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
Parents
14
Deep Learning Based Mineral Image Classification Combined With Visual Attention Mechanism
Yang Liu, Zelin Zhang, Xiang Liu et al. · IEEE Access · 2021 · 49 citations · Full text
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17
Wuhan University of Science and Technology
Wuhan, China
2 h-index
10 publications
320 citations

University of British Columbia
Vancouver, Canada
2 h-index
5 publications
225 citations
Tata Steel (India)
Mumbai, India
1 h-index
2 publications
234 citations

Helmholtz-Zentrum Dresden-Rossendorf
Dresden, Germany
1 h-index
6 publications
282 citations
East China University of Technology
Nanchang, China
1 h-index
7 publications
154 citations
Helmholtz Institute Freiberg for Resource Technology
Freiberg, Germany
1 h-index
6 publications
282 citations
Universidad de Ingeniería y Tecnología
Lima, Peru
1 h-index
1 publications
36 citations
China University of Mining and Technology
Xuzhou, China
1 h-index
6 publications
66 citations
1–8 of 17
4 publications · 234 citations
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2 publications · 69 citations
2
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1 publications · 11 citations
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1 publications · 11 citations
1
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1 publications · 17 citations
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