Complex fuzzy soft matrices with applications

Khan, Madad; Anis, Saima; Shi, Song; Jun, Young Bae

doi:10.15672/hujms.588700

Cited by 14 publications

(4 citation statements)

References 8 publications

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“…Selvachandran et al [29] applied the interval-valued complex fuzzy relations in economics problem. Khan et al [17] de…ned the notion of complex fuzzy soft matrices and applied it to a decision-making problem in signal processing.…”

Section: Introductionmentioning

confidence: 99%

Riemannian Complex Fuzzy Sets with Applications in Signals

Zeeshan

Khan

2022

Preprint

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The objective of this paper is to introduce the innovative concept of Riemannian complex fuzzy sets (RCFSs). We discussed some new operations and laws of a RCFS such as Riemannian complex fuzzy union, intersection, complement, disjunctive sum, simple difference, bounded difference, simple product, Cartesian product, probabilistic sum, bold sum, convex linear sum, relations, and distance functions. We studied the basic results and particular examples of these operations. We used RCFSs in signals and systems because its behavior is similar to a Fourier transform in certain cases. Moreover, we develop a new algorithm using RCFSs for applications in signals and systems by which we identify a reference signal out of large input signals detected by a system. We restricted the amplitude of the inverse discrete Fourier transform for incoming signals and a reference signal. Thus, a method for measuring the resembling values of two signals is provided by which we can identify the reference signal.

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Section: Introductionmentioning

confidence: 99%

Riemannian Complex Fuzzy Sets with Applications in Signals

Zeeshan

Khan

2022

Preprint

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“…They developed a decision-making method based on complex fuzzy relations. Khan et al [25] de…ned the notion of complex fuzzy soft matrices and applied it to a decision-making problem in signal processing. Selvachandran et al [42] applied the interval-valued complex fuzzy relations in economics problem.…”

Section: Introductionmentioning

confidence: 99%

Denoising Data in Signal Processing under the Complex Fuzzy Environment

Khan

Rehman

Anis

et al. 2022

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In this paper, we have established some new operations and results of set theory for complex fuzzy sets (CFSs). Moreover, we introduced the basic results of complex fuzzy sets with their examples such as complex fuzzy union, complex fuzzy intersection, complex fuzzy complement, complex fuzzy Cartesian product, complex fuzzy bounded sum, complex fuzzy bounded difference, complex fuzzy algebraic sum, and complex fuzzy algebraic product. We established a new algorithm for the application of a complex fuzzy set in denoising data. We employed CFSs in data denoising using Fast Fourier transform since the phase term of CFS is periodic and performs like a Fast Fourier transform. We introduced a novel methodology for exploiting a CFS to develop a new model of the matrix, in which we identified a reference signal and removed noise from a specific signal.

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“…Moreover, the generalized 𝐹𝑆 𝑓𝑡 𝑆 theory has been given in [17] which modify the basic structure of𝐹𝑆 𝑓𝑡 𝑆. Also the idea of complex fuzzy soft matrices with their applications and a study of fuzzy soft AG-groupoids is given in [18][19].…”

Section: Introductionmentioning

confidence: 99%

Spherical Fuzzy Soft Rough Sets and Their Applications in Decision Making Problems

Mahmood

Ahmmad

Rehman

2022

Preprint

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Soft set theory, established by Molodtsov is the universal mathematical structure to tackle the vagueness in data. Since soft set can consider the parameterization structure and spherical fuzzy rough set (SFRS) is a dominant structure to categorize the objects, so due to this reason, here in this manuscript, we have combined, crisp soft relation and fuzzy soft relation respectively with SFRS tointroduce the idea of soft rough spherical fuzzy set and spherical fuzzy soft rough set which can be seen as two new universal soft rough set concepts. Moreover, the classical representations of approximation operators and approximation operators have been established and their properties are discussed in detail. Furthermore, an algorithm is given based on established work and illustrative example is proposed to show how the given idea works in decision making (DM) problems. Finally supremacy of the introduced work is shown and comparative analysis with existence tools by numerical example is established.

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Complex fuzzy soft matrices with applications

Abstract: In this paper, we introduce complex fuzzy soft matrices and define some new operations on these matrices. Moreover we develop an algorithm using complex fuzzy soft matrices and apply it to a decision making problem in signal processing.

Cited by 14 publications

References 8 publications

Riemannian Complex Fuzzy Sets with Applications in Signals

Riemannian Complex Fuzzy Sets with Applications in Signals

Denoising Data in Signal Processing under the Complex Fuzzy Environment

Spherical Fuzzy Soft Rough Sets and Their Applications in Decision Making Problems

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