Deep Web Content Mining for Personalized Web Search: An Application of Optimized Fuzzy Ensemble of CNN Models
Deep Web Content Mining for Personalized Web Search: An Application of Optimized Fuzzy Ensemble of CNN Models |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-10 |
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| Year of Publication : 2025 | ||
| Author : Suruchi Chawla | ||
| DOI : 10.14445/22315381/IJETT-V73I10P118 | ||
How to Cite?
Suruchi Chawla,"Deep Web Content Mining for Personalized Web Search: An Application of Optimized Fuzzy Ensemble of CNN Models", International Journal of Engineering Trends and Technology, vol. 73, no. 10, pp.227-238, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I10P118
Abstract
A novel design is proposed for Personalized web search based on web document classification using a Fuzzy ensemble of CNN models with Genetic Algorithm(GA) based hyperparameter optimization. The hyperparameter optimization of CNN is done using GA to improve the classification accuracy. The proposed ensemble model combines the advantages of ensemble and a Fuzzy weighted combination of deep learning models that classifies web documents into five classes, mainly politics, sports, technology, entertainment, and business. The performance of the fuzzy ensemble of CNN classifiers with GA based hyperparameter optimization was compared with other state-of-the-art models. The use of the proposed approach displays a significant improvement in the web document classification accuracy. The web documents classified based on the proposed method are grouped together in clusters for personalized web search, and the average precision of the search results is improved significantly.
Keywords
Ensemble Deep Learning, Genetic Algorithm(GA), Hyperparameters Optimization, Web document recommendation, classification, Convolution Neural Network (CNN), Personalized Web Search (PWS).
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