Anomaly Detection in Automatic Meter Intelligence System Using Positive Unlabeled Learning and Multiple Symbolic Aggregate Approximation-Reference-Cited by-同舟云学术

Anomaly Detection in Automatic Meter Intelligence System Using Positive Unlabeled Learning and Multiple Symbolic Aggregate Approximation

Published:2023-06-01 Issue:3 Volume:11 Page:225-238
ISSN:2167-6461
Container-title:Big Data
language:en
Short-container-title:Big Data

Author:

Nguyen Thi Ngoc Anh¹²^ORCID,Vu Hoai Thu²³,Dang Minh Tuan²³,Kim Dohyeun⁴,Le Anh Ngoc⁵

Affiliation:

1. Applied Mathematics Department, School of Applied Mathematics and Informatics, Hanoi University of Science and Technology, Hanoi, Vietnam.

2. Big Data Lab, CMC Institute of Science and Technology, Hanoi, Vietnam.

3. Faculty of Information Technology, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.

4. Department of Computer Engineering, Advanced Technology Research Institute, Jeju National University, Jeju, Korea.

5. Swinburne Vietnam, FPT University, Hanoi, Vietnam.

Publisher

Mary Ann Liebert Inc

Subject

Information Systems and Management,Computer Science Applications,Information Systems

Link

https://www.liebertpub.com/doi/pdf/10.1089/big.2021.0471

Reference21 articles.

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3. 4. Tung NX, Anh NTN, Anh NN, Anh NHQ. Feature selection using genetic algorithm and bayesian hyper-parameter optimization for LSTM in short-term load forecasting. In: Tran DT, Jeon G, Nguyen TDL, et al. (Eds.): Intelligent Systems and Networks. Singapore: Springer, 2021.

4. Hybrid online model based multi seasonal decompose for short-term electricity load forecasting using ARIMA and online RNN

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