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Squiride Rank: Squirrel Ride Rank Algorithm-Based Feature Extraction for Re-Ranking of Web Pages

Squiride Rank: Squirrel Ride Rank Algorithm-Based Feature Extraction for Re-Ranking of Web Pages

Lata Jaywant Sankpal, Suhas H. Patil
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 23
ISSN: 1938-0194|EISSN: 1938-0208|EISBN13: 9781683180753|DOI: 10.4018/IJWP.298990
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MLA

Sankpal, Lata Jaywant, and Suhas H. Patil. "Squiride Rank: Squirrel Ride Rank Algorithm-Based Feature Extraction for Re-Ranking of Web Pages." IJWP vol.14, no.1 2022: pp.1-23. http://doi.org/10.4018/IJWP.298990

APA

Sankpal, L. J. & Patil, S. H. (2022). Squiride Rank: Squirrel Ride Rank Algorithm-Based Feature Extraction for Re-Ranking of Web Pages. International Journal of Web Portals (IJWP), 14(1), 1-23. http://doi.org/10.4018/IJWP.298990

Chicago

Sankpal, Lata Jaywant, and Suhas H. Patil. "Squiride Rank: Squirrel Ride Rank Algorithm-Based Feature Extraction for Re-Ranking of Web Pages," International Journal of Web Portals (IJWP) 14, no.1: 1-23. http://doi.org/10.4018/IJWP.298990

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Abstract

The proposed SquiRide Rank algorithm is designed by integrating the Squirrel Search Algorithm (SSA) with the Rider Optimization Algorithm (ROA), respectively. The concept of fictional computing and the foraging behavior realize the re-ranking process more effectively in the web environment. However, the features extracted from the web pages makes the process more effective and achieve global optimal solution through the fitness measure. The proposed SquiRide Rank algorithm effectively captures and analyzes the ranking scores of different search engines in order to generate the re-ranked score result. However, the proposed SquiRide Rank algorithm provides satisfactory results using the metrics, like precision, recall, and F-measure, which acquired with the values of 0.964, 0.996, and 0.980, respectively.

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