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Classification of Steel Samples Using Machine Learning Methods on Narrow and Information Rich Libs Spectra

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/b978-0-12-818829-3.00005-8openalex-extracted

Instrumentation for LIBS and recent advances

inferredcites →https://doi.org/10.1016/j.sab.2020.105802openalex-extracted

Determination of minor metal elements in steel using laser-induced breakdown spectroscopy combined with machine learning algorithms

inferredcites →https://doi.org/10.1039/c4ja00352gopenalex-extracted

Classification of iron ores by laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF)

inferredcites →https://doi.org/10.1039/c5ay02208hopenalex-extracted

Classification of different types of slag samples by laser-induced breakdown spectroscopy (LIBS) coupled with random forest based on variable importance (VIRF)

inferredcites →https://doi.org/10.1039/c8ja00216aopenalex-extracted

Identification accuracy improvement for steel species using a least squares support vector machine and laser-induced breakdown spectroscopy

inferredcites →https://doi.org/10.1039/c9ay02728aopenalex-extracted

Industrial applications of laser-induced breakdown spectroscopy: a review

inferredcites →https://doi.org/10.1080/05704928.2020.1843175openalex-extracted

A brief review of new data analysis methods of laser-induced breakdown spectroscopy: machine learning

inferredcites →https://doi.org/10.1088/0950-7671/25/3/108openalex-extracted

The review of scientific instruments

inferredcites →https://doi.org/10.1155/2015/471371openalex-extracted

Unbiased Feature Selection in Learning Random Forests for High‐Dimensional Data

inferredcites →https://doi.org/10.3390/app11199274openalex-extracted

Review of Element Analysis of Industrial Materials by In-Line Laser—Induced Breakdown Spectroscopy (LIBS)

inferredcites →https://doi.org/10.4028/www.scientific.net/amm.644-650.4722openalex-extracted

A Comparative Study of Two Data Reduction Methods for Steel Classification Based on LIBS

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.