Interdependency of Complex Fuzzy Neighborhood Operators and Derived Complex Fuzzy Coverings

Mahmood, Tahir; Ali, Zeeshan; Gumaei, Abdu

doi:10.1109/access.2021.3074590

Cited by 22 publications

(9 citation statements)

References 28 publications

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“…As shown in Figure . 1, Yang and Hu put forward some types of fuzzy coverings-based rough set model based on the concepts of fuzzy β-neighborhood in [67,69]. Based on these work, some research focus on fuzzy neighborhood operators are addressed, such as [5,6,34]. Furthermore, a fuzzy covering-based rough set model was proposed in [66] by using fuzzy β-minimal description.…”

Section: The Motivation Of Our Researchmentioning

confidence: 99%

Fuzzy covering-based rough set on two different universes and its application

Bin

2021

Preprint

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In this paper, we propose a new type of fuzzy covering-based rough set model over two different universes by using Zadeh’s extension principle. We mainly address the following issues in this paper. First, we present the definition of fuzzy β-neighborhood, which can be seen as a fuzzy mapping from a universe to the set of fuzzy sets on another universe and study its properties. Then we define a new type of fuzzy covering-based rough set model on two different universes and investigate the properties of this model. Meanwhile, we give a necessary and sufficient condition under which two fuzzy β-coverings to generate the same fuzzy covering lower approximation or the same fuzzy covering upper approximation. Moreover, matrix representations of thefuzzy covering lower and fuzzy covering upper approximation operators are investigated. Finally, we propose a new approach to a kind of multiple criteria decision making problem based on fuzzy covering-based rough set model over two universes. The proposed models not onlyenrich the theory of fuzzy covering-based rough set but also provide a new perspective for multiple criteria decision making with uncertainty.

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Section: The Motivation Of Our Researchmentioning

confidence: 99%

Fuzzy covering-based rough set on two different universes and its application

Bin

2021

Preprint

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“…Thus, handling such sort of information is very complicated for a decisionmaker, for this, every expert has needed to change the range of FS, therefore, the main theory of complex FS was diagnosed by Ramot et al [35]. Certain applications of CFS have been done in shape, called distance measures [36], complex fuzzy hypersoft sets [37], neighborhood operators for CFS [38], algebraic structure [39], and entropy measures [40][41][42]. By utilizing the two-dimension data, the comprehensive data can be expected in one set, and hence, loss of data can be prevented.…”

Section: Introductionmentioning

confidence: 99%

Identification and Prioritization of DevOps Success Factors Using Bipolar Complex Fuzzy Setting With Frank Aggregation Operators and Analytical Hierarchy Process

et al. 2022

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DevOps ("development and operations") is a dominant and collaborative organizational effort to computerize simultaneously the supply of a software strategy with a theme to enhance software features. The usage of the DevOps technique is not an easy task as there is various vagueness involved with it. The main goal of this analysis is to employ the Frank t-norm and t-conorm (which is an accommodating class of norms and conorms and due to the parameters the Frank t-norm and t-conorm become more flexible in the combining the data and appropriate to address the real-life issues) in the environment of bipolar complex fuzzy theory to handle the DevOps procedures. Moreover, we define Frank's operational laws and their appropriate result employing BCF information. Then, we establish, the bipolar complex fuzzy (BCF) Frank weight averaging (BCFFWA), BCF Frank ordered weight averaging (BCFFOWA), BCF Frank hybrid weight averaging (BCFFHWA), BCF Frank weight geometric (BCFFWG), BCF Frank ordered weight geometric (BCFFOWG), BCF Frank hybrid weight geometric (BCFFHWG) operators and assess certain properties and results. Furthermore, we find the major factors that positively influence the DevOps technique in software organizations. In the presence of the prevailing information, nineteen factors were diagnosed. The diagnosed features were moreover authorized by intellectuals via hypothetical information. Finally, BCF analytical hierarchy process (BCFAHP) technique is invented to prioritize the classification success features. The final ranking mentioned that "DevOps security pipeline", "use system orchestration", and "attempt matrix organization and transparency" features are the beneficial ranked success features for the valuable utilization of DevOps techniques. Finally, we compare the diagnosed operators with various existing theories to improve the worth of the invented approaches.

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“…The fuzzy set is one of the most important cases of the CF set, because if we choose , then the value of the supporting grade in the CF set is changed to the value of the supporting grade in a fuzzy set. Certain applications have been done by different people, for instance, cross-entropy measures [ 21 ], neighborhood operators [ 22 ], orthogonality [ 23 ], and complex fuzzy soft set theory [ 24 ]. But still, the grade of supporting against is missing in the CF information, due to these issues many scholars have faced a lot of ambiguity; therefore, to handle such kind of hurdles, Alkouri and Salleh [ 25 ] diagnosed the complex IF (CIF) set with two grades in the shape of polar coordinates in the shape: and .…”

Section: Introductionmentioning

confidence: 99%

Archimedean Aggregation Operators Based on Complex Pythagorean Fuzzy Sets Using Confidence Levels and Their Application in Decision Making

Лю

Ali

Mahmood

2022

Int. J. Fuzzy Syst.

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The diagnosed complex Pythagorean fuzzy (CPF) set is a more valuable and dominant tool than the Pythagorean and intuitionistic fuzzy sets to describe awkward and unreliable information more effectively. Further, Archimedean t-norm and t-conorm have a significant influence to depict the relation among aggregated values. To take advantage of the CPF set and Archimedean t-norm and t-conorm, and assume the relation among Archimedean norms and algebraic, Einstein, Hamacher, and frank norms at the same time, in this analysis, first, we proposed the fundamental Archimedean operational laws. Second, based on these laws, we developed confidence CPF Archimedean-weighted averaging (CCPFSAWA), confidence CPF Archimedean-ordered weighted averaging (CCPFSAOWA), confidence CPF Archimedean-weighted geometric (CCPFSAWG), confidence CPF Archimedean-ordered weighted geometric (CCPFSAOWG) operators and implemented their valuable results and properties. We know that Archimedean t-norm and t-conorm are the general form of the all-aggregation operators, so by using different values of t-norm and t-conorm, we explored the confidence CPF-weighted averaging (CCPFWA), confidence CPF-ordered weighted averaging (CCPFOWA), confidence CPF Einstein-weighted averaging (CCPFEWA), confidence CPF Einstein-ordered weighted averaging (CCPFEOWA), confidence CPF Hamacher-weighted averaging (CCPFHWA), confidence CPF Hamacher-ordered weighted averaging (CCPFHOWA), confidence CPF frank-weighted averaging (CCPFFWA), confidence CPF frank-ordered weighted averaging (CCPFFOWA), confidence CPF-weighted geometric (CCPFWG), confidence CPF-ordered weighted geometric (CCPFOWG), confidence CPF Einstein-weighted geometric (CCPFEWG), confidence CPF Einstein-ordered weighted geometric (CCPFEOWG), confidence CPF Hamacher-weighted geometric (CCPFHWG), confidence CPF Hamacher-ordered weighted geometric (CCPFHOWG), confidence CPF frank-weighted geometric (CCPFFWG), and confidence CPF frank-ordered weighted geometric (CCPFFOWG) operators. Then, we developed a multi-attribute decision-making (MADM) method based on the proposed operators. Finally, many examples are used to do comparative analysis among proposed and existing methods to show the validation of the new approaches.

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Interdependency of Complex Fuzzy Neighborhood Operators and Derived Complex Fuzzy Coverings

Cited by 22 publications

References 28 publications

Fuzzy covering-based rough set on two different universes and its application

Fuzzy covering-based rough set on two different universes and its application

Identification and Prioritization of DevOps Success Factors Using Bipolar Complex Fuzzy Setting With Frank Aggregation Operators and Analytical Hierarchy Process

Archimedean Aggregation Operators Based on Complex Pythagorean Fuzzy Sets Using Confidence Levels and Their Application in Decision Making

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