Towards deep probabilistic graph neural network for natural gas leak detection and localization without labeled anomaly data-Reference-Cited by-同舟云学术

Towards deep probabilistic graph neural network for natural gas leak detection and localization without labeled anomaly data

Published:2023-11 Issue: Volume:231 Page:120542
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Zhang Xinqi,Shi Jihao^ORCID,Huang Xinyan^ORCID,Xiao Fu^ORCID,Yang Ming,Huang Jiawei,Yin Xiaokang^ORCID,Sohail Usmani Asif,Chen Guoming

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference40 articles.

1. Variational Inference: A Review for Statisticians;Blei;Journal of the American Statistical Association,2017

2. Generating realistic cyber data for training and evaluating machine learning classifiers for network intrusion detection systems;Chalé;Expert Systems with Applications,2022

3. Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines;Choi;IEEE Access,2021

4. Deng, A., & Hooi, B. (2021). Graph Neural Network-Based Anomaly Detection in Multivariate Time Series. 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 5A, 4027–4035.

5. MST-GAT: A multimodal spatial–temporal graph attention network for time series anomaly detection;Ding;Information Fusion,2023

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