Chemical substance classification using long short-term memory recurrent neural network

Jinlei Zhang, Junxiu Liu, Yuling Luo, Qiang Fu, Jinjie Bi, Senhui Qiu, Yi Cao, Xuemei Ding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication2017 17th IEEE International Conference on Communication Technology, ICCT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1994-1997
Number of pages4
ISBN (Electronic)9781509039432
DOIs
Publication statusPublished - 17 May 2018
Event17th IEEE International Conference on Communication Technology, ICCT 2017 - Chengdu, China
Duration: 27 Oct 201730 Oct 2017

Publication series

NameInternational Conference on Communication Technology Proceedings, ICCT
Volume2017-October

Conference

Conference17th IEEE International Conference on Communication Technology, ICCT 2017
Country/TerritoryChina
CityChengdu
Period27/10/1730/10/17

Keywords

  • chemical substances
  • feed forward neural networks
  • long short-term memory
  • recurrent neural networks

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