Classify and predict web user behaviour using butterfly optimization and recurrent neural network
Multimedia Tools and Applications, ISSN: 1573-7721, Vol: 83, Issue: 25, Page: 66319-66341
2024
- 1Citations
- 1Mentions
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Metrics Details
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Most Recent News
Studies from Department of Computer Sciences and Engineering Provide New Data on Networks (Classify and Predict Web User Behaviour Using Butterfly Optimization and Recurrent Neural Network)
2024 MAR 04 (NewsRx) -- By a News Reporter-Staff News Editor at Network Daily News -- Researchers detail new data in Networks. According to news
Article Description
Classifying user browsing behavior is an essential task to put suitable information on the web. Also, the browsing behavior is stored in the web server logs, which are used for identifying and classifying commonly assessed patterns of web users. This work designs a Butterfly-based Recurrent Neural Scheme (BbRNS) for accurate classification of the web user browsing behavior based on the URLs. It involves preprocessing, feature extraction, and classification. Generally, preprocessing removes the error, and feature extraction is employed to extract the web user browsing URLs. Then, updating the fitness function in the classification layer for accurate prediction of the browsing behavior of web users also enhances the performance of user behavior detection. Consequently, the developed framework is implemented using a Python tool, and the parameters of the current research work are evaluated with prevailing assignments. The designed model gained 98.8 accuracies, 97.5% recall, and 98% precision for predicting and classifying web user behavior. The experimental result shows improved accuracy for classifying web user browsing behavior with less error rate.
Bibliographic Details
Springer Science and Business Media LLC
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