Quantitative analysis of LIBS spectra using hybrid chemometric models through fusion of extreme learning machines and support vector regression-Reference-Cited by-同舟云学术

Quantitative analysis of LIBS spectra using hybrid chemometric models through fusion of extreme learning machines and support vector regression

Published:2018-12-24 Issue:6 Volume:35 Page:6277-6286
ISSN:1064-1246
Container-title:Journal of Intelligent & Fuzzy Systems
language:
Short-container-title:IFS

Author:

Owolabi Taoreed O.¹,Gondal Mohammed A.¹

Affiliation:

1. Department of Physics, Laser Research Group, Center of Excellence in Nanotechnology King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference69 articles.

1. A novel approach for quantitative analysis of multi-elements in steels based on laser-induced breakdown spectroscopy (LIBS) and random forest regression (RFR);Zhang;J Anal At Spectrom,2014

2. Detection of rare earth elements in Powder River Basin sub-bituminous coal ash using laser-induced breakdown spectroscopy (LIBS);Phuoc;Fuel,2016

3. Direct spectral analysis of tea samples using 266nm UV pulsed laser-induced breakdown spectroscopy and cross validation of LIBS results with ICP-MS;Gondal;Talanta,2016

4. Laser-Induced Breakdown Spectroscopy (LIBS) applied to terrestrial and extraterrestrial analogue geomaterials with emphasis to minerals and rocks;Senesi;Earth-Science Rev,2014

5. On-stream and quantitative mineral identification of tailing slurries using LIBS technique;Khajehzadeh;Miner Eng,2016

Cited by 21 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Modeling thermoelectric figure of merit of BiCuSeO oxyselenide ceramic using extreme learning machine computational method;Solid State Sciences;2024-04

2. Modeling Temperature-Dependent Thermoelectric Performance of Magnesium-Based Compounds for Energy Conversion Efficiency Enhancement Using Intelligent Computational Methods;Inorganics;2024-03-09

3. Exploration and quantification of magnetocaloric effect in EuTiO3 perovskite using extreme learning machine intelligent computational method;Materials Today Communications;2023-12

4. Extreme learning machine computational method of modeling energy gap of doped zinc selenide nano-material semiconductor;Materials Today Communications;2022-06

5. Extreme Learning Machine Approach to Modeling the Superconducting Critical Temperature of Doped MgB2 Superconductor;Crystals;2022-02-07