Intelligent Search Optimization using Artificial Fuzzy Logics
Jai Manral

TL;DR
This paper proposes a new web search ranking algorithm that incorporates SEO parameters using artificial fuzzy logics, resulting in improved search result relevance and user satisfaction.
Contribution
It introduces a meta search engine with a novel heuristic page ranking algorithm based on SEO parameters and fuzzy logic, enhancing search accuracy.
Findings
Search results improved with SEO parameter integration
Meta search engine outperforms existing engines in precision
Using SEO parameters increases user satisfaction
Abstract
Information on the web is prodigious; searching relevant information is difficult making web users to rely on search engines for finding relevant information on the web. Search engines index and categorize web pages according to their contents using crawlers and rank them accordingly. For given user query they retrieve millions of webpages and display them to users according to web-page rank. Every search engine has their own algorithms based on certain parameters for ranking web-pages. Search Engine Optimization (SEO) is that technique by which webmasters try to improve ranking of their websites by optimizing it according to search engines ranking parameters. It is the aim of this research to identify the most popular SEO techniques used by search engines for ranking web-pages and to establish their importance for indexing and categorizing web data. The research tries to establish that…
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Taxonomy
TopicsWeb Data Mining and Analysis · Spam and Phishing Detection · Consumer Market Behavior and Pricing
