@inproceedings{0a000941098d48d8bd6f1374e978b5f1,
title = "Chemical substance classification using long short-term memory recurrent neural network",
abstract = "This paper proposed a chemical substance detection method using the Long Short-Term Memory of Recurrent Neural Networks (LSTM-RNN). The chemical substance data was collected using a mass spectrometer which is a time-series data. The classification accuracy using the LSTM-RNN classifier is 96.84%, which is higher than 75.07% of the ordinary feed forward neural networks. The experimental results show that the LSTM-RNN can learn the properties of the chemical substance dataset and achieve a high detection accuracy.",
keywords = "chemical substances, feed forward neural networks, long short-term memory, recurrent neural networks",
author = "Jinlei Zhang and Junxiu Liu and Yuling Luo and Qiang Fu and Jinjie Bi and Senhui Qiu and Yi Cao and Xuemei Ding",
year = "2018",
month = may,
day = "17",
doi = "10.1109/ICCT.2017.8359978",
language = "English",
series = "International Conference on Communication Technology Proceedings, ICCT",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "1994--1997",
booktitle = "2017 17th IEEE International Conference on Communication Technology, ICCT 2017",
address = "United States",
note = "17th IEEE International Conference on Communication Technology, ICCT 2017 ; Conference date: 27-10-2017 Through 30-10-2017",
}