Comparison of Convolutional and Conventional Artificial Neural Networks for Laser-Induced Breakdown Spectroscopy Quantitative Analysis.

Poggialini, Francesco; Campanella, Beatrice; Legnaioli, Stefano; Raneri, Simona; Palleschi, Vincenzo

Poggialini, Francesco; Campanella, Beatrice; Legnaioli, Stefano; Raneri, Simona; Palleschi, Vincenzo.

Poggialini F; Applied and Laser Spectroscopy Lab, ICCOM, Pisa, Italy.
Campanella B; Applied and Laser Spectroscopy Lab, ICCOM, Pisa, Italy.
Legnaioli S; Applied and Laser Spectroscopy Lab, ICCOM, Pisa, Italy.
Raneri S; Applied and Laser Spectroscopy Lab, ICCOM, Pisa, Italy.
Palleschi V; Applied and Laser Spectroscopy Lab, ICCOM, Pisa, Italy.

Appl Spectrosc ; 76(8): 959-966, 2022 Aug.

Article en En | MEDLINE | ID: mdl-35291826

Asunto(s)

Inteligencia Artificial; Redes Neurales de la Computación; Algoritmos; Análisis Espectral

Palabras clave

CNN, classification; Deep learning; LIBS, convolutional neural networks; bronze; laser-induced breakdown spectroscopy; quantitative analysis

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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Redes Neurales de la Computación Idioma: En Año: 2022 Tipo del documento: Article

Texto completo

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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Redes Neurales de la Computación Idioma: En Año: 2022 Tipo del documento: Article