Keywords
Fuzzy Soft set, Fuzzy code, interval-valued fuzzy soft code, generalised fuzzy soft set, Generalised fuzzy soft code.
This article is included in the Software and Hardware Engineering gateway.
Several scholars defined the concepts of fuzzy soft set theory and their application on decision-making problem. Based on this concept, researchers defined the generalised fuzzy soft set and its applications. However, to the best of the author’s knowledge, the generalised fuzzy soft set has not been dealing with in the generalised fuzzy soft code set. In this paper, we introduce the notion of generalised fuzzy soft code set and its application.
The theory of fuzzy soft sets and its application, generalised fuzzy soft sets and fuzzy codes in different years were studied with various researchers. To derive a generalised fuzzy soft code set, we apply the concepts of generalised fuzzy soft set and a new view of fuzzy codes and its application. A new aspect of this paper is to introduce the definition of generalised fuzzy soft code sets and its application on medical diagnosis and decision-makings.
Generalised fuzzy soft code is the most powerful and effective extension of fuzzy soft sets that deal with the choice’s parameterized values. It is an extended model of fuzzy soft sets and a new mathematical tool with significant advantages for handling uncertain information and is proposed by combining the concept of fuzzy soft sets and fuzzy code sets. This paper introduces the concept of generalised fuzzy soft code and its properties.
In this study, we combine generalised fuzzy soft set and a different approach to coding theory to introduce generalisation of fuzzy soft codes. The paper also considers the relation between generalised fuzzy soft code and its application. We discussed the matrix representation of generalised fuzzy soft code. Furthermore, a demonstration example illustrates how the strategy could be effectively applied to various problems.
Fuzzy Soft set, Fuzzy code, interval-valued fuzzy soft code, generalised fuzzy soft set, Generalised fuzzy soft code.
Title, keywords, abstracts, definitions, and spelling mistakes have all been corrected. Generally, we incorporated some minor and major changes have been made in response to reviewer’s suggestions and comments. And also, we cited all suggested works of literature
See the authors' detailed response to the review by Rana Muhammad Zulqarnain
See the authors' detailed response to the review by Tahir Mahmood
See the authors' detailed response to the review by Faruk Karaaslan
Zadeh (1965) established the notion of a “fuzzy set” as a way to represent a class of objects with different membership grades. A membership function, also known as a characteristic function, describes such a set, with each item’s membership degree ranging from 0 to 1. Molodtsov (1999) presented the theory of soft sets as a novel mathematical technique for handling uncertainties that are outside the scope of current mathematical techniques. In order to address complicated issues including ambiguity and uncertainty, Molodtsov created a universal mathematical tool that may be applied to both conventional and some modern mathematical methods. Fuzzy sets are conceptualized in a way that makes their methods intuitively clear. Maji, Biswas, and Roy (2003) developed the theory of soft sets. Gogoi, Kr. Dutta, and Chutia (2014) introduced an application of fuzzy soft set theory in day to day problems; Roy and Maji (2007) presented the application of fuzzy soft set theory to decision-making problems. Amudhambigai and Neeraja (2019) discussed a new view of fuzzy codes and its application. Ozkan and Mehmet (2002) introduced different approaches of fuzzy codes and their properties. Malik, Mordeson, and Nair (1992) defined the concept of a fuzzy generating set and describe the fuzzy subgroup which it generates and introduced the notion of a minimal fuzzy generating set. Ali et al. (2018) designed and develop a new class of linear algebraic codes defined as soft linear algebraic codes using soft sets. They also discussed some algebraic properties of soft codes. Garg and Arora (2018) describe the concept of generalized IFSS. Kong, Wang, and Wu (2011) studied the implementation of grey theory-based fuzzy soft sets in decision-making situations. Lin et al. (2018) a set of code words from the cyclic code are decoded using a binary parity-check matrix in a soft-joint manner. Gereme, Demamu, and Alaba (2023), Hamming distance of fuzzy codes and other features of binary fuzzy codes. (Lin et al. 2018) a novel coding technique for the Galois-Fourier transform domain that is intended for collective encoding and collective iterative soft-decision decoding of cyclic codes of prime lengths. Kamble (2017) examined the codes that emerged from soft sets and fuzzy sets, and using fuzzy linear space, explained the properties of fuzzy linear codes. Kim (2023) developed a fuzzy linear code description based on linear algebraic codes. Tsafack et al. (2018) established the concepts of fuzzy cyclic and linear codes over a Galois ring. Majumdar and Samanta (2010) established generalized fuzzy soft sets and investigated a few of their characteristics. It has been demonstrated that generalized fuzzy soft sets can be used to diagnose medical conditions and decision-making problem. Adde, Toro, and Jego (2012) examined linear block code of maximum likelihood soft-decision decoding. Dauda, Mamat, and Waziri (2015) an application of fuzzy soft sets based on a thorough theoretical analysis of the fundamental operations of soft sets and a definition of soft sets. Kané (2021) suggested a different method for figuring out the fuzzy optimal solution to a semi-fully fuzzy linear programming issue. Ali et al. (2023) discussed the generalized intuitionistic decision-theoretic rough set, a combination of intuitionistic fuzzy sets and decision-theoretic rough sets.
The researchers studied new operational laws for interval-valued Pythagorean fuzzy soft numbers and create two interaction operators-the interval valued Pythagorean fuzzy soft interaction weighted average and the interval-valued Pythagorean fuzzy soft interaction weighted geometric operators, and analysed their properties (Zulqarnain et al. 2023a). Scholar proposed the correlation coefficient and weighted correlation coefficient for interval valued Pythagorean fuzzy soft set and examine their necessary properties (Muhammad et al. 2023). Zulqarnain et al (2022a) investigated some average aggregation operators, such as q-rung ortho pair fuzzy soft Einstein weighted average and q-rung ortho pair fuzzy soft Einstein ordered weighted average operators. Zulqarnain et al. (2022b) developed some novel operational laws for q- rung ortho pair fuzzy soft numbers. The scholars introduced T-SF partitioned Bonferroni mean (T-SFPBM) and T-SF weighted partitioned Bonferroni mean (T-SFWPBM) operators to fuse the evaluation information provided by experts. Then, an IFM is designed to achieve a consensus between multiple experts (Gurmani et al. 2023a). The researchers proposed the concept of interval-valued probabilistic linguistic T-spherical fuzzy set (Gurmani et al. 2023b). Zulqarnain et al. (2024) introduced the interval-valued q-rung ortho pair fuzzy soft Einstein-ordered weighted and Einstein hybrid weighted aggregation operators. The scholars investigated the formulation of the q-rung ortho pair fuzzy soft Einstein hybrid weighted average operator and its specific characteristics (Zulqarnain et al. 2023b). The researcher developed a new approach to determining expert weights using distance and similarity measures for interval-valued q-rung ortho pair fuzzy numbers (Hussain & Harish 2023). The scholars proposed the generalized version of the multipolar neutrosophic soft set with operations and basic properties (Zulqarnain et al. 2021). Zulqarnain et al. (2022c) some novel operational laws for interval valued Pythagorean fuzzy soft set have been proposed. Zulqarnain et al. (2022d) developed the Einstein-weighted ordered aggregation operators for the Pythagorean fuzzy hyper soft set, which proficiently contracts with tentative and confusing data. The researchers also introduce the Einstein operational laws for Pythagorean fuzzy soft numbers (Zulqarnain et al. 2022e). The scholars described and evaluated the characteristics of the correlation coefficient (CC) and weighted correlation coefficient (WCC) for q-ROFSS (Muhammad et al. 2024). Studied the novel Einstein aggregation operators (AOs) for this model, specifically the interval-valued q-rung ortho pair fuzzy soft Einstein weighted average (IVq-ROFSEWA) and interval-valued q-rung ortho pair fuzzy soft Einstein weighted geometric (IVq-ROFSEWG) (Mukherjee & Mukherjee 2022). Zulqarnain et al. (2022) proposed Einstein’s operational laws for q-rung ortho pair fuzzy soft numbers (q-ROFSNs).
However, as far as the author’s awared, no studied pertaining to the fundamentals of generalised fuzzy soft code have been released. Thus, motivated by the formationed works, the current study seeks to introduce this idea. To derive a generalised fuzzy soft code and its properties, we integrate the concepts of a generalised fuzzy soft set (Majumdar and Samanta 2010) and a different approaches of coding theory (Ozkan & Mehmet 2002). A new aspect of this paper is to introduce and develop the definition and application of generalised fuzzy soft code by combining the concepts of fuzzy soft set and fuzzy codes. The definition and features of relations on generalised fuzzy soft codes are proposed.
The organization of this study as follows: Section 2 included fundamental notions and properties of preliminary concepts, Section 3, Generalised fuzzy soft code, and some of their characteristics, whereas, Section 4 takes into consider relation on generalised fuzzy soft codes and its application.
In this section, we review a few fundamental concepts that we utilize to get our outcomes.
Ozkan & Mehmet (2002) The notion is a q-ary code which is a collection of symbol sequences in which each symbol is chosen from a set of q different components. The set is sometimes referred to as the alphabet and is defined as the set
(Amudhambigai & Neeraja 2019) is the collection of all ordered tuples
Where each p i in . The elements of are referred to as words or vectors.
Ozkan & Mehmet (2002) Let us assume that has as a code word. The relative weight of a code word denoted as
, is the sum of if are defined as the positions of 1s in a . For instance, if is a code word of in .
is the relative weight of which is a code word of in . This can be articulated as the sum of the first n positive integers. The largest relative weight of code in is referred to as this matter. Where is the length of a code word, where is a mapping defined as for C ∈ F2 n and for any .
(Ozkan & Mehmet 2002) Let is the code word of with length n. Let be a code.
For each let be a fuzzy code word related to the code words , in . Then the fuzzy code intersection and union of any two fuzzy codes and , respectively, are given as follows:
(Amudhambigai and Neeraja 2019) For any fuzzy code , the complement is is calculated by subtracting from 1 the relative weight of each member of .
(Molodtsov 2011) Consider the idea of a set of parameters represented by and be the notion of a starting universe of items. is the power set of and . A pair is a soft set over if and only if is a mapping given by
In other words, the soft set is a parameterized family of subjects from the set U.
Consider the set of e ∈ A, or the set of approximate components of the soft set, where can be arbitrary, some of which can have nonempty intersection, and some of which can be empty. This applies to every F (e) from this family for e in . where the attributes, traits, or properties of objects are usually the arguments. We might consider Zadeh’s fuzzy set as a subset of the soft set. Given a fuzzy set , its membership function is , that is, μ A is a mapping from into that is
(Majumdar & Samanta, 2010) The set of all fuzzy subsets within a universal set is denoted by .
Assume and that is a parameter set. Then, a pair is a fuzzy soft set over , where is a mapping denoted by
(Ali et al. 2018) Assume that a vector space of size n over the field is represented by . is as of t algebraic linear code over . For every , the symbol in x indicates a linear algebraic code of . The number of linearly independent elements of is denoted by which is the dimension of . Each in this instance represents a linear algebraic code. The soft dimension of is and the number of soft code words of is given by:
Where .
Keep in mind that represents the soft dimension .
(Majumdar & Samanta 2010). The universal set of elements is , and the universal set of parameters is We shall refer to the pair as a soft universe. Let be a fuzzy subset of , that is,
have the following definition:
, where .
A generalised fuzzy soft set (in short GFSS) over the soft universe is then denoted by . Here, for every parameter , showed both the degree of belongingness that represents and the degree of belongingness of the elements of in
(Majumdar & Samanta 2010) Let and be two generalised fuzzy soft sets over Now is said to be a generalised fuzzy soft subset of if
(Majumdar and Samanta 2010) Consider a generalised fuzzy soft set over , denoted by . Then, represents the complement of , which is defined as
(Majumdar & Samanta 2010) represents the union of two generalised fuzzy soft sets and . This results in a generalised fuzzy soft set , which is defined as such that where and ° denotes a t-co norm.
(Majumdar & Samanta 2010) represents the intersection of two generalised fuzzy soft sets, and . This leads to a generalised fuzzy soft set , which is defined as
such that , where is a t-co norm and
(Majumdar & Samanta 2010) indicates a generalised null fuzzy soft set, which is referred to as a generalised fuzzy soft set, such that where (null set or no element is included under any parameter within the soft set), and, , for all .
(Majumdar & Samanta 2010) A generalised absolute fuzzy soft set, represented by
is refered to as a generalised fuzzy soft set, where
is defined by (maximal soft set or where every parameter is associated with the whole set under consideration) and , for all .
In this subsection, we introduce generalised fuzzy soft codes and its properties.
Let for which is the universal element, consider the set of parameters be and is an element of a code vector. The fuzzy soft code universe is a pair
Let where all fuzzy code subsets of are collected as . And consider a mapping and such that
is a representation of the possibility of membership, and the degree of belongingness of is indicated for each parameter by
Consider parameter set E, and define a fuzzy set on this parameter set μ(e) ∈ [0,1] for any set of R wr(a). And let
be a fuzzy code of the eight computers under consideration.
Let be the set of qualities of the given computers, where the symbols denote a super computer, a micro computer, a workstation computer, and a personal computer.
Let be a fuzzy code subset of R wr : C → [0,1] defined as follows:
And we define a function
A Generalised fuzzy soft code over is then .
This can be represented in matrix form as follows:
The GFSC is then can be defined as
and .
Given two GFSCs over let and . Specifically, is a subset of that is considered a generalised fuzzy soft code if
Here, we compose
The GFSC over is the one presented in example 1. Let be an additional GFSC defined as follows over
Here and are the respective mapping given by
Then, a generalised fuzzy soft code subset of is .
Matrix representation of is given by
(Intersection of GFSC). represents the GFSC intersection of two GFSCs of and over , and is defined as
(Union of GFSC) represents the GFSC union of two GFSCs of and over a soft universe , and is defined as
(Complement of GFSC). The following notation represents the complement of the GFSC of or Subtracting the GFSC of each member of from 1 yield . That is,
If and represent any GFSC over a shared soft universe , then the following result is hold:
The proof follows directly from the definition.
The following properties are hold, if and are two generalised fuzzy soft codes over the common universe
A generalised null fuzzy soft code, represented by is considered to be a generalised null fuzzy soft code if
A GFSC is said to be a generalised absolute fuzzy soft code, denoted by if where is defined by and
Given any GFSC over , let’s say . The generalised null fuzzy soft code and generalised absolute fuzzy soft code over denoted as and , respectively. Then, the following result is hold:
(i) Let be any GFSC over and let be the generalised null fuzzy soft code over . Then
(ii-v) Similar to the proof of (i)
Given a universal set consider two generalised fuzzy soft codes over and . Then, the addition operation modulo between and is represented by and the multiplication operation modulo between and is represented by . The interpretation of these notations are as follows:
In this subsection, we introduce generalised fuzzy soft codes and its properties.
Let for which is the universal element, consider the set of parameters be and is an element of a code vector. The fuzzy soft code universe is a pair
Let where all fuzzy code subsets of are collected as . And consider a mapping and such that
is a representation of the possibility of membership, and the degree of belongingness of is indicated for each parameter by
Consider parameter set E, and define a fuzzy set on this parameter set μ(e) ∈ [0,1] for any set of R wr(a). And let
be a fuzzy code of the eight computers under consideration.
Let be the set of qualities of the given computers, where the symbols denote a super computer, a micro computer, a workstation computer, and a personal computer.
Let be a fuzzy code subset of R wr : C → [0,1] defined as follows:
And we define a function
A Generalised fuzzy soft code over is then .
This can be represented in matrix form as follows:
The GFSC is then can be defined as
and .
Given two GFSCs over let and . Specifically, is a subset of that is considered a generalised fuzzy soft code if
Here, we compose
The GFSC over is the one presented in example 1. Let be an additional GFSC defined as follows over
Here and are the respective mapping given by
Then, a generalised fuzzy soft code subset of is .
Matrix representation of is given by
(Intersection of GFSC). represents the GFSC intersection of two GFSCs of and over , and is defined as
(Union of GFSC) represents the GFSC union of two GFSCs of and over a soft universe , and is defined as
(Complement of GFSC). The following notation represents the complement of the GFSC of or Subtracting the GFSC of each member of from 1 yield . That is,
If and represent any GFSC over a shared soft universe , then the following result is hold:
The proof follows directly from the definition.
The following properties are hold, if and are two generalised fuzzy soft codes over the common universe
A generalised null fuzzy soft code, represented by is considered to be a generalised null fuzzy soft code if
A GFSC is said to be a generalised absolute fuzzy soft code, denoted by if where is defined by and
Given any GFSC over , let’s say . The generalised null fuzzy soft code and generalised absolute fuzzy soft code over denoted as and , respectively. Then, the following result is hold:
(i) Let be any GFSC over and let be the generalised null fuzzy soft code over . Then
(ii-v) Similar to the proof of (i)
Given a universal set consider two generalised fuzzy soft codes over and . Then, the addition operation modulo between and is represented by and the multiplication operation modulo between and is represented by . The interpretation of these notations are as follows:
This research paper is an original work that has not been published or submitted elsewhere at the same time.
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Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Fuzzy extensions.
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Yes
Are all the source data underlying the results available to ensure full reproducibility?
No source data required
Are the conclusions drawn adequately supported by the results?
Yes
References
1. Wang X, Mostafa Khalil A: A New Kind of Generalized Pythagorean Fuzzy Soft Set and Its Application in Decision-Making. Computer Modeling in Engineering & Sciences. 2023; 136 (3): 2861-2871 Publisher Full TextCompeting Interests: No competing interests were disclosed.
Reviewer Expertise: Fuzzy set theory and applications, soft set theory, Fuzzy extensions, Sequence spaces, Water quality assessment
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Not applicable
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Fuzzy extensions.
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
I cannot comment. A qualified statistician is required.
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Fuzzy set, soft sets, decision-making, fuzzy graphs, fuzzy algebraic.
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Yes
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Yes
Are all the source data underlying the results available to ensure full reproducibility?
No source data required
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Alongside their report, reviewers assign a status to the article:
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