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Integrated ore classification using stand-alone and hybridised machine learning algorithms.

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Bibliographic Details
Title: Integrated ore classification using stand-alone and hybridised machine learning algorithms.
Authors: Gholami Vijouyeh A; Earth Sciences Department, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran., Kadkhodaie A; Earth Sciences Department, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. kadkhodaie_ali@tabrizu.ac.ir., Siahcheshm K; Earth Sciences Department, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran., Asadi A; Earth Sciences Department, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran.; Department of Gemmology, Institute of Medical Geology and Environment Research, University of Tabriz, Tabriz, Iran., Hosseinzadeh M; Earth Sciences Department, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran.; Department of Gemmology, Institute of Medical Geology and Environment Research, University of Tabriz, Tabriz, Iran.
Source: Scientific reports [Sci Rep] 2026 Mar 23; Vol. 16 (1). Date of Electronic Publication: 2026 Mar 23.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE; PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2045-2322
DOI:10.1038/s41598-026-42248-x