An elitism based self-adaptive multi-population Poor and Rich optimization algorithm for grouping similar documents

Thirumoorthy, K.; Muneeswaran, K.

doi:10.1007/s12652-021-02955-x

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…e accuracy and scope of information extraction can be improved using an entity-relationship-based framework [42][43][44][45][46][47]. Few research works employed the term-frequency methodology for ranking the webpages [48][49][50][51][52][53][54]. us, there is a demand for a practical ontological framework for managing documents and retrieving information based on the user query.…”

Section: Literature Reviewmentioning

confidence: 99%

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Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

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In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization’s functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents’ relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users’ queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.

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Section: Literature Reviewmentioning

confidence: 99%

“…us, organizations cannot access the document's content without its metadata [48][49][50][51]. e KSA's Vision 2030 motivates researchers to apply innovative techniques to the current functionalities of the organization.…”

Section: Introductionmentioning

confidence: 99%

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

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A systematic review of the emerging metaheuristic algorithms on solving complex optimization problems

Turgut

Kırtepe

2023

Neural Comput & Applic

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Binary African vultures optimization algorithm for various optimization problems

Song

et al. 2022

Int. J. Mach. Learn. & Cyber.

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An elitism based self-adaptive multi-population Poor and Rich optimization algorithm for grouping similar documents

Cited by 6 publications

References 24 publications

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

A systematic review of the emerging metaheuristic algorithms on solving complex optimization problems

Binary African vultures optimization algorithm for various optimization problems

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