E-İşte Sürdürülebilir Bağlantılığı İzlemek için Ağ Tabanlı Teorinin Kullanımı

Loading...
Thumbnail Image

Date

2020

Authors

Perdahçı, Ziya Nazım
Çavur, Mahmut

Journal Title

Journal ISSN

Volume Title

Publisher

Turkish Journal of Electrical Engineering and Computer Sciences

Open Access Color

OpenAIRE Downloads

OpenAIRE Views

Research Projects

Organizational Units

Journal Issue

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.

Description

Keywords

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