Article: Social network user browsing trajectory detection based on soft computing to promote a healthy environment Journal: International Journal of Computational Systems Engineering (IJCSYSE) 2025 Vol.9 No.12 pp.12 - 21 Abstract: In order to improve the detection ability of browsing trajectory data for social network users, a mobile computing based method for detecting browsing trajectories of social network users is proposed. The study first utilises fuzzy logic to establish a social network user browsing trajectory data detection model. Then, the fuzzy parameter recognition method is used to extract the features of the browsing trajectory data of social network users. Finally, a social network user browsing trajectory detection method was designed by combining random forest learning algorithm and matched filtering detection method. The experimental results show that the method has a good output signal-to-noise ratio to eliminate redundancy, with a maximum redundancy elimination of 23.7 dB. The accuracy and stability are high, up to 93%, and it has a good detection effect on the browsing trajectory of social network users. Inderscience Publishers - linking academia, business and industry through research

Title: Social network user browsing trajectory detection based on soft computing to promote a healthy environment

Authors: Qing Cai

Addresses: Department of Literature and Law, North China Institute of Science and Technology, Langfang 065201, China

Abstract: In order to improve the detection ability of browsing trajectory data for social network users, a mobile computing based method for detecting browsing trajectories of social network users is proposed. The study first utilises fuzzy logic to establish a social network user browsing trajectory data detection model. Then, the fuzzy parameter recognition method is used to extract the features of the browsing trajectory data of social network users. Finally, a social network user browsing trajectory detection method was designed by combining random forest learning algorithm and matched filtering detection method. The experimental results show that the method has a good output signal-to-noise ratio to eliminate redundancy, with a maximum redundancy elimination of 23.7 dB. The accuracy and stability are high, up to 93%, and it has a good detection effect on the browsing trajectory of social network users.

Keywords: soft computing; social networks; trajectory similarity; browse track; random forest; environment; social media.

DOI: 10.1504/IJCSYSE.2025.147790

International Journal of Computational Systems Engineering, 2025 Vol.9 No.12, pp.12 - 21

Received: 21 Nov 2023
Accepted: 17 Jan 2024
Published online: 01 Aug 2025 *

Title: Social network user browsing trajectory detection based on soft computing to promote a healthy environment

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