Abstract
In this article, firstly, we describe picture fuzzy N-soft sets (PFN-SSs) as a generalization of picture fuzzy sets (PFSs) and N-soft sets (N-SS) by observing that one of the essential concept of neutral grade is missing in intuitionistic fuzzy N-SS (IFN-SS) theory. The concept of neutrality grade can be observed in the situation when we encounter human views including more answers of type: yes, abstain, no, refusal. For instance, in election the election commission or election council issues voting papers for the candidate. The voting outcomes are categorized into 4 groups with the number of papers namely, vote for, abstain, vote against, and refusal voting. Further, We define the fundamental properties of PFN-SS and introduce M-subset, F-subset, compliment, intersections, unions, of PFN-SS and give their examples. Secondly, we define an algorithm to cope with PFN-SS data which is more generalized then the algorithm defined for IFN-SS. To show the advantage and usefulness of the defined technique, we give two examples from real life by utilizing PFN-SS data. The result shows in the comparison that our initiated method is more general and suitable than the IFN-SS, fuzzy N-SS (FN-SS), and N-SS.
1. Introduction
The majority of the real-life problems include uncertainty and vagueness in data. Handling such data is a big issue in many fields like engineering, social sciences, medical sciences, and economics. In light of this worry, Zadeh [Citation1] presented fuzzy sets (FSs). The idea of FS is truly manageable because it catches vague and uncertain data. In FSs theory the grade of membership belongs to a closed interval [0,1]. Nonetheless, it may not generally be right to assume that the grade of non-membership in FS is equivalent to 1 minus the grade of membership, due to the presence of the degree of uncertainty or indeterminacy. Consequently, Atanassov [Citation2] defined the idea of intuitionistic FSs (IFSs), which is a suitable generalisation of FSs. After this, Cuong [Citation3] characterised a new idea by putting an additional grade which is a grade of neutral membership, and named it to picture FS (PFS). It triply contains positive membership, neutral membership, and negative membership. Researchers show interest to work on PFS because it is straightforwardly applied to tackle everyday life issues. Some fuzzy logic operators for PFSs were given by Cuong and Van Hai [Citation4]. Luo and Zhang et al. [Citation5] described new similarity measures for PFSs and its applications. Khan et al. [Citation6] presented bi-parametric distance and similarity measures of PFSs and their applications in medical diagnosis. Khan et al. [Citation7] presented distance and similarity measures for spherical FSs and their application in selecting mega projects. Mahmood et al. [Citation8] gave the concept of complex hesitant FSs (CHFSs). The idea of complex dual hesitant FSs (CDHFSs) was given by Mahmood et al. [Citation9].
Nonetheless, Molodtsov [Citation10] showed that few challenges of the previously mentioned notions can be overwhelmed with the establishment of parameterised interpretation of the universe, an idea giving rise to soft sets (SSs) [Citation10]. Soon, Maji et al. [Citation11] established few algebraic operations in SS theory, which currently assume a significant part in fields such as DM [Citation12–16] and data analysis [Citation17]. Ali et al. [Citation18] defined some modified operations in SS theory. Anyway, the information frequently comes across in a complex structure. A lot of researchers have developed new models to overcome such types of data and merge the advantages of the presented models. These models are developed to resolve a vast range of DM issues since they have more advantages than the original models. For example, by the mixture of FSs and SSs, Maji et al. [Citation19] defined a new model, called fuzzy SS (FSS). A new idea of DM issues based on FSSs was given by Alcantud [Citation20]. Maji et a. [Citation21] gave the idea of intuitionistic FSSs (IFSSs) which is a mixture of IFSs and SSs. Khan et al. [Citation22] defined a novel approach to generalised IFSSs and its application in decision support system. Khan et al. [Citation23] gave another view on generalised interval-valued IFSSs. The renewable energy source selection by remoteness index-based VIKOR method for generalised IFSSs was presented by khan et al. [Citation24]. The distance and similarity measures of generalised IFSSs were defined by khan and Kumam [Citation25]. Yang et al. [Citation26] defined an adjustable soft discernibility matrix based on PFSSs and its application in DM. Khan et al. [Citation27] defined generalised PFSSs and described some modified operations for PFSSs. Jan et al. [Citation28] defined multi-valued PFSSs and their applications in group DM problems. The applications of generalised PFSSs in concept selection were given by khan et al. [Citation29]. Khan et al. [Citation30] described an adjustable weighted soft discernibility matrix based on generalised PFSS and its applications in decision-making. Khan et al. [Citation31] defined a theoretical-justifications for the empirically successful VIKOR approach to multi-criteria DM. Akram et al. [Citation32] described hybrid DM frameworks under complex spherical FSSs.
We noted a lot of modifications of SSs in various directions such as probabilistic SS theory [Citation33], bijective SSs [Citation34], and fuzzy bipolar SSs [Citation35]. Specifically, Fatimah et al. [Citation36] introduced N-SS as a modification of SS that takes into consideration multinary parameterised interpretation of the universe. Alcantud et al. [Citation37] interpreted an N-SS approach to rough sets. Kamaci and Petchimuthu [Citation38] defined bipolar N-SS theory with applications. Fatimah and Alcantud [Citation39] presented the multi-fuzzy N-SS and its applications to DM. Akram et al. [Citation40] defined complex neutrosophic N-SS. A hybrid DM approach under complex Pythagorean fuzzy N-SS was given by Akram et al. [Citation41]. Mahmood et al. [Citation42] defined the complex picture fuzzy N-SSs. Akram et al. [Citation43] provided the parameter reductions in N-SSs and their applications in DM. The inspiration for their defined N-SS is given as. An investigation of models with SSs as one of their components had indicated that the authors were motivated by SSs and their work of assessments (memberships are either 0 or 1) or the situation with partial memberships containing in [0,1] [Citation17,Citation44]. Anyway, a lot of real-life DM issues contain information with discrete and non-binary structure, which consequently didn’t in the shape of those formats. It is simple to create examples including non-binary assessments. We frequently get them in ranking or rating position such as websites comparing software, movies, or mobile phones. Even though they frequently appear as several stars or dots (such as four stars, three stars, two stars, one star), which can simply be linked with natural numbers. As additional evidence of the capability of N-SS theory, Akram et al. [Citation45] merge the N-SS with FS to defined FN-SS. After this, Akram et al. [Citation46] defined the notion of IFN-SS which the mixture of IFS and N-SS.
IFN-SS is a good tool to deal with uncertainty and is in the form of , where is an intuitionistic fuzzy number and is N-SS. It is noticed that one of the essential notion of neutrality grade is missing in IFN-SS theory. The notion of neutrality grade can be observed in the situation when we encounter human views including more answers of kinds: yes, abstain, no, and refusal. For instance, in an election, the election commission or election council issues ballot papers for the contestant. The voting outcomes are categorised into 4 groups with the number of papers, i.e. vote for, abstain, vote against, and refusal voting. Class of abstain signifies that the voting paper is a white paper refusing both agree and disagree for the contestant but still takes the vote. Class of refusal voting is either unacceptable papers or avoiding the vote. Nevertheless, the neutral grade can be taken into the consideration in medical diagnosis. For example, there may not influence the symptoms of fever, leg pain on the diseases heart problem, and brain tumour. Likewise, the symptoms of heart problems and blood cancer have a neutral influence on the diseases flu, corona, TB, etc. In IFN-SS we lost some of the important information in the shape of neutral grade which effect the end result in the decision-making problem. To overcome this and other weaknesses presented in some existing models in this article, we presented the notion of PFN-SS which is the extension of IFN-SS, FN-SS and will perform better than IFN-SS in DM. The geometrical representation of the PFN-SS is given in Figure .
The organisation of this article as follows:
Section 2 is consists with preliminaries in which we recall few basic definitions such as FSs, IFS, PFSs, SSs, FSS, IFSS, PFSS N-SSs, and IFN-SS. We also recall the basic properties of some of the above basic definitions.
Section 3 is a picture fuzzy N-Soft set in which we initiate PFN-SSs which is a mixture of PFSs and N-SS and explain it by giving examples. We also initiate the fundamental properties of PFN-SS in Section 3.
Section 4 of this article is applications of PFN-SS in which we introduce an algorithm to cope with PFN-SS data and to show the advantage and usefulness of the defined technique.
Section 5 is a comparison in which we do a comparison of our initiated model with IFN-SS.
In Section 6, the conclusion and future study of the proposed work are presented.
2. Preliminaries
In this section, let us recall few basic definitions such as FSs, IFS, PFSs, SSs, FSS, IFSS, PFSS N-SSs, and IFN-SS. We also recall the basic properties of some of the above basic definitions. be set of universe and be set of attributes throughout this manuscript.
Zadeh [Citation1], defined the idea of FS, which gives a powerful structure to dealing with uncertainty.
Definition 1:
[1] A FS over is given as
,
where is a grade of membership. For every , describes the degree to which be a member of FS . The pair is called fuzzy number (FN)
In [Citation1] Zadeh described some basic operations which are as follows:
Definition 2:
[1]Let and be two FNs. Then
;
;
;
.
Definition 3:
[Citation2] An IFS over is given as where is a grade of membership and is the grade of non-membership with a condition that for each . The pair is called intuitionistic FN (IFN).
In [Citation3], Cuong described PFSs by putting an additional grade of membership called the grade of neutral membership. Mainly, the notion of PFS may be appropriate in the environment when one faces human judgments containing more replies of the form: yes, no, abstain refusal. Voting is the best example of PFS.
Definition 4:
[Citation3] A PFS over is given as where , is a grade of membership, is the grade of neutral membership and is the grade of non-membership with a condition that for each . The pair is called picture FN (PFN).
In [Citation3] Cuong also defined basic operations of PFS which are given below
Definition 5:
[3]
Let and be two PFNs. Then
;
, ;
;
;
.
Definition 6:
[10] A pair is a SS over where be set-valued function and .
Maji et al. [Citation19] described the FSS, which is a mixture of FS and SS. In FSS, each parameter should be represented by grade of membership, as in real-world, the opinion of the people is represented by some degree of imprecision.
Definition 7:
[19] A pair represents an FSS over if , where represents the set of all FS of .
Maji et al. [Citation21] described the IFSS, which is a mixture of IFS and SS.
Definition 8:
[21] A pair describes an IFSS over if , where represents the set of all IFS of .
In [Citation26], Yang described the PFSS, a mixture of PFS and SS. In PFSS, we can view vagueness from a parametrisation opinion in a picture fuzzy (PF) environment.
Definition 9:
[26] Let be a set of all PFSs over . A pair is called the PFSS over , where .
Fatimah et al. [Citation36] described the idea of N-SS which is the modification of SS.
Definition 10:
[36] Let a universal set of alternatives be and be attributes. A triplet is called N-SS over , where , with the property that for every and a unique such that , be ordered grades set.
Fatimah et al. [Citation36] also described the extended and restricted intersection and union of N-SS which is given below
Definition 11:
[36] Let and be two N-SSs with . Then the restricted union and the intersection of and are described below: where . where .
Definition 12:
[Citation36] Let and be two N-SSs. Then the extended union and the intersection of and are described below where where
Akram et al. [Citation46], defined IFN-SS.
Definition 13:
[46] Let a universal set of alternatives be , be attributes, and be ordered grades set . A pair is called IFN-SS, where is an N-SS on , and , where is the set of all IFS over .
3. Picture Fuzzy N-soft Sets
In this part of the article, we describe the notion of PFN-SS and present some basic operations on PFN-SS such as union, complement, intersection, and so on. PFN-SS is a hybrid model of PFS and N-SS. In this article, represent the universe set, be set of attributes, be a set of ordered grades and be N-SS over , where .
Now we describe the definition of a PFN-SS and give an example to illustrate it.
Definition 14:
Let be universe set and be set of attributes. A pair is called PFN-SS over , where be an N-SS over with and is a mapping from to , i.e. , where be the set of all PFSs over .
In the above definition, for each parameter (attribute) the mapping allot a PFS on the image of that attribute under the mapping . Consequently, for every and a unique such that and , which is the notation that condenses to .
Example 1:
Suppose be a set of five corona vaccines which is under consideration of a health expert to buy and be a set of attributes, where represents price, represents experimental results of vaccines, and represents delivery time. Based on these attributes the health expert assigns grades to the corona vaccines in the form of stars which is given in Table . Where Four stars are for ‘Excellent', three stars are for ‘very good', two stars are for ‘good', one star is for ‘normal', a hole is for ‘poor’.
The set can be associated with the stars given in Table , where 4 represents , 3 represents , 2 represents , 1 represents , and 0 represents . The 5-SS is given as follows:
The tabular form of 5-SS is interpreted in Table .
The membership , neutral , and non-membership follow the following grading criteria. For all , where . We allot grade 0 if . Similarly we relate: grade 1 if , grade 2, if . Then by Definition 14 the PF5-SS is given as follows:
Clearly, the PF5-SS can be described in the tabular form given in Table .
Example 2:
A multi-national company wants to enhance the security of its wireless network. The company plans to buy the next generation firewall for blocking illegal access. For that, the company hire an expert and provide him four next generation firewalls, i.e. . The expert have to finalise one of the best next generation firewalls in these four firewalls based on the three given attributes , where represents scan for viruses and malware in allowed collaborative applications, represents identify and control applications sharing the same connection, and represents decrypt outbound SSL. Based on these attributes the expert assigns grades to given four next generation firewalls in the form of stars. A 3-soft set can be obtained from Table , where three stars are for ‘very good’, two stars are for ‘good', one star is for ‘normal', a hole is for ‘poor'.
The set can be associated with the stars given in Table , where 3 represents , 2 represents , 1 represents , and 0 represents . The 4-SS is given as follows:The tabular form of 4-SS is interpreted in Table .
The membership , neutral , and non-membership follow the following grading criteria. For all , where . We allot grade 0 if . Similarly we relate: grade 1 if , grade 2, if Then by Definition 14, the PF5-SS is given as follows:
Clearly, the PF5-SS can be described in the tabular form given in Table (Table ).
The above Examples 1 and 2 are examples of PFN-SS. Later in Section 4, we will get the results of both examples by solving them by our proposed algorithm.
Any PFN-SS over is also called PF(N+1)-SS. E.g. in Example 1, we have PF5-SS but we can also call it PF6-SS. In PF6-SS we suppose that we have 5 grades which are never used in the Example 1.
Here we describe two kinds of containment in PFN-SS, i.e. F-subset and M-subset.
Definition 15:
Let and be two PFN-SSs over . Then is called picture fuzzy N-soft (PFN-S) F-subset of , designated by , if
,
, , and .
Example 3:
Let be a universe set and , where , and . Let and be PF4-SS and PF5-SS over respectively. The tabular form of and is given in Tables 7 and 8 respectively.
We can see that , and for all . So this implies that .
Definition 16:
Let and be two PFN-SSs over . Then is called picture fuzzy N-soft M-subset of , designated by , if
,
, , and .
Example 4:
Let be a universe set and , where , and . Let and be two PF5-SS over . The tabular form of and is given in Tables 9 and 10, respectively.
We can see that , and for all . So this implies that .
Definition 17:
Let and be two PFN-SSs over . Then is called PFN-S equal to , designated by , if
,
, , and .
Definition 18:
A PFN-SS over is efficient if for some .
Example 5:
Let PF5-SS over in tabular form as given in Table , which is efficient as we can see that for and we have in .
Definition 19:
An efficient PFN-SS is said to be minimised efficient denoted by and describe as for some and for all , .
Next, we describe the definitions of picture fuzzy (PF) weak complement, top and bottom weak complements of PFN-SS.
Definition 20:
The PF weak complement of the PFN-SS over , is denoted by , where is a weak complement of N-SS, i.e. for all , and for all .
Example 6:
A PF weak complement of PF5-SS given in Table of Example 1, denoted by , is described in Table .
Definition 21:
The top PF weak complement of a PFN-SS is where,
Example 7:
A top PF weak complement of PF5-SS given in Table of Example 1, denoted by , is described in Table .
Definition 22:
The bottom PF weak complement of a PFN-SS is where,
Example 8:
A bottom PF weak complement of PF5-SS given in Table of Example 1, denoted by , is described in Table .
The notion of union and intersection of PFN-SS are given below
Definition 23:
Let and be two PFN-SS over , where and are N-SSs over . Then their restricted intersection is represented by and is describe as , where , , , , , and if and .
Example 9:
Let be a universe set and , where , and . Let and be PF5-SS and PF6-SS over respectively. The tabular form of and is given in Tables 15 and 16 respectively. Then their restricted intersection is given in Table 17.
Definition 24:
Let and be two PFN-SS over , where and are N-SSs over . Then their restricted union is represented by and is describe as , where , , , , , and if and .
Example 10:
Let be a universe set and , where , and . Let and be PF5-SS and PF6-SS over respectively. The tabular form of and is given in Tables 15 and 16 respectively. Then their restricted union is given in Table 18.
Definition 25:
Let and be two PFN-SS over , where and are N-SSs over . Then their extended intersection is represented by and is describe as , where where
Example 11:
Let be a universe set and , where , and . Let and be PF5-SS and PF6-SS over respectively. The tabular form of and is given in Tables 15 and 16 respectively. Then their extended intersection is given in Table 19.
Definition 26:
Let and be two PFN-SS over , where and are N-SSs over . Then their extended union is represented by and is describe as , where where
Example 12:
Let be a universe set and , where , and . Let and be PF5-SS and PF6-SS over respectively. The tabular form of and is given in Tables 15 and 16 respectively. Then their extended union is given in Table 20.
Now we linked PFSSs with PFN-SS.
Definition 27:
Let be PFN-SS over , and be a threshold. A PFSS linked with and , represented by , is described as follows: Specifically, we say that is the bottom PFSS linked with and is the top PFSS linked with .
Example 13:
Let a PF5-SS given in Table of Example 1. From Definition 27, we have . The possible PFSSs associated with thresholds 1, 2, 3, and 4 are presented from Tables .
4. Applications of the PFN-SS
In this part of the manuscript, we interpreted the algorithm for the real-life problems that are presented in the form of PFN-SS. To show the usefulness of the PFN-SS we present the two examples (selection of corona vaccine and selection of next generation firewall) of DM using PFN-SS.
4.1. Algorithm
We describe an algorithm for the selection of the alternative in the environment PFN-SS. We have the following 9 steps of the algorithm. The flow chart of of defined algorithm is given in .
Input as universal set and as a set of attributes.
Create a PFN-SS in tabular form.
Create the tables of Membership pole (MP), neutral membership pole (nMP), and non-membership pole (NMP).
Compute the comparison tables for membership pole, neutral membership pole, and non-membership pole.
Create the score tables for membership pole, neutral membership pole, and non-membership pole.
Add the scores of neutral membership pole, and non-membership pole.
Find out the final score by subtracting the sum of the scores of neutral membership pole, and non-membership pole from the score of membership.
Observe the greatest score with the highest grades, if it is in row, then we will select option ,
The end.
4.2. Selection of Corona Vaccine
The world is in the midst of a COVID-19 pandemic. A huge number of people are as yet exposed to coronavirus. It’s just the current restrictions that are saving extra people from the deathbed. A vaccine would show our bodies to oppose the infection by preventing us from getting coronavirus or if nothing else making Covid less dangerous. Having a vaccine, alongside better treatments, is the exit strategy. WHO and other medical companies are working on the development of the corona vaccine. Nowadays few medical companies succeed in the development of the corona vaccine. In the following example, we will show that a country can select the best corona vaccine using PFN-SS.
Example 14:
Suppose a country wants to buy a corona vaccine, for this government hires a health expert who will select the best corona vaccine. Let be a set of five corona vaccines which is under consideration of a health expert to buy and be a set of attributes, where represents price, represents experimental results of vaccines, and represents delivery time. Based on these attributes, the health expert assigns grades to the corona vaccines in the form of stars which is given in Table in Section 3. The 5-SS is presented in Table of Section 3 and PF5-SS is presented in Table of Section 3.
The tabular form of the MP is given in Table .
Next, we create the comparison table for MP which is presented in Table .
After this the membership score for every corona vaccine with the sum of grades is gotten by subtracting the column sum from the row sum of Table . We presented it in Table .
Likewise, the tabular form of the nMP is given in Table .
Next, we create the comparison table for nMP which is presented in Table .
After this the neutral membership score for every corona vaccine with the sum of grades is gotten by subtracting the column sum from the row sum of Table . We presented it in Table .
Similarly, now the tabular form of the NMP is given in Table .
Next, we create the comparison table for NMP which is presented in Table .
After this the non-membership score for every corona vaccine with the sum of grades is gotten by subtracting the column sum from the row sum of Table . We presented it in Table .
Finally, we add the scores of neutral membership and non-membership and then subtract it by the score of membership as presented in Table . In Table , we show the addition of scores of neutral membership and non-membership.
The corona vaccine has highest grades with the greatest score so the health expert wants to buy the corona vaccine for his country. He thinks that is the best corona vaccine among the present 5 corona vaccines which can save more people from the corona.
4.3. Selection of a Next Generation Firewall (NGFW)
The firewall refers to a security gateway that protects the computer networks from malicious instructions. There are several kinds of firewalls in the market and every firewall has its benefits and disbenefits. Therefore, how to select a beneficial firewall by seeing its effectiveness and precision is a conventional multi-attribute DM (MADM) problem.
Chances are you have probably heard or read about next generation firewalls, it seems like everyone is talking about what a great innovation it is in the network security market. Traditional firewalls are becoming less capable of adequately protecting enterprise wireless networks with the rapid evolution of applications and threats. Instead of a completely new approach to wireless network security, the traditional firewall has been transformed into next generation firewall to restore application visibility and control and regain the advantage in protecting the enterprise wireless networks. See Figure .
In this article, we discussed that a company wants to purchase a next generation firewall whose details are given in Example 2 Section 3. Below in Example 15 we will reconsider Example 2 to get the result.
Example 15:
A multi-national company wants to enhance the security of its wireless network. The company plans to buy the next generation firewall for blocking illegal access. For that, the company hire an expert and provide him four next generation firewalls, i.e. . The expert have to finalise one of the best next generation firewalls in these four firewalls based on the three given attributes , where represents scan for viruses and malware in allowed collaborative applications, represents identify and control applications sharing the same connection, and represents decrypt outbound SSL. Based on these attributes the expert assigns grades to given four next generation firewalls in the form of stars. A 3-soft set can be obtained from Table , where three stars are for ‘very good', two stars are for ‘good', one star is for ‘normal', a hole is for ‘poor'.
The set can be associated with the stars given in Table , where 3 represents , 2 represents , 1 represents , and 0 represents . The 4-SS is given as follows:
The tabular form of 4-SS is interpreted in Table .
The membership , neutral , and non-membership follow the following grading criteria. For all , where . We allot grade 0 if . Similarly we relate: grade 1 if , grade 2, if Then by Definition 14, the PF5-SS is given as follows:
Clearly, the PF5-SS can be described in the tabular form given in Table .
The tabular form of the MP is given in Table .
Next, we create the comparison table for MP which is presented in Table .
After this the membership score for every firewall with the sum of grades is gotten by subtracting the column sum from the row sum of Table . We presented it in the Table (Table ).
Likewise, the tabular form of the nMP is given in Table .
Next, we create the comparison table for nMP which is presented in Table .
After this the neutral membership score for every firewall with the sum of grades is gotten by subtracting the column sum from the row sum of Table . We presented it in Table .
Similarly, now the tabular form of the NMP is given in Table .
Next we create the comparison table for NMP which is presented in Table .
After this the non-membership score for every firewall with the sum of grades is gotten by subtracting the column sum from the row sum of Table . We presented it in Table .
Finally, we add the scores of neutral membership and non-membership and then subtract it by the score of membership as presented in Table . In Table we show the addition of scores of neutral membership and non-membership.
The firewall has the greatest score so the expert wants to prefer the firewall to the company to purchase.
5. Comparison
In this segment, we will compare our presented work PFN-SS with IFN-SS. By the following, we will show that the PFN-SS is more general and more effective than the IFN-SS.
Example 16:
An expert wants to give his opinion about the presidential election of America. Let there are 3 presidential candidates in the election, i.e. , and be a set of attributes, where represents experience, represents working abilities, and future vision. An expert gives them grades based on these attributes in the shape of stars which is presented in Table . Where Four stars are for ‘Excellent', three stars are for ‘very good', two stars are for ‘good', one star is for ‘normal', a hole is for ‘poor’.
The set can be associated with the stars given in Table , where 4 represents , 3 represents , 2 represents , 1 represents , and 0 represents . The 5-SS is given as follows:
The tabular form of 5-SS is interpreted in Table .
An expert link an IFN with each of the grade given by his self. In the result, he will get IF5-SS which is presented in Table ().
To explain this more we consider the cell in the 1st row and 3rd column, i.e. , where 3 is the grade given by the expert to the candidate on his working ability, be the grade of membership which is given by the expert to the candidate based on his working ability and 0.1 is the grade of non-membership given to the candidate by the expert.
Now we will use the algorithm defined for IFN-SS by Akram et al. [Citation46] to find which candidate will win the election in the expert’s opinion. The tabular form of MP is described in Table . We create the comparison table for MP which is presented in Table . After this the membership score for every presidential candidate with the sum of grades is gotten by subtracting the column sum from the row sum of Table . We presented it in Table .
Similarly, now the tabular form of the NMP is given in Table . We create the comparison table for NMP which is presented in Table . After this the non-membership score for a presidential candidate with the sum of grades is gotten by subtracting the column sum from the row sum of Table . We presented it in the Table .
Finally, we subtract the score of non-membership by the membership which is described in Table .
According to the algorithm defined by Akram et al. [Citation46], the expert found that presidential candidate has highest grades with the greatest score so he thinks will the next president of America.
Later the expert realised that in this DM, he forgot to count the grade of neutral membership. Now he wants to add the grade of neutral membership in this DM because he knows that the voters may be categorised into 4 groups, i.e. vote for, abstains, vote against, and refusal of the voting. In the above DM, he has no abstain so now he wants to add this in this DM to get a more accurate and exact result. But he can’t add this grade of neutral membership in the environment of IFN-SS. IFN-SS is unable to solve such data. To get the result the expert will use PFN-SS. Reconsider Example 15, The expert will link a PFN to every grade given in Table . As a result, he will get PF5-SS which is presented in Table .
To explain this more we consider the cell in the 1st row and 3rd column, i.e. , where 3 is the grade given by the expert to the candidate on his working ability, be the grade of membership which is given by the expert to the candidate based on his working ability, is the grade of neutral membership, and 0.1 is the grade of non-membership given to the candidate by the expert.
Now we will use the algorithm defined in Section 4 of this article to find that which candidate will win the election in the expert’s opinion. The tabular form of MP is described in Table . We create the comparison table for MP which is presented in Table . After this the membership score for every presidential candidate with the sum of grades is gotten by subtracting the column sum from the row sum of Table . We presented it in Table .
Likewise, the tabular form of the nMP is given in Table .We create the comparison table for nMP which is presented in Table . After this the neutral membership score for every presidential candidate with the sum of grades is gotten by subtracting the column sum from the row sum of Table . We presented it in Table .
Similarly, now the tabular form of the NMP is given in Table . We create the comparison table for NMP which is presented in Table . After this the non-membership score for a presidential candidate with the sum of grades is gotten by subtracting the column sum from the row sum of Table . We presented it in Table .
Finally, we add the scores of neutral membership and non-membership and then subtract it by the score of membership as presented in Table . In Table , we show the addition of scores of neutral membership and non-membership.
According to the algorithm defined in Section 4 of this article, the expert found that presidential candidate has highest grades with the greatest score as presented in Table , so he thinks will the next president of America.
From Example 16, we observe the following results
When the expert uses the data in the form of IFN-SS, the candidate is selected as a president but when he took the data in the shape of PFN-SS, the candidate is selected as president.
In the IFN-SS the expert ignores the grade of neutral membership which effected the result of selecting candidates as we can see that after adding the neutral membership the result is different from the previous result.
On viewpoint 1 and 2 we can say that our proposed model PFN-SS give more precise and accurate result in DM.
Our defined algorithm in Section 4 is more general than the algorithm defined by Akram et al. [Citation46]. One can solve IFN-SS data by our proposed algorithm by taking the neutral membership zero. By our proposed algorithm one can also solve the FN-SS by taking neutral membership and non-membership zero.
Our described model PFN-SS is more general than the IFN-SS and FN-SS.
PFN-SS is more effective and powerful than the existing methods.
6. Conclusion and Future Study
It is observed that one of the essential concept of neutral grade is missing in the IFN-SS theory. Concept of neutrality grade can be observed in the situation when we encounter human views including more answers of type: yes, abstain, no, refusal. To overcome this issue, we interpreted the idea of PFN-SS by merging PFSs and N-SSs in this manuscript. We defined some relevant properties of PFN-SS such as a compliment, restricted intersection and union, extended intersection, and union, M-subset, and F-subset of PFN-SS. We also linked PFSSs with PFN-SS by using a threshold. Further, we gave numerical examples to explain these notions. We described an algorithm to deal with PFN-SS data. To show the advantage and usefulness of the defined technique, we used two examples which are
Selection of corona vaccine
Selection of next generation firewall
In near future, we aim to discuss this study in the environment of spherical fuzzy sets, T-spherical fuzzy sets [Citation47], the environment of bipolar soft sets [Citation48], etc. [Citation49–52].
Data Statement
The data used in this manuscript is artificial and anyone can use it without prior permission by just citing this article.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
Ubaid Ur Rehman
Ubaid Ur Rehman received the MSC degrees in Mathematics from International Islamic University Islamabad, Pakistan, in 2018. He received has M.S. degrees in Mathematics from International Islamic University Islamabad, Pakistan, in 2020. Currently, He is a Student of Ph. D in mathematics from International Islamic University Islamabad, Pakistan. His research interests include similarity measures, soft set, fuzzy logic, fuzzy decision making, and their applications. He has published 7 articles in reputed journals.
Tahir Mahmood
Tahir Mahmood is Assistant Professor of Mathematics at Department of Mathematics and Statistics, International Islamic University Islamabad, Pakistan. He received his Ph.D. degree in Mathematics from Quaid-i-Azam University Islamabad, Pakistan in 2012 under the supervision of Professor Dr. Muhammad Shabir. His areas of interest are Algebraic structures, Fuzzy Algebraic structures, and Soft sets. He has more than 180 international publications to his credit and he has also produced more than 46 MS students and 6 Ph.D. students.
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