GA-GWNN: Detecting anomalies of online learners by granular computing and graph wavelet convolutional neural network-Reference-Cited by-同舟云学术

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GA-GWNN: Detecting anomalies of online learners by granular computing and graph wavelet convolutional neural network

Published:2022-02-23 Issue:11 Volume:52 Page:13162-13183
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Han Zhongmei,Huang Qionghao,Zhang Jie,Huang Changqin,Wang Huijin,Huang Xiaodi

Funder

the Science and Technology Project of Guangzhou Municipality

the National Natural Science Foundation of China

the Key Research and Development Program of Zhejiang Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-03337-2.pdf

Reference61 articles.

1. Adedoyin OB, Soykan E (2020) Covid-19 pandemic and online learning: the challenges and opportunities. Interact Learn Environ, 1–13

2. Li J, Xu X, Gao L, Wang Z, Shao J (2020) Cognitive visual anomaly detection with constrained latent representations for industrial inspection robot. Appl Soft Comput 95:106539

3. Otero J, Sánchez L, Junco LA, Couso I (2021) Analysis of students’ online interactions in the covid era from the perspective of anomaly detection. In: Computational intelligence in security for information systems conference, Springer, pp 305–314

4. Zhou Y, Ren H, Li Z, Pedrycz W (2022) Anomaly detection based on a granular markov model. Expert Syst Appl 187:115744

5. Yang X, Li T, Liu D, Fujita H (2020) A multilevel neighborhood sequential decision approach of three-way granular computing. Inf Sci 538:119–141

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