The detection method of continuous outliers in complex network data streams based on C-LSTM

Author:

Shu ZhinianORCID,Li Xiaorong

Funder

Horizontal scientific research project of Chaohu University

Chaohu University Quality Engineering Project

Provincial Teaching Demonstration Course Project of Anhui Provincial Colleges and Universities

Publisher

Springer Science and Business Media LLC

Reference26 articles.

1. Abdulghafoor SA, Mohamed LA (2022) A local density-based outlier detection method for high dimension data. Int J Nonlinear Anal Appl 13:1683–1699

2. Asghari P, Rahmani AM, Javadi HHS (2019) Internet of Things applications: a systematic review. Comput Netw 148:241–261

3. Boers N, Goswami B, Rheinwalt A, Bookhagen B, Hoskins B, Kurths J (2019) Complex networks reveal global pattern of extreme-rainfall teleconnections. Nature 566:373–377

4. Brahmam MV, Gopikrishnan S (2023) NODSTAC: novel outlier detection technique based on spatial, temporal and attribute correlations on IoT bigdata. Comput J 67:bxad034

5. Chen Y, Zhang D (2020) Well log generation via ensemble long short-term memory (EnLSTM) network. Geophys Res Lett 47:e2020GL087685

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