Title: Abnormal behaviours identification method of college students by WOS-IForest under smart campus

Authors: Ronghua Teng; Shuyu Teng; Junpeng Wang

Addresses: Information Center, Jiangxi Institute of Economic Administrators, Nanchang, 330088, China ' Shenzhen Campus, Jinan University, Shenzhen, 518053, China ' College of Flight Technology, Jiangxi Institute of Economic Administrators, Nanchang, 330088, China

Abstract: Based on isolated forests, a weighted optimum sub forest algorithm is therefore constructed and examined in response to the tiny fraction of aberrant data and large variations from normal data. Subsequently, a twin gated recurrent neural network model based on linear discriminant analysis loss function is examined and built using the features of data from college students, ultimately, integrating the two results in a mechanism for recognising aberrant conduct in college students. The research results show that the algorithm proposed in the study has the shortest running time in different dimensional datasets, with an average running time of 124.5 ms and a maximum average accuracy of 98.76%. The average accuracy of the model designed for the study was 98.01%. Finally, the study employed the recognition approach of abnormal behaviour among college students to build a digital image of the students' aberrant activity, with a pretty broad presentation impact.

Keywords: smart campus; WOS-IForest; abnormal behaviour identification; twin network; digital portrait.

DOI: 10.1504/IJCSYSE.2026.151340

International Journal of Computational Systems Engineering, 2026 Vol.10 No.1/2/3/4, pp.78 - 89

Received: 21 Aug 2023
Accepted: 24 Sep 2023
Published online: 26 Jan 2026 *

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