S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials, Volume 33, Number 4(1) (2021)
Copyright(C) MYU K.K.
pp. 1149-1165
S&M2524 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2021.3002
Published: April 6, 2021

Genetic-algorithm-based Convolutional Neural Network for Robust Time Series Classification with Unreliable Data [PDF]

Jiang Wu, Yanju Ji, and Suyi Li

(Received September 24, 2020; Accepted March 8, 2021)

Keywords: genetic algorithm, convolutional neural network, time series classification, photoplethysmography

Finding robust solutions to time series classification problems using deep neural networks has received wide attention. However, unreliable data makes classification very difficult. Traditional deep neural networks cannot effectively solve problems with strong noise. In this paper, we propose a hybrid convolutional neural network (CNN) model combined with a genetic algorithm (GA) for time series classification (TSC) with unreliable data. To obtain a robust CNN structure, even though network structural optimization is an NP-hard problem, we design a GA for network structure optimization. Several benchmarks and actual datasets are adopted, and tests are carried out to prove the effectiveness of the proposed GA-based CNN. The numerical results show that our approach has better performance than other state-of-the-art deep neural networks.

Corresponding author: Jiang Wu


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Cite this article
Jiang Wu, Yanju Ji, and Suyi Li, Genetic-algorithm-based Convolutional Neural Network for Robust Time Series Classification with Unreliable Data, Sens. Mater., Vol. 33, No. 4, 2021, p. 1149-1165.



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