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Chemical Substance Classification using Long Short-Term Memory Recurrent Neural Network

Zhang, J, Liu, J, Luo, Y, Fu, Q, Bi, J, Qiu, S, Cao, Yi and Ding, X (2017) Chemical Substance Classification using Long Short-Term Memory Recurrent Neural Network In: 17th IEEE International Conference on Communication Technology (ICCT 2017), October 27 - 30 2017, Chengdu, China.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
NameEmailORCID
Zhang, JUNSPECIFIEDUNSPECIFIED
Liu, JUNSPECIFIEDUNSPECIFIED
Luo, YUNSPECIFIEDUNSPECIFIED
Fu, QUNSPECIFIEDUNSPECIFIED
Bi, JUNSPECIFIEDUNSPECIFIED
Qiu, SUNSPECIFIEDUNSPECIFIED
Cao, Yiyc0006@surrey.ac.ukUNSPECIFIED
Ding, XUNSPECIFIEDUNSPECIFIED
Date : 30 October 2017
Copyright Disclaimer : © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : recurrent neural networks; chemical substances; long short-term memory; feed forward neural networks
Depositing User : Melanie Hughes
Date Deposited : 12 Oct 2017 10:56
Last Modified : 12 Oct 2017 10:56
URI: http://epubs.surrey.ac.uk/id/eprint/842529

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