Fuzzy rough prime and semi-prime ideals in semigroups

Subha, V. S.; Thillaigovindan, N.; Sharmila, S.

doi:10.1063/1.5135268

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…The feature selection method is to choose the optimal features from higher dimensional dataset to limit the computational time [9]. The vast number of attributes existing in real-life data sets and only a section of code is effectively applied to mark the accurate dataset.…”

Section: Fuzzy Rough Set Theory For Feature Selectionmentioning

confidence: 99%

Classification of data with optimized fuzzy rough set feature selection on Nash equilibrium based kernel support vector machine

Maheshwari¹,

Mithra²

2020

Malaya J. Mat.

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In data mining, the reduction of dimension is a highly complicated process, and it should be processed in an efficient way to get an optimal solution. So in this paper, the fuzzy rough set theory is implemented for optimal feature selection in the normalized data of KDD cup 99 datasets. The Nash equilibrium game theory is fed as an input to update the kernel on SVM. The Nash equilibrium gives the rapid update and provides an accurate rate of classification in data reduction. The performance values computed for this method is measured in terms of accuracy, precision, detection rate, F-score, FPR and AUC.

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Section: Fuzzy Rough Set Theory For Feature Selectionmentioning

confidence: 99%

Classification of data with optimized fuzzy rough set feature selection on Nash equilibrium based kernel support vector machine

Maheshwari¹,

Mithra²

2020

Malaya J. Mat.

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“…V.S.Subha et al Stated the fuzzy rough prime and semi-prime ideals in semigroups and discussed their properties. Bagirmaz described the rough image and inverse image of the prime ideals [26][27][28]. Zhan and Marynirmala proposed RFI and hearings [29].…”

Section: Introductionmentioning

confidence: 99%

Characterizations of Γ Rings in Terms of Rough Fuzzy Ideals

Pushpanathan¹,

Ezhilmaran²

2022

Symmetry

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Fuzzy sets are a major simplification and wing of classical sets. The extended concept of set theory is rough set (RS) theory. It is a formalistic theory based upon a foundational study of the logical features of the fundamental system. The RS theory provides a new mathematical method for insufficient understanding. It enables the creation of sets of verdict rules from data in a presentable manner. An RS boundary area can be created using the algebraic operators’ union and intersection, which is known as an approximation. In terms of data uncertainty, fuzzy set theory and RS theory are both applicable. In general, as a uniting theme that unites diverse areas of modern arithmetic, symmetry is immensely important and helpful. The goal of this article is to present the notion of rough fuzzy ideals (RFI) in the gamma ring structure.We introduce the basic concept of RFI, and the theorems are proven for their characteristic function. After that, weexplain the operations on RFI, and related theorems are given. Additionally, we prove some theorems on rough fuzzy prime ideals. Furthermore, using the concept of rough gamma endomorphism, we propose some theorems on the morphism properties of RFI in the gamma ring.

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