Jewelry rock discrimination as interpretable data using laser-induced breakdown spectroscopy and a convolutional LSTM deep learning algorithm

Jewelry rock discrimination as interpretable data using laser-induced breakdown spectroscopy and a convolutional LSTM deep learning algorithm

Abstract In this study, the deep learning algorithm of Convolutional Neural Network long short-term memory (CNN–LSTM) is used to classify various jewelry rocks such as agate, turquoise, calcites, and azure from various historical periods and styles related to Shahr-e Sokhteh. Here, the CNN–LSTM arch...

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Bibliographic Details
Main Authors: Pouriya Khalilian, Fatemeh Rezaei, Nazli Darkhal, Parvin Karimi, Ali Safi, Vincenzo Palleschi, Noureddine Melikechi, Seyed Hassan Tavassoli
Format: Article
Language:English
Published: Nature Portfolio 2024-03-01
Series:Scientific Reports
Subjects:
Deep learning
LIBS spectroscopy
Jewelry rock
Convolutional LSTM
Chemometrics
Online Access:https://doi.org/10.1038/s41598-024-55502-x

Internet

https://doi.org/10.1038/s41598-024-55502-x

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