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Issue title: Fuzzy model for human autonomous computing in extreme surveillance and it’s applications
Guest editors: Varatharajan Ramachandran
Article type: Research Article
Affiliations: School of Foreign Languages, Xinyu University, Xinyu, Jiangxi, China
Correspondence: [*] Corresponding author. Zhou Fen, School of Foreign Languages, Xinyu University, Xinyu, Jiangxi, China. E-mail: [email protected].
Abstract: In the era of artificial intelligence, the traditional English teaching model can no longer meet the needs of society, and online English teaching has become the main development direction of English teaching in the future. In order to study the efficiency of English online teaching system, based on machine learning algorithms, this paper constructs an efficiency improvement model of English online teaching system. Moreover, in view of the shortcomings of current situation estimation algorithms that cannot coexist in terms of flexibility, causal interpretability and complexity, this paper proposes a biological immune algorithm framework that uses GBDT algorithm coding, which objectively and accurately shows the spread of the situation. In addition, for the problem that redundant information between features will reduce the accuracy of the framework, this paper proposes a streaming feature selection algorithm based on bagging learning. Finally, this paper designs a control experiment to analyze the performance of the model. The research results show that the model constructed in this paper is highly reliable.
Keywords: Bagging learning, flow feature, feature selection, English online teaching, machine learning
DOI: 10.3233/JIFS-189504
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6695-6705, 2021
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