TY - GEN
T1 - Chemical substance classification using long short-term memory recurrent neural network
AU - Zhang, Jinlei
AU - Liu, Junxiu
AU - Luo, Yuling
AU - Fu, Qiang
AU - Bi, Jinjie
AU - Qiu, Senhui
AU - Cao, Yi
AU - Ding, Xuemei
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - 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.
AB - 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.
KW - Chemical substances
KW - Feed forward neural networks
KW - Long short-term memory
KW - Recurrent neural networks
UR - http://www.scopus.com/inward/record.url?scp=85047741969&partnerID=8YFLogxK
U2 - 10.1109/ICCT.2017.8359978
DO - 10.1109/ICCT.2017.8359978
M3 - Conference Proceeding
AN - SCOPUS:85047741969
T3 - International Conference on Communication Technology Proceedings, ICCT
SP - 1994
EP - 1997
BT - 2017 17th IEEE International Conference on Communication Technology, ICCT 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Communication Technology, ICCT 2017
Y2 - 27 October 2017 through 30 October 2017
ER -