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Enhanced Neuro-Fuzzy-Based Crop Ontology for Effective Information Retrieval

K. Ezhilarasi1,*, G. Maria Kalavathy2

1 Computer sceince and Engineering, Anna university, Chennai, 600025, India
2 Computer sceince and Engineering, St. Joseph’s College of Engineering, Chennai, 600119, India

* Corresponding Author: K. Ezhilarasi. Email: email

Computer Systems Science and Engineering 2022, 41(2), 569-582. https://doi.org/10.32604/csse.2022.020280

Abstract

Ontology is the progression of interpreting the conceptions of the information domain for an assembly of handlers. Familiarizing ontology as information retrieval (IR) aids in augmenting the searching effects of user-required relevant information. The crux of conventional keyword matching-related IR utilizes advanced algorithms for recovering facts from the Internet, mapping the connection between keywords and information, and categorizing the retrieval outcomes. The prevailing procedures for IR consume considerable time, and they could not recover information proficiently. In this study, through applying a modified neuro-fuzzy algorithm (MNFA), the IR time is mitigated, and the retrieval accuracy is enhanced for trouncing the above-stated downsides. The proposed method encompasses three phases: i) development of a crop ontology, ii) implementation of the IR system, and iii) processing of user query. In the initial phase, a crop ontology is developed and evaluated by gathering crop information. In the next phase, a hash tree is constructed using closed frequent patterns (CFPs), and MNFA is used to train the database. In the last phase, for a specified user query, CFP is calculated, and similarity assessment results are retrieved using the database. The performance of the proposed system is measured and compared with that of existing techniques. Experimental results demonstrate that the proposed MNFA has an accuracy of 92.77% for simple queries and 91.45% for complex queries.

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APA Style
Ezhilarasi, K., Kalavathy, G.M. (2022). Enhanced neuro-fuzzy-based crop ontology for effective information retrieval. Computer Systems Science and Engineering, 41(2), 569-582. https://doi.org/10.32604/csse.2022.020280
Vancouver Style
Ezhilarasi K, Kalavathy GM. Enhanced neuro-fuzzy-based crop ontology for effective information retrieval. Comput Syst Sci Eng. 2022;41(2):569-582 https://doi.org/10.32604/csse.2022.020280
IEEE Style
K. Ezhilarasi and G.M. Kalavathy, "Enhanced Neuro-Fuzzy-Based Crop Ontology for Effective Information Retrieval," Comput. Syst. Sci. Eng., vol. 41, no. 2, pp. 569-582. 2022. https://doi.org/10.32604/csse.2022.020280



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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