Bees Swarm Optimization Based Approach for Web Information Retrieval

Drias, Habiba; Mosteghanemi, Hadia

doi:10.1109/wi-iat.2010.179

Cited by 30 publications

(14 citation statements)

References 8 publications

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“…Where is term frequency and is the inverse document frequency and common it formulated a log ( ⁄ ), where N is the size of the document collection and is the document frequency [12]. The factor is normalized by the maximum in the query vector [11]. The vector space model refer to the inner product between these two vectors is computed to find the similarity between a query and a document use equation-3 [11]: ∑ …(3) the cosine similarity is a value reflecting the similarity between two documents xi and yj by usingequation-4 [11]:…”

Section: Methodsmentioning

confidence: 99%

Search Result Enhancement For Arabic Datasets Using Modified Chicken Swarm

Hassan

AbdulAmeer

2018

ijs

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The need for information web-searching is needed by many users nowadays. They use the search engines to input their query or question and wait for the answer or best search results. As results to user query the search engines many times may be return irrelevant pages or not related to information need. This paper presents a proposed model to provide the user with efficient and effective result through search engine, based on modified chicken swarm algorithm and cosine similarity to eliminate and delete irrelevant pages(outliers) from the ranked list results, and to improve the results of the user's query . The proposed model is applied to Arabic dataset and use the ZAD corpus dataset for 27300 document. The experimental result shows that the proposed model improves the precision, recall, and accuracy. Thus the result produced by this method improves accuracy.

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Section: Methodsmentioning

confidence: 99%

Search Result Enhancement For Arabic Datasets Using Modified Chicken Swarm

Hassan

AbdulAmeer

2018

ijs

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“…The comparison with classic approach in terms of response time and document quality showed explicitly less efficiency of the classic approach. However this work is inspired by [5] where the adaptation of heuristic search techniques to large scale IR and their comparison with classical approaches can be seen.…”

Section: Related Workmentioning

confidence: 99%

A Novel PSO Methodology for Web Documents Retrieval

Ramya¹

2019

International Journal of Computational Linguistics Research

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This paper focuses on retrieval of web documents with improved response time and similarity using particle swarm optimization (PSO) technique. Since the nature of the web data is distributed, volatile and uncertain, an accurate and speedy access is required. Hence a novel approach on evolutionary bio-inspired Swarm Intelligence techniques to optimize search process in Web Information Retrieval systems is proposed and developed. Here, we propose a novel algorithm using basic PSO technique which works on both small CACM and huge RCV1 collections. We apply this on the pre-processed documents to retrieve most similar documents with a very less response time. This paper also reveals a comparative study with the existing method.

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“…However, the inverted index file may become untreatable for an environment with huge documents. Metaheuristic approaches can get a response time with a polynomial rate on a higher computation scale [6][7].…”

Section: Information Retrievalmentioning

confidence: 99%

“…In this work, we used one of the swarm intelligence approaches called Artificial Bee Colony (ABC), but with making significant modifications in order to suit the mechanism of the query expansion. Swarm intelligence approaches, in general, are reported to get a response time with a polynomial rate on a higher computation scale [6][7]. The essential contribution of this work lies in improving IR performance in the term of effectiveness through improving the automatic word sense disambiguation and query expansion, and in the term of efficiency through using a stochastic search rather than the traditional complete search approach.…”

Section: Introductionmentioning

confidence: 99%