scholarly journals Correction: Discrimination of nasopharyngeal carcinoma serum using laser-induced breakdown spectroscopy combined with an extreme learning machine and random forest method

2020 ◽  
Vol 35 (7) ◽  
pp. 1505-1505
Author(s):  
Yanwu Chu ◽  
Tong Chen ◽  
Feng Chen ◽  
Yun Tang ◽  
Shisong Tang ◽  
...  
Keyword(s):  
Random Forest ◽  
Nasopharyngeal Carcinoma ◽  
Extreme Learning Machine ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Random Forest Method ◽  
Laser Induced Breakdown ◽  
Learning Machine

Correction for ‘Discrimination of nasopharyngeal carcinoma serum using laser-induced breakdown spectroscopy combined with an extreme learning machine and random forest method’ by Yanwu Chu et al., J. Anal. At. Spectrom., 2018, 33, 2083–2088, DOI: 10.1039/C8JA00263K.

A hybrid random forest method fusing wavelet transform and variable importance for the quantitative analysis of K in potassic salt ore using laser-induced breakdown spectroscopy

2020 ◽  
Vol 35 (6) ◽  
pp. 1131-1138 ◽  
Author(s):  
Yu Ding ◽  
Wan Zhang ◽  
Xingqiang Zhao ◽  
Liwen Zhang ◽  
Fei Yan
Keyword(s):  
Wavelet Transform ◽  
Quantitative Analysis ◽  
Random Forest ◽  
Variable Importance ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Random Forest Method ◽  
Laser Induced Breakdown

The aims of this study were to explore the feasibility of using LIBS technology combined with a hybrid RF algorithm for quantitative analysis of K in potash ore.

Determination of coal properties using laser-induced breakdown spectroscopy combined with kernel extreme learning machine and variable selection

2018 ◽  
Vol 33 (12) ◽  
pp. 2089-2097 ◽  
Author(s):  
Chunhua Yan ◽  
Juan Qi ◽  
Jing Liang ◽  
Tianlong Zhang ◽  
Hua Li
Keyword(s):  
Variable Selection ◽  
Extreme Learning Machine ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Kernel Extreme Learning Machine ◽  
Coal Properties ◽  
Learning Machine

Determination of coal properties using LIBS combined with K-ELM and variable selection.

Quantitative analysis of sinters using laser-induced breakdown spectroscopy (LIBS) coupled with kernel-based extreme learning machine (K-ELM)

2018 ◽  
Vol 10 (9) ◽  
pp. 1074-1079 ◽  
Author(s):  
Yu Ding ◽  
Fei Yan ◽  
Guang Yang ◽  
Haixiu Chen ◽  
Zhensheng Song
Keyword(s):  
Quantitative Analysis ◽  
Extreme Learning Machine ◽  
Total Iron ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Learning Machine

This work explores the combination of LIBS technology and K-ELM algorithm for the quantitative analysis of total iron (TFe) content and alkalinity of sinter.

Rapid determination of water COD using laser-induced breakdown spectroscopy coupled with partial least-squares and random forest

2018 ◽  
Vol 10 (40) ◽  
pp. 4879-4885 ◽  
Author(s):  
Song Ye ◽  
Xiao Chen ◽  
Daming Dong ◽  
Jiejun Wang ◽  
Xinqiang Wang ◽  
...  
Keyword(s):  
Water Quality ◽  
Random Forest ◽  
Chemical Analysis ◽  
Rapid Determination ◽  
Oxygen Demand ◽  
Laser Induced Breakdown Spectroscopy ◽  
Water Quality Indicator ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Chemical oxygen demand (COD) is a water quality indicator that is typically measured by lengthy chemical analysis methods in the laboratory, which indicates that obtaining rapid results is difficult.

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

2015 ◽  
Vol 30 (2) ◽  
pp. 453-458 ◽  
Author(s):  
Liwen Sheng ◽  
Tianlong Zhang ◽  
Guanghui Niu ◽  
Kang Wang ◽  
Hongsheng Tang ◽  
...  
Keyword(s):  
Random Forest ◽  
Iron Ore ◽  
Laser Induced Breakdown Spectroscopy ◽  
Iron Ores ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Laser-induced breakdown spectroscopy combined with the random forest (RF) algorithm was proposed for the classification of ten iron ore samples.

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