sensor-derived data, enabling data-driven decision-making in mineral processing.

14

Publications

568

Citations

54

Authors

17

Institutions

Publications per year

2017–2026

11

Authors

54

  1. BK
    Bern Klein

    University of British Columbia, Canada

    2 publications · 81 citations

    2

    h-index

  2. ZZ
    Zelin Zhang

    Wuhan University of Science and Technology, China

    2 publications · 64 citations

    2

    h-index

  3. PR
    Pierre Rognon

    The University of Sydney, Australia

    1 publications · 11 citations

    1

    h-index

  4. VM
    Víctor Murray

    University of New Mexico, United States

    1 publications · 36 citations

    1

    h-index

  5. SL
    Sudip Laudari

    The University of Sydney, Australia

    1 publications · 11 citations

    1

    h-index

1–5 of 54

Venues

  1. Minerals Engineering

    4 publications · 234 citations

    4

    h-index

  2. 2

    h-index

  3. Scientific Reports

    1 publications · 11 citations

    1

    h-index

  4. Journal of Cleaner Production

    1 publications · 11 citations

    1

    h-index

  5. E3S Web of Conferences

    1 publications · 17 citations

    1

    h-index

1–5