Class-Driven Graph Attention Network for Multi-Label Time Series Classification in Mobile Health Digital Twins-Reference-Cited by-同舟云学术

Class-Driven Graph Attention Network for Multi-Label Time Series Classification in Mobile Health Digital Twins

Published:2023-10 Issue:10 Volume:41 Page:3267-3278
ISSN:0733-8716
Container-title:IEEE Journal on Selected Areas in Communications
language:
Short-container-title:IEEE J. Select. Areas Commun.

Author:

Sun Le¹^ORCID,Li Chenyang¹,Liu Bo²^ORCID,Zhang Yanchun³^ORCID

Affiliation:

1. Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China

2. School of Economics and Management, Hubei University of Science and Technology, Xianning, China

3. Department of New Networks, Peng Cheng Laboratory, Shenzhen, China

Funder

National Natural Science Foundation of China

Major Key Project of Pengcheng Laboratory

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/49/10297956/10234411.pdf?arnumber=10234411

Reference42 articles.

1. An approach for ECG classification based on wavelet feature extraction and decision tree

2. A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs

3. Active k-labelsets ensemble for multi-label classification

4. Rule-Based methods and Deep Learning Networks for Automatic Classification of ECG

5. AIMM: Artificial Intelligence Merged Methods for flood DDoS attacks detection

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