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

2018 ◽  
Vol 33 (12) ◽  
pp. 2083-2088 ◽  
Author(s):  
Yanwu Chu ◽  
Tong Chen ◽  
Feng Chen ◽  
Yun Tang ◽  
Shisong Tang ◽  
...  
Keyword(s):  
Random Forest ◽  
Nasopharyngeal Carcinoma ◽  
Extreme Learning Machine ◽  
Serum Sample ◽  
Laser Induced Breakdown Spectroscopy ◽  
Plasma Emission ◽  
Breakdown Spectroscopy ◽  
Random Forest Method ◽  
Laser Induced Breakdown ◽  
Learning Machine

Plasma emission of biochemical elements in a serum sample.

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.

Laser-Induced Breakdown Spectroscopy for Polymer Identification

1998 ◽  
Vol 52 (3) ◽  
pp. 456-461 ◽  
Author(s):  
R. Sattmann ◽  
I. Mönch ◽  
H. Krause ◽  
R. Noll ◽  
S. Couris ◽  
...  
Keyword(s):  
High Density Polyethylene ◽  
Emission Spectra ◽  
Low Density Polyethylene ◽  
Laser Induced Breakdown Spectroscopy ◽  
Spectral Features ◽  
Low Density ◽  
Plasma Emission ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Polymer Type

Laser-induced breakdown spectroscopy has been applied to polymer samples in order to investigate the possibility of using this method for the identification of different materials. The plasma emission spectra of high-density polyethylene (HDPE), low-density polyethylene (LDPE), polyvinyl chloride (PVC), polyethylene terephthalate (PET), and polypropylene (PP) have been studied. Spectral features have been measured—for example, the 725.7 nm chlorine line, the 486.13 mm Hβ line, and the 247.86 nm carbon line—whose evaluation with neural networks permits identification accuracies between 90 and 100%, depending on polymer type.

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