E-İşte Sürdürülebilir Bağlantılığı İzlemek için Ağ Tabanlı Teorinin Kullanımı
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Date
2020
Authors
Perdahçı, Ziya Nazım
Çavur, Mahmut
Journal Title
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Volume Title
Publisher
Turkish Journal of Electrical Engineering and Computer Sciences
Open Access Color
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Abstract
The Mineral Liberation Analyzer (MLA) can be used to obtain mineral maps from backscattered electron (BSE) images of particles. This paper proposes an alternative methodology that includes random forest classification, a prospective machine learning algorithm, to develop mineral maps from BSE images. The results show that the overall accuracy and kappa statistic of the proposed method are 97% and 0.94, respectively, proving that random forest classification is accurate. The accuracy indicators also suggest that the proposed method may be applied to classify minerals with similar appearances under BSE imaging. Meanwhile, random forest predicts fewer middling particles with binary and ternary composition, but the MLA predicts more middling particles only with ternary composition. These discrepancies may arise because the MLA, unlike random forest, may also measure the elemental compositions of mineral surfaces below the polished section.
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Turkish CoHE Thesis Center URL
Fields of Science
Citation
3
WoS Q
Q4
Scopus Q
Q3
Source
Volume
28
Issue
2
Start Page
1030
End Page
1043