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Full Length Article
International Journal of Neutrosophic Science
Volume 18 , Issue 3, PP: 125-134 , 2022 | Cite this article as | XML | Html |PDF

Title

Hybridization of Neutrosophic Logic with Quasi-Oppositional Chimp Optimization based Data Classification Model

Authors Names :   Sundus Naji AL-Aziz   1 *     Reem Atassi   2     Abd Al-Aziz Hosni El-Bagoury   3  

1  Affiliation :  Department of Mathematical Sciences, Faculty of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

    Email :  snalaziz@pnu.edu.sa


2  Affiliation :  Faculty of Computer Information System, Higher Colleges of Technology, UAE

    Email :  ratassi@hct.ac.ae


3  Affiliation :  Higher Institute of Engineering and Technology, El-Mahala El-Kobra, Egypt.

    Email :  azizhel2013@yahoo.com



Doi   :   https://doi.org/10.54216/IJNS.1803011

Received: February3, 2022 Accepted: April 22, 2022

Abstract :

Data classification is the procedure of investigating structured or unstructured data and forming it into distinct classes depending upon file types, size, etc. It assist the organizations to derive important solutions based on the data and helps decision making process. The computational intelligence techniques such as neural computing, fuzzy logic, machine learning, etc. can be used to design effective data classification models. This study offers a Hybridization of Neutrosophic Logic with Quasi-Oppositional Chimp Optimization based Data Classification (HNLQOCO) model. The presented HNLQOCO algorithm aims to integrate the concepts of NL and QOCO algorithm for improved data classification outcomes. Besides, the QOCO algorithm is designed by incorporating the concepts of quasi oppositional based learning (QOBL) with traditional chimp optimization algorithm (COA). Here, the NL is applied to represent various kinds of knowledge and the QOCO algorithm is applied to tune the produced NS rules. The experimental result analysis of the HNLQOCO model is tested using three benchmark medical dataset. The obtained results reported the significant performance of the HNLQOCO model over the other methods.

Keywords :

Data classification , Neutrosophic Logic , Chimp optimization algorithm , Rule generation , QOBL.

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Cite this Article as :
Sundus Naji AL-Aziz , Reem Atassi , Abd Al-Aziz Hosni El-Bagoury, Hybridization of Neutrosophic Logic with Quasi-Oppositional Chimp Optimization based Data Classification Model, International Journal of Neutrosophic Science, Vol. 18 , No. 3 , (2022) : 125-134 (Doi   :  https://doi.org/10.54216/IJNS.1803011)