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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 32, Number 7(2) (2020)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 2375-2385
S&M2266 Research Paper of Special Issue https://doi.org/10.18494/SAM.2020.2813 Published: July 20, 2020 Using Artificial Neural Network to Predict a Variety of Pathogenic Microorganisms [PDF] Yu-Hsuan Liao, Yu-Ning Yu, Maysam F. Abbod, Chung-Hung Shih, and Jiann-Shing Shieh (Received December 15, 2019; Accepted April 20, 2020) Keywords: electronic nose, pneumonia, artificial neural network
In this study, an electronic nose is used to record breathing data from healthy and pneumonia patients. The electronic nose records resistance data using a microarray of 11 sensors made of a metal oxide semiconductor. The recorded data are fed to an artificial neural network (ANN), which is used to train a model for the detection of infections. Initially, five patients’ data are used to construct the ANN model. Then, another two patients’ data are used to test the accuracy of the model. In this preliminary study, the ANN achieved good results, showing that it can be further developed into an efficient online pneumonia detection system in the near future.
Corresponding author: Chung-Hung Shih, Jiann-Shing Shieh  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Yu-Hsuan Liao, Yu-Ning Yu, Maysam F. Abbod, Chung-Hung Shih, and Jiann-Shing Shieh, Using Artificial Neural Network to Predict a Variety of Pathogenic Microorganisms, Sens. Mater., Vol. 32, No. 7, 2020, p. 2375-2385. |
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