Fast identification of biominerals by means of stand-off laser‐induced breakdown spectroscopy using linear discriminant analysis and artificial neural networks

Modern approaches to the region’s landslide susceptibility assessment are considered in this paper. Have been presented descriptions of the most used techniques for landslide susceptibility assessment: logistic regression, indicator validity, linear discriminant analysis and application of artificial neural networks. These techniques’ advantages and disadvantages are discussed in the paper. The most suitable techniques for various conditions of analysis have been marked. It has been concluded that the most acceptable techniques of analysis for a large number of input data related to the studied region are the method of logistic regression and indicator validity method. With these methods the most accurate results are achieved. When there is a lack of information, it is more expedient to use linear discriminant analysis and artificial neural networks that will minimize potential analysis inaccuracies.

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Geographical classification of Spanish bottled mineral waters by means of iterative models based on linear discriminant analysis and artificial neural networks

Neural Computing and Applications ◽

10.1007/s00521-016-2459-5 ◽

2016 ◽

Vol 29 (2) ◽

pp. 459-468 ◽

Cited By ~ 2

Author(s):

Francisco Gutiérrez-Reguera ◽

J. Marcos Jurado ◽

Rocío Montoya-Mayor ◽

Miguel Ternero-Rodríguez

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Mineral Waters ◽

Linear Discriminant ◽

Artificial Neural

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.4722 ◽

2014 ◽

Vol 644-650 ◽

pp. 4722-4725 ◽

Cited By ~ 2

Author(s):

Hai Yang Kong ◽

Lan Xiang Sun ◽

Jing Tao Hu ◽

Yong Xin ◽

Zhi Bo Cong

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Principal Component ◽

Laser Induced Breakdown Spectroscopy ◽

Breakdown Spectroscopy ◽

Characteristic Lines ◽

Laser Induced Breakdown ◽

Reduction Methods ◽

Artificial Neural ◽

Priori Knowledge

Spectra of 27 steel samples were acquired by Laser-Induced Breakdown Spectroscopy (LIBS) for steel classification. Two methods were used to reduce dimensions: the first is to select characteristic lines of elements contained in the samples manually and the second is to do principal component analysis (PCA) of original spectra. Then the data after reducing dimensions was used as the input of artificial neural networks (ANN) to classify steel samples. The results show that, the better result can be achieved by selecting peak lines manually, but this solution needs much priori knowledge and wastes much time. The principal components (PCs) of original spectra were utilized as the input of artificial neural networks can also attain a good result nevertheless and this method can be developed into an automatic solution without any priori knowledge.

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