Rough Substructures Based on Overlaps of Successor in Quantales Under Serial Fuzzy Relations

Qurashi, Saqib Mazher; Almutairi, Bander; Kanwal, Rani Sumaira; Krstić, Mladen; Yousaf, Muhammad

doi:10.1109/access.2023.3306028

Cited by 5 publications

(1 citation statement)

References 41 publications

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“…Considering that soft relations were employed to more effectively that minimize some types of information was extended by Shabir et al [42]. Additionally, decision-making methods including soft relations were connected to algebraic structures and gave rise to fascinating new study areas ( [43], [44], [45], [46]).…”

Section: Introductionmentioning

confidence: 99%

Rough Fuzzy Substructures of Quantale Module Under Soft Relations and Corresponding Decision-Making Methods

Qurashi,

Almutairi,

Kanwal

et al. 2023

IEEE Access

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This research paper has developed a way of roughness of fuzzy substructures by using soft relations for developing rough fuzzy substructures in Quantale module. Thus, an innovative concept of fuzzy substructures of Quantale module under rough environment by soft relations, is presented. The lower and upper approximations of fuzzy subsets of quantale module are defined by aftersets and foresets. This relationship leads to various characterizations of the rough fuzzy substructures of quantale modules. Besides of more comprehensive results, soft compatible and soft complete relations are required with foresets and aftersets. Soft relations are further being used to determine upper (lower) approximation of fuzzy subsets of quantale module using foreset and afterset. Moreover, several characterizations of rough fuzzy substructures of quantale module are investigated. Furthermore, the algebraic relations of upper (lower) approximations of fuzzy quantale submodule and fuzzy quantale submodule ideals are studied with the help of soft relations under weak quantale module homomorphism. To illustrate that the suggested approach is superior to the given methods, examples are provided. At last, we describe decision-making methods by using rough fuzzy substructures of Quantale module under soft relations to deal with uncertainties in the real-world problems. To demonstrate the validity, applicability, and efficacy of the suggested method, a detailed example of the decision-making process is provided.INDEX TERMS Quantale module and its substructures, rough sets, soft sets and fuzzy sets.

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Section: Introductionmentioning

confidence: 99%

Rough Fuzzy Substructures of Quantale Module Under Soft Relations and Corresponding Decision-Making Methods

Qurashi,

Almutairi,

Kanwal

et al. 2023

IEEE Access

Self Cite

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A Qualitative Approach to Universal Numerical Integrators (UNIs) with Computational Application

Tasinaffo,

Dias,

da Cunha

2024

Hum-Cent Intell Syst

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Universal Numerical Integrators (UNIs) can be defined as the coupling of a universal approximator of functions (e.g., artificial neural network) with some conventional numerical integrator (e.g., Euler or Runge–Kutta). The UNIs are used to model non-linear dynamic systems governed by Ordinary Differential Equations (ODEs). Among the main types of UNIs existing in the literature, we can mention (i) The Euler-Type Universal Numerical Integrator (E-TUNI), (ii) The Runge-Kutta Neural Network (RKNN), and (iii) The Non-linear Auto Regressive Moving Average with Exogenous input or NARMAX model. All of them are equally accurate, regardless of their order. Furthermore, one of the reasons for writing this article is to show the reader that there are many other UNIs besides these. Thus, this article aims to carry out a detailed bibliographic review of this object of study, taking into more significant consideration the qualitative aspects of these UNIs. Computational experiments are also presented in this article to prove the numerical effectiveness of the main types of UNIs in the literature. Therefore, it is expected that this paper will help researchers in the future development of new UNIs.

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Conditional Aggregation Operator Defined by the Power Information Concerning Type-2 Fuzzy Deep Learning Algorithm for Financial Investment Data Decision-Making

Xiong,

Siddique,

Ali

et al. 2024

IEEE Access

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Rough Substructures Based on Overlaps of Successor in Quantales Under Serial Fuzzy Relations

Cited by 5 publications

References 41 publications

Rough Fuzzy Substructures of Quantale Module Under Soft Relations and Corresponding Decision-Making Methods

Rough Fuzzy Substructures of Quantale Module Under Soft Relations and Corresponding Decision-Making Methods

A Qualitative Approach to Universal Numerical Integrators (UNIs) with Computational Application

Conditional Aggregation Operator Defined by the Power Information Concerning Type-2 Fuzzy Deep Learning Algorithm for Financial Investment Data Decision-Making

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