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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 10(1) (2020)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 3137-3155
S&M2329 Research Paper of Special Issue https://doi.org/10.18494/SAM.2020.2771 Published: October 9, 2020 Uniform Experimental Design for Optimizing the Parameters of Multi-input Convolutional Neural Networks [PDF] Cheng-Jian Lin, Chen-Hsien Wu, Chi-Chia Sun, and Cheng-Hsien Lin (Received January 17, 2020; Accepted May 25, 2020) Keywords: image recognition, gender classification, convolutional neural network, uniform experimental design
In this paper, a multi-input convolutional neural network (CNN) based on a uniform experimental design (UED) is proposed for gender classification applications. The proposed multi-input CNN uses multiple CNNs to obtain output results through individual training and concatenation. In addition, to avoid using trial and error for determining the architecture parameters of the multi-input CNN, a UED was used in this study. The experimental results confirmed that the dual-input CNN with a UED achieved accuracies of 99.68 and 99.06% for the CIA and MORPH datasets, respectively. The accuracy of the proposed CNN increased significantly when increasing the number of inputs.
Corresponding author: Cheng-Jian Lin  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Cheng-Jian Lin, Chen-Hsien Wu, Chi-Chia Sun, and Cheng-Hsien Lin, Uniform Experimental Design for Optimizing the Parameters of Multi-input Convolutional Neural Networks, Sens. Mater., Vol. 32, No. 10, 2020, p. 3137-3155. |
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