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...

全面介绍

书目详细资料
Main Authors: Pouriya Khalilian, Fatemeh Rezaei, Nazli Darkhal, Parvin Karimi, Ali Safi, Vincenzo Palleschi, Noureddine Melikechi, Seyed Hassan Tavassoli
格式: 文件
语言:English
出版: Nature Portfolio 2024-03-01
丛编:Scientific Reports
主题:
Deep learning
LIBS spectroscopy
Jewelry rock
Convolutional LSTM
Chemometrics
在线阅读:https://doi.org/10.1038/s41598-024-55502-x

因特网

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

相似书籍