Improving Time-Series Classification Accuracy Based on Temporal Feature Representation Learning Using CRU-LSTM Autoencoder-Reference-Cited by-同舟云学术

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Improving Time-Series Classification Accuracy Based on Temporal Feature Representation Learning Using CRU-LSTM Autoencoder

Published:2023-11-01 Issue: Volume:9 Page:175-183
ISSN:
Container-title:2023 IEEE 5th International Conference on Cognitive Machine Intelligence (CogMI)
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Author:

Kim Dohee¹,Sim Sunghyun²,Yoon Bori²,Liu Ling³,Bae Hyerim¹

Affiliation:

1. Pusan National University,Major in Industrial Data Science & Engineering,Dept. Industrial Engineering,Busan,South Korea

2. Dong-Eui University,Dept. Industrial Management & Big Data Engineering,Busan,South Korea

3. College of Computing, Georgia Institute of Technology,Georgia,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10431548/10431532/10431541.pdf?arnumber=10431541

Reference29 articles.

1. Urban fire emergency management based on big data intelligent processing system and Internet of Things

2. Temporal Attention Gate Network With Temporal Decomposition for Improved Prediction Accuracy of Univariate Time-Series Data

3. VGbel: An exploration of ensemble learning incorporating non-Euclidean structural representation for time series classification

4. A review on distance based time series classification

5. Dynamic Time Warping Based Adversarial Framework for Time-Series Domain

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Temporal Multi-Features Representation Learning-Based Clustering for Time-Series Data;IEEE Access;2024

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