Artificial Bee Colony (ABC) Approach for Ranking Web Pages

Anuradha, G.; Devi, G. Lavanya

doi:10.5120/17340-7482

Cited by 5 publications

(6 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial Bee Colony approach for ranking web pages [5] In This Paper Artificial Bee Colony (ABC) approach is used as a new method for web mining particularly in ranking web pages.…”

Section: Comparison Resultsmentioning

confidence: 99%

“…Artificial Bee Colony approach for ranking web pages [5] Claims to be more efficient compared with tradition page Rank Algorithm Authenticity should be checked with other types of real time database…”

Section: Comparison Resultsmentioning

confidence: 99%

“…The famous family of Ant Colony Optimization (ACO) algorithms is inspired by the Ants swarm intelligence and their use of stigmergic processes. [5] In this Paper [5], here the model shows collective intelligence of honeybee. It consists of three essential components: food sources, employed foragers, and unemployed foragers.…”

Section: Literature Surveymentioning

confidence: 99%

“…Information retrieval then spread in web information access. The information retrieval provides solution in finding relevant information in unstructured information [5]. …”

mentioning

confidence: 99%

See 3 more Smart Citations

Comparing Performance of Various Optimization Algorithms for Effective Information Retrieval – A Review

Shaikh¹

2017

IJRASET

View full text Add to dashboard Cite

Keywords: Information Retrieval; optimization; Accelerated particle swarm intelligence algorithm; Bat algorithm; Query Expansion I. OBJECTIVE The objective of this paper is to compare various optimization algorithms in information retrieval domain. The researchers have used various optimization algorithms to solve the problems for information retrieval. Here we have studied various information retrieval optimization algorithms. Different algorithms have been implemented on different sets of databases like MEDLINE database, Web database etc. We are comparing these algorithms based on effectiveness. II. INTRODUCTION A. Information RetrievalDuring the last decade the information over the web have increased and optimization of information retrieval effectiveness has driven the quality of the results over the web, People are more trusting and preferring web search as a source of information. Information retrieval has come out of academic discipline to become the basis of most preferred and reliable source of information. The field of information retrieval began with scientific library records and scientific publications; it spread rapidly in other domains like journalism, lawyers and medical fields. Information retrieval then spread in web information access. The information retrieval provides solution in finding relevant information in unstructured information [5]. B. Optimization (Swarm Intelligence)The researchers have used number of optimization technique in information retrieval domain. There are various models of IR and methods for optimization. Here we are concentrating on one of stochastic optimization technique called swarm intelligence. Swarm intelligence is the study of computational systems inspired by the 'collective intelligence'. Collective Intelligence emerges through the working together of large numbers of similar agents in the environment. Schools of fish, flocks of birds, and colonies of ants are some the examples. The property of swarm intelligence is self-organization, decentralization and distribution throughout the environment. The problems are solved in nature like foraging for food, prey evading, and colony relocation through SI. The information is stored and transferred by the means of agents such as proximity in fish and birds, pheromones in ants and dancing in bees [1] [6].

show abstract

“…Artificial Bee Colony approach for ranking web pages [5] In This Paper Artificial Bee Colony (ABC) approach is used as a new method for web mining particularly in ranking web pages.…”

Section: Comparison Resultsmentioning

confidence: 99%

Section: Comparison Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

“…Information retrieval then spread in web information access. The information retrieval provides solution in finding relevant information in unstructured information [5]. …”

mentioning

confidence: 99%

See 2 more Smart Citations

Comparing Performance of Various Optimization Algorithms for Effective Information Retrieval – A Review

Shaikh¹

2017

IJRASET

View full text Add to dashboard Cite

show abstract

“…The authors of [47] proposed a hybrid search technique based on the genetic algorithms and ant colony optimization where user profiling was used for search optimization. In [48], the authors used the ABC algorithm to rank webpages. The study considered user interest, total website linkage, and growth analysis rate to assign a rank to the webpages.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Arabic Document Retrieval Using Optimized Query Paraphrasing

Aldayel

Ykhlef

2015

Arab J Sci Eng

View full text Add to dashboard Cite

Query paraphrasing aims to construct a better formulation of user queries in order to enhance retrieval. Formulating search queries remains complicated for a subset of Web users. In a typical situation, a user will not receive satisfactory results from the submitted search query and will subsequently attempt different query paraphrases. The Arabic vocabulary is rich in synonyms and hyponyms. Such richness of synonyms makes automation of the paraphrasing technique crucial for Arabic information retrieval systems in order to facilitate the process of paraphrasing synonyms. In this article, we propose an enhancement for Arabic information retrieval using a query paraphrasing technique. Furthermore, two query paraphrasing optimization techniques are proposed to overcome the time complexity and exhaustive calculation of existing query paraphrasing techniques. One of these techniques uses a genetic algorithm (GA-QP), and the other employs the artificial bee colony algorithm (ABC-QP). The performance of these two algorithms is compared. ABC-QP shows an improvement in Arabic information retrieval performance compared with the genetic algorithm query paraphrasing system. B Abeer Al-Dayel

show abstract

A new PSO methodology for web documents retrieval

Ramya

Shreedhara

2017

2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT)

View full text Add to dashboard Cite

Artificial Bee Colony (ABC) Approach for Ranking Web Pages

Cited by 5 publications

References 2 publications

Comparing Performance of Various Optimization Algorithms for Effective Information Retrieval – A Review

Comparing Performance of Various Optimization Algorithms for Effective Information Retrieval – A Review

Enhanced Arabic Document Retrieval Using Optimized Query Paraphrasing

A new PSO methodology for web documents retrieval

Contact Info

Product

Resources

About