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

J Zhang; J Liu; Y Luo; Q Fu; J Bi; S Qiu; Yi Cao; X Ding

Back

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

Conference presentation

Open access

Peer reviewed

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

J Zhang, J Liu, Y Luo, Q Fu, J Bi, S Qiu, Yi Cao and X Ding

IEEE Conference Proceedings ICCT 2017

IEEE

17th IEEE International Conference on Communication Technology (ICCT 2017) (Chengdu, China)

30/10/2017

Abstract

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

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.

Files and links (2)

pdf

2017-ICCT-Chemical Substance Classification using Long Short-Term Memory Recurrent395.77 kBDownload View

Text Open Access

url

http://www.ieee-icct.org/View

Published (Version of record)

Metrics

703 File views/ downloads

47 Record Views

Details

Title: Chemical Substance Classification using Long Short-Term Memory Recurrent Neural Network
Creators: J Zhang
J Liu
Y Luo
Q Fu
J Bi
S Qiu
Yi Cao
X Ding
Publication Details: IEEE Conference Proceedings ICCT 2017
Conference: 17th IEEE International Conference on Communication Technology (ICCT 2017) (Chengdu, China)
Publisher: IEEE
Date published: 30/10/2017
Date submitted: 12/10/2017
Identifiers: 99516347302346
Copyright: © 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.
Academic Unit: Surrey Business School
Resource Type: Conference presentation

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

Abstract

Files and links (2)

Metrics

Details

Usage Policy