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Research Article

An algebraic approach to the variants of convexity for soft expert approximate function with intuitionistic fuzzy setting

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Article: 2182144 | Received 01 Jun 2022, Accepted 09 Feb 2023, Published online: 27 Feb 2023

Abstract

A new area of research called intuitionistic fuzzy soft expert set is expected to overcome the drawbacks of an intuitionistic fuzzy soft set in terms of eligibility for soft expert-argument approximate function. This type of function views the power set of the universe as its co-domain and the cartesian product of attributes, experts, and their opinions as its domain. The domain of this function is larger as compared to the domain of a soft approximation function. It can manage a situation in which several expert opinions are taken into account by a single model. For the soft expert-argument approximate function with intuitionistic fuzzy setting, concepts such as set inclusion, (α,v)-convexity(concave) sets, strongly (α,v)-convexity (concave) sets, strictly (α,v)-convexity (concave) sets, convex hull, and convex cone are conceived in this paper. Some set-theoretic inequalities are established with generalized properties and results on the basis of these specified notions. Additionally, by using a theoretic cum analytical approach, various elements of computational geometry, such as convex hull and convex cone, are theorized and some pertinent results are generalized.

1. Introduction

In 1999, Molodtsov [Citation1] used the idea of soft approximate function for constructing a novel model soft set in order to give fuzzy set like models [Citation2,Citation3] the parameterizations tool for dealing with uncertain data. This set makes use of the soft approximate function, which converts a single set of parameters into the starting universe's power set. There have been many discussions about the fundamentals of soft sets, but the contributions of Ali et al. [Citation4], Babitha and Sunil [Citation5,Citation6], Ge and Yang [Citation7], Li [Citation8], Maji et al. [Citation9], Pei and Miao [Citation10], and Sezgin and Atagun [Citation11] are thought to be particularly significant for characterizing the fundamental properties and set-theoretic operations of soft sets. Abbas et al. [Citation12] examined the idea of soft points and addressed its drawbacks, similarities, and difficulties. Zadeh [Citation13] introduced the concept of fuzzy set as a generalization of crisp set. In fuzzy set, each element has a membership degree. Xia [Citation14] applied fuzzy in multi-criteria decision-making problems and named as EFMCDM-method. He applied this technique to deal with the problem that uncertainty is inevitably present in the MCDM process owing to human subjectivity. Intuitionistic fuzzy sets have a stronger capacity to represent and address the ambiguity of information than previous generations of fuzzy sets. Despite the development of numerous measuring techniques, there are still certain issues with the poor axioms of distance measurement or that lack discernment and lead to counterintuitive circumstances. To solve the aforementioned problems, Xiao [Citation15] suggested a brand-new intuitionistic distance metric based on the Jensen-Shannon divergence. The Interval-Valued Intuitionistic Fuzzy Set has drawn a lot of interest because it is a powerful tool for modelling uncertainty. Using this structure, Wang et al. [Citation16] introduced the interval-valued intuitionistic fuzzy Jenson-Shannon (IVIFJS) divergence, a new distance of interval-valued intuitionistic fuzzy set that can assess the similarity or difference between inter-valued intuitionistic fuzzy sets. The Pythagorean fuzzy set is a useful tool for addressing uncertainty in real-world applications, but it is still unclear how to gauge its level of uncertainty. By considering Pythagorean fuzziness entropy in terms of membership and non-membership degrees as well as Pythagorean hesitation entropy in terms of hesitation degree, Wang et al. [Citation17] developed a novel entropy measure of Pythagorean fuzzy set. To address uncertainties with parameterizations tools, Maji et al. [Citation18] advanced bonding impression of fuzzy soft set and debated its crucial things and outcomes. Numerous researchers [Citation19,Citation20] discussed the properties of the soft set, such as subset, absolute set, not set, etc., AND, OR, etc., and applied them to real-world issues. Rahman et al. [Citation21] studies (m, n)convexity(concavity) using the structure of fuzzy soft set and discussed its certain properties. Many intriguing applications of soft set theory have been extended by certain researchers utilizing this idea of fuzzy soft sets. Fuzzy soft sets have some applications, according to Roy and Maji [Citation22]. On the basis of the theory of soft sets, Som [Citation23] defined soft relation and fuzzy soft relation. Working on intuitionistic fuzzy soft relations were Mukherjee and Chakraborty [Citation24]. Soft sets and the related ideas of fuzzy sets and rough sets were compared by Aktas and Çaǧman [Citation25]. Operations on fuzzy soft sets were established by Yang et al. [Citation26] and are based on the three fuzzy logic operators negation, triangular norm, and triangular co-norm. The soft set and fuzzy soft set were introduced into the incomplete environment by Zou and Xiao [Citation27]. For their combined forecasting strategy based on fuzzy soft sets, Xiao et al. [Citation28] employed predicting accuracy as the criterion of fuzzy membership function. The combination of an interval-valued fuzzy set and a soft set was introduced by Yang et al. [Citation29]. In fuzzy soft-sets, Kong et al. [Citation30] developed the typical parameter reduction and demonstrated that Roy and Maji's [Citation22] technique is not practical in most situations. Çaǧman and Kartaş[Citation31] introduced the concept of intuitionistic fuzzy soft set and successfully applied it in decision-making problems. Alkhazaleh et al. [Citation32] advanced the structure of soft expert set, a mingling of soft set and expert set. They categorized its fundamentals results and strongly put in decision-making problems. Alkhazaleh et al. [Citation33] instigated the model of fuzzy soft expert set by making a nice extension in soft expert set using fuzzy environment. They developed its operations and used in decision-making problems. He also introduced the concept of mappings on fuzzy soft expert set and described its properties. Broumi and Samarandache [Citation34] introduced the concept of intuitionistic fuzzy soft expert set and applied in multi-criteria decision-making problems by giving examples. They [Citation35] also introduced mappings on this structure and discussed certain theorems with examples. Smarandache [Citation36] made extension in soft set by creating a new structure called hypersoft set. He made this work by dividing the parameters into sub-parameters.

1.1. Research gap, motivation and novelty

Convexity is very convenient in unalike arenas like optimization, recognition and classification of certain patterns, dualism difficulties and numerous extra-linked issues in operation research. The soft expert set is the mingling of soft and expert set. It is more flexible as it manages the restrictions of soft set for the respect of adept's thoughts. In order to make the existing convexity-like literature adequate with that situation, it is a literary necessity to get hold of a basic context for resolving such sort of issues under more flexible setting, i.e. soft expert set. convexity is very useful to solve and understand the optimization problems. Classically, the main focus of linear programming was in optimization area. At the beginning, it was considered that problems were majorly classified into two categories linear and non-linear optimized problems. Later on researchers found that the right division was between convex and non-convex problems because the some non-linear problems were found difficult to the others. But the use of convexity in uncertain environment was a big challenge. Various convex fuzzy and concave fuzzy set definitions exist, however, Zadeh [Citation13] and Chaudhuri [Citation37] are credited with introducing the first convex fuzzy set definition. Concavo-convex fuzzy sets were suggested by Sarkar [Citation38] after Zadeh [Citation13], with some features. Additionally, research on convex (concave) fuzzy sets has advanced quickly in both theory and application. A few examples include [Citation39,Citation40]. First, Deli [Citation41] introduced the idea of convexity on soft set as well as fuzzy soft set. Deli proved some important results by using operations like union, intersection and compliment. He proved the following important results: (i)Intersection of convex soft sets is convex set but union is not necessarily a convex soft set. (ii) Union of concave soft sets is concave soft sets but intersection is not necessarily. (iii) The compliment of convex soft set is concave soft set and vice versa. (iv) Intersection of two strictly convex soft sets is convex but union is not necessarily. The same results have been proved for convex(concave) fuzzy soft sets. Later on, Majeed [Citation42] instigated the geometrical futures like convex hull and cone s-set environment showing the use of convexity in geometry. Salleh and Sabir [Citation43] talked about the certain features of convexity(concavity) on soft sets. Rahman et al. [Citation44,Citation45] introduced different structures of convexity(concavity) on soft and fuzzy soft sets. Ihsan et al. [Citation46,Citation47] conceptualized convexity on soft and fuzzy soft expert sets with certain properties. The research that is currently available on soft inclusions and inequalities for soft and fuzzy soft sets is only appropriate for managing soft expert argument approximate functions with intuitionistic fuzzy settings. In other words, it may be said that the literature now available on soft and fuzzy soft sets is unable to offer a mathematical model that might address all of the aforementioned real-world circumstances at once:

  1. When evaluating alternatives with an unclear nature (entities in a universal set), fuzzy membership grades must be applied to each entity that corresponds to each parameter.

  2. The situation where we can know the opinions of different experts in a single model without using any additional operations like union and intersection etc.

  3. When data is of two-dimensional type, i.e. membership and non-memberships values (intuitionistic fuzzy).

Therefore, motivated by the aforementioned gap in the literature, this study aims to create a new structure intuitionistic fuzzy expert sets for convexity(concavity) that is more adaptable than existing models because it can handle their constraints and is useful for making accurate and unbiased decisions because it places a strong emphasis on parameters as well as experts and their multi-decisive opinions. Consequently, an algebraic procedure is adapted to progress a core support of (α,v)-convexity(concavity), (α,v)-convexity(concavity) in 1st-sense and 2nd-senses, convex hull, convex cone on intuitionistic fuzzy soft expert sets (IFSEs) as well as some essential results have been proved. Numerical cases of these structures on this model are described too. The paper's primary contributions are summarized as follows:

  1. Under IFSEs's environment, the traditional concepts of (α,v)-convex (concave) set, strictly (α,v)-convex (concave) set, strongly (α,v)-convex (concave) set, convex hull, and convex cone are generalized with the right to a soft expert approximate function.

  2. Set-theoretic inequalities are developed based on these set inclusions as well as other suggested notions when the concept of set inclusion for soft expert approximate function is defined.

  3. In order to evaluate the uniqueness of the proposed study, a detailed comparison is done with relevant, already published research works.

The paper is organized as Section 3 describes the definition of (α,v)-convexity and concavity and related theorems. Section 4 describes the definition of (α,v)-convexity and concavity in 1st and 2nd-senses with related theorems. Section 5 formulates the concept of strictly convex(concavity). Convex hull and cone are added to Section 6. In Sections 7 and 8, respectively, the comparison and conclusion with regard to future guidelines have been reached.

2. Preliminaries

This part shows the basic definitions from the literature. In this part, set of parameters will be denoted by F and Z as a universe of discourse and set of experts is by Y and O will be a set of opinions, T=F×Y×O. P(Z) will be used as a power set.

Definition 2.1

[Citation13]

A set ”Fz” is named as a fuzzy set shown by Fz={(rˆ,N(rˆ))rˆZ} with N:Z[0,1] and N(rˆ) shows the membership value of rˆZ.

Definition 2.2

[Citation1]

A soft set is a pair (hM,F) where hM is defined by mapping hM:FP(Z).

Definition 2.3

[Citation18]

A pair (ΛL,) is will be a fs-set on Z, with ΛL:FP(Z) and FP(Z) is being used as a collection of fuzzy subsets of Z, F.

Definition 2.4

[Citation32]

A pair (Θ,P) is known as a soft expert set with Θ is a mapping Θ:PP(Z) and PT=F×Y×O.

Definition 2.5

[Citation34]

A pair (E,W) is called an IFSEs, with E is given by E:WIZ while IZ is being used as a collection of intuitionistic fuzzy subsets of Z and WT.

Definition 2.6

[Citation34]

For two IFSEs (J1,C) and (J2,D) on S, then (J1,C)(J2,D) with CD and J1(λ)J2(λ), for all λC.

Definition 2.7

[Citation34]

Let (G,V) be an IFSEs on ST, then its complement (G,V)c is characterized by

(G,V)c = (Gc,V) with Gc:SIZ is a function and Gc(o)=c(G(o)) = 1G(o) for each o S, here c represents a intuitionistic fuzzy complement.

Definition 2.8

[Citation34]

Let (J1,C) and (J2,D) be two IFSESs, then these are equal if (J1,C)(J2,D) and (J2,D)(J1,C).

Definition 2.9

[Citation34]

The union between IFSESs (η1,Υ1) and (η2,Υ2) is again an IFSEs (η3,Υ3) ; Υ3Υ1Υ2, and ∀eˆΥ3, η3(eˆ)={η1(eˆ);eˆΥ1Υ2η2(eˆ);eˆΥ2Υ1s(η1(eˆ),η2(eˆ));eˆΥ1Υ2while s is a s-norm.

Definition 2.10

[Citation34]

The intersection of IFSEs (1,Υ1) and (2,Υ2) on Z is an IFSEs (3,Υ3) while Υ3Υ1Υ2, ; for all eˆΥ3, 3(eˆ)={1(eˆ);eˆΥ1Υ22(eˆ);eˆΥ2Υ1t(1(eˆ),2(eˆ));eˆΥ1Υ2where t is a t-norm.

Definition 2.11

[Citation34]

Suppose {(Hi,S):iI} be a finite set of IFSESs. Then

  1. The operation union of finite collection can be characterized as (iIHi,S)=iIHi(α),∀αZ.

  2. The operation intersection of finite collection can be characterized as (iIHi,S)=iIHi(α),∀αZ.

Definition 2.12

[Citation41]

The fuzzy soft set on F is named as a convex fuzzy soft set if hS(uϵ1+(1u)ϵ2)hS(ϵ1)hS(ϵ2) for each ϵ1,ϵ2F and uΓ=[0,1].

Definition 2.13

[Citation41]

The fuzzy soft set on F is named as a convex fuzzy soft set if hS(uϵ1+(1u)ϵ2)hS(ϵ1)hS(ϵ2) for each ϵ1,ϵ2F and uΓ.

3. Convex and concave intuitionistic fuzzy soft expert set

This part of the paper describes the definitions of convex(concave) intuitionistic fuzzy soft expert set and various results have been proved.

Deli [Citation41] is the first person who introduced the concept of convexity(concavity) in s-set and f-set environment. But this work is not suitable when some experts opinions is required. To deal with situation, Ihsan et al. [Citation46] made extension in the work of Deli by introducing convexity in se-set. Then Ihsan et al. [Citation47] put forward this idea in fuzzy soft expert environment by conceptualizing convexity in fse-set. Fuzz soft set is useful for handling membership values but not for non-membership environment. In order to handle these limitations, idea of convexity(concavity) is extended to intuitionistic fuzzy soft environment under multi-decisive opinion. Some dominant and valuable characteristics have been extended.

Definition 3.1

The IFSEs on S is called a convex IFSEs if qifses(αϵ1+(1α)ϵ2)qifses(ϵ1)qifses(ϵ2) for each ϵ1,ϵ2S and αΓ.

Example 3.1

Assume that an organization delivers new sorts of items and needs to take the assessment of certain specialists about these items. Suppose Z={w1,w2,w3,w4} is used as s set of products, E={b1,b2,b3}={1,2,3} represents a collection of decision parameters where bi(i=1,2,3) are the parameters; easy to handle, quality and moderate. Let X={f,g,h}={1,2,3} represents an experts set. Suppose that 1=(b1,f,1)={w1<0.5,0.5>,w2<0.2,0.6>,w3<0.4,0.6>,w4<0.1,0.8>},2=(b1,g,1)={w1<0.4,0.5>,w2<0.2,0.1>,w3<0.4,0.5>,w4<0.2,0.6>}3=(b1,h,1)={w1<0.7,0.4>,w2<0.5,0.6>,w3<0.6,0.2>,w4<0.3,0.5>},4=(b2,f,1)={w1<0.9,0.1>,w2<0.4,0.3>,w3<0.7,0.2>,w4<0.3,0.2>},5=(b2,g,1)={w1<0.4,0.2>,w2<0.8,0.2>,w3<0.3,0.4>,w4<0.2,0.3>},6=(b2,h,1)={w1<0.5,0.3>,w2<0.3,0.4>,w3<0.2,0.1>,w4<0.8,0.1>},7=(b3,f,1)={w1<0.2,0.4>,w2<0.1,0.9>,w3<0.4,0.2>,w4<0.5,0.3>},8=(b3,g,1)={w1<0.4,0.2>,w2<0.6,0.3>,w3<0.2,0.7>,w4<0.1,0.9>},9=(b3,h,1)={w1<0.2,0.7>,w2<0.2,0.3>,w3<0.3,0.5>,w4<0.1,0.2>}, 10=(b1,f,0)={w1<0.5,0.5>,w2<0.3,0.7>,w3<0.4,0.6>,w4<0.2,0.8>},11=(b1,g,0)={w1<0.1,0.2>,w2<0.1,0.9>,w3<0.3,0.6>,w4<0.5,0.2>},12=(b1,h,0)={w1<0.1,0.2>,w2<0.6,0.1>,w3<0.2,0.3>,w4<0.3,0.5>},13=(b2,f,0)={w1<0.2,0.8>,w2<0.1,0.3>,w3<0.3,0.5>,w4<0.2,0.7>},14=(b2,g,0)={w1<0.2,0.7>,w2<0.2,0.5>,w3<0.2,0.7>,w4<0.3,0.4>},15=(b2,h,0)={w1<0.3,0.6>,w2<0.2,0.7>,w3<0.2,0.3>,w4<0.1,0.2>},16=(b3,f,0)={w1<0.1,0.4>,w2<0.2,0.4>,w3<0.2,0.7>,w4<0.2,0.8>},17=(b3,g,0)={w1<0.1,0.2>,w2<0.1,0.6>,w3<0.2,0.8>,w4<0.2,0.3>},18=(b3,h,0)={w1<0.3,0.5>,w2<0.2,0.3>,w3<0.4,0.6>,w4<0.1,0.4>},The IFSEs can be described as (H,S)={1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18}.

Example 3.2

Using Example (3.1), consider C={(b1,f,1),(b2,f,0),(b3,f,1),(b1,g,1),(b2,g,1),(b3,g,0),(b1,h,0),(b2,h,1),(b3,h,1)} and D={(b1,f,1),(b2,f,0),(b3,f,1),(b1,g,1),(b2,g,1),(b3,g,1),(b1,h,0),(b2,h,1)}. Let (J1,C) and (J2,D) be two IFSESs such that (J1,C)={1=(b1,f,1)={w1<0.5,0.2>,w2<0.7,0.3>,w3<0.4,0.5>,w4<0.1,0.4>},2=(b1,g,1)={w1<0.4,0.5>,w2<0.2,0.8>,w3<0.4,0.6>,w4<0.2,0.3>},5=(b2,g,1)={w1<0.4,0.6>,w2<0.2,0.3>,w3<0.3,0.4>,w4<0.2,0.3>},6=(b2,h,1)={w1<0.5,0.4>,w2<0.3,0.4>,w3<0.6,0.4>,w4<0.8,0.2>},}. (J2,D)={1=(b1,f,1)={w1<0.1,0.3>,w2<0.2,0.3>,w3<0.4,0.5>,w4<0.2,0.4>},2=(b1,g,1)={w1<0.5,0.4>,w2<0.2,0.3>,w3<0.2,0.1>,w4<0.3,0.5>},5=(b2,g,1)={w1<0.6,0.3>,w2<0.3,0.4>,w3<0.2,0.7>,w4<0.4,0.5>},6=(b2,h,1)={w1<0.3,0.5>,w2<0.4,0.6>,w3<0.7,0.3>,w4<0.9,0.1>},}.Using union operation(max), we get (H,L)={1=(b1,f,1)={w1<0.5,0.3>,w2<0.7,0.3>,w3<0.4,0.5>,w4<0.2,0.4>},2=(b1,g,1)={w1<0.5,0.5>,w2<0.2,0.8>,w3<0.4,0.6>,w4<0.3,0.5>},5=(b2,g,1)={w1<0.6,0.3>,w2<0.3,0.4>,w3<0.2,0.4>,w4<0.4,0.5>},6=(b2,h,1)={w1<0.5,0.4>,w2<0.4,0.4>,w3<0.7,0.3>,w4<0.9,0.1>},}.

Example 3.3

Referring to Example (3.1), let C={(b1,f,1),(b2,f,0),(b3,f,1),(b1,g,1),(b2,g,1),(b3,g,0),(b1,h,0),(b2,h,1),(b3,h,1)} and D={(b1,f,1),(b2,f,0),(b3,f,1),(b1,g,1),(b2,g,1),(b3,g,1),(b1,h,0),(b2,h,1)}.

Considering the above two IFSESs in (3.2) and using intersection operation(min), we get (M,L)={1=(b1,f,1)={w1<0.1,0.3>,w2<0.2,0.3>,w3<0.4,0.5>,w4<0.1,0.4>},2=(b1,g,1)={w1<0.4,0.4>,w2<0.2,0.8>,w3<0.2,0.6>,w4<0.2,0.5>},5=(b2,g,1)={w1<0.4,0.6>,w2<0.2,0.4>,w3<0.2,0.7>,w4<0.2,0.5>},6=(b2,h,1)={w1<0.3,0.4>,w2<0.3,0.6>,w3<0.6,0.4>,w4<0.8,0.2>},}.

Example 3.4

Considering Example (3.1) with E={b1,b2,b3}={1,2,3}, and X={f,g,h}={1,2,3}, 1=qifses(1,1,1)={w1<0.5,0.5>,w2<0.2,0.6>,w3<0.4,0.6>,w4<0.1,0.8>},2=qifses(1,2,1)={w1<0.4,0.5>,w2<0.2,0.1>,w3<0.4,0.5>,w4<0.2,0.6>}

Take α = 0.6, and ϵ1=(1,1,1), ϵ2=(1,2,1) then qifses(αϵ1+(1α)ϵ2=qifses(0.6(1,1,1)+(10.6)(1,2,1))=qifses((0.6,0.6,0.6)+(0.4,0.8,0.4))=qifses(1,1.4,1).By applying the decimal round off property, we get {1,1.4,1}={1,1,1} qifses(1,1,1)={w1<0.5,0.5>,w2<0.2,0.6>,w3<0.4,0.6>,w4<0.1,0.8>} and qifses(ϵ1)qifses(ϵ2)=qifses(1,1,1)qifses(1,2,1={w1<0.5,0.5>,w2<0.2,0.6>,w3<0.4,0.6>,w4<0.1,0.8>} {w1<0.4,0.5>,w2<0.2,0.1>,w3<0.4,0.5>,w4<0.2,0.6>}={w1<0.4,0.5>,w2<0.2,0.6>,w3<0.4,0.5>,w4<0.1,0.8>}.

It is clear that qifses(αϵ1+(1α)ϵ2)qifses(ϵ1)qifses(ϵ2).

Ihsan et al. [Citation47] have defined concavity in fuzzy soft expert environment. Now following definition is the extension of fuzzy soft expert set.

Definition 3.2

The IFSEs on S is called concave IFSEs if qifses(αϵ1+(1α)ϵ2)qifses(ϵ1)qifses(ϵ2)for each ϵ1,ϵ2S and αΥ.

Example 3.5

Considering Example (3.1) with E={b1,b2,b3}={1,2,3}, and X={f,g,h}={1,2,3}, 6=qifses(1,1,1)={w1<0.5,0.3>,w2<0.3,0.4>,w3<0.2,0.1>,w4<0.8,0.1>}, 7=qifses(1,2,1)={w1<0.2,0.4>,w2<0.1,0.9>,w3<0.4,0.2>,w4<0.5,0.3>}.

Take α = 0.6, and ϵ1=(1,1,1), ϵ2=(1,2,1) then qifses(αϵ1+(1α)ϵ2)=qifses(0.6(1,1,1)+(10.6)(1,2,1))=qifses((0.6,0.6,0.6)+(0.4,0.8,0.4))=qifses(1,1.4,1).By applying the decimal round off property, we get {1,1.4,1}={1,1,1} qifses(1,1,1)={w1<0.5,0.3>,w2<0.3,0.4>,w3<0.2,0.1>,w4<0.8,0.1>} and qifses(ϵ1)qifses(ϵ2) = qifses(1,1,1)qifses(1,2,1)={w1<0.5,0.3>,w2<0.3,0.4>,w3<0.2,0.1>,w4<0.8,0.1>} {w1<0.2,0.4>,w2<0.1,0.9>,w3<0.4,0.2>,w4<0.5,0.3>={w1<0.5,0.3>,w2<0.3,0.4>,w3<0.4,0.1>,w4<0.8,0.1>}.

It is clear that qIFSEs(αϵ1+(1α)ϵ2)qIFSEs(ϵ1)qIFSEs(ϵ2).

Set-inclusion has already been introduced in Ihsan et al. [Citation47] for fuzzy environment. Now this work is for its extension.

Definition 3.3

Suppose W be an IFSEs on Z and ρZ. Then ρ-inclusion of W is characterized as Wρ={ϵF:gW(ϵ)ρ}.

Theorem 3.1

P1P2 is a (α,v)-convex IFSESs while P1 and P2 are (α,v)-convex IFSESs.

Proof.

Assume ∃ϵ1,ϵ2S and α(0,1], and P3=P1P2. qifses(P3)(αϵ1+v(1α)ϵ2)=qifses(P1)(αϵ1+v(1α)ϵ2)qifses(P2)(αϵ1+v(1α)ϵ2)P1 and P2 are (α,v)-convex IFSESs, so (1) qifses(P1)(αϵ1+v(1α)ϵ2)hifses(P1)(ϵ1)qifses(P1)(ϵ2)(1) (2) qifses(P2)(αϵ1+v(1α)ϵ2)qifses(P2)(ϵ1)qifses(P2)(ϵ2)(2) By applying the intersection operation, we have qifses(P3)(αϵ1+v(1α)ϵ2){(qifses(P1)(ϵ1)qifses(P1)(ϵ2))(qifses(P2)(ϵ1)qifses(P2)(ϵ2))}qifses(P3)(αϵ1+v(1α)ϵ2)qifses(P3)(ϵ1)qifses(P3)(ϵ2hence the result is proved.

Remark 3.1

If {Pk:k{1,2,3,}} is any collection of (α,v)-convex IFSESs, then the kIPk is a (α,v)-convex IFSEs.

Theorem 3.2

Y is a (α,v)-convex IFSEs on S iff for each α(0,1] and ρP(Z), Yρ is (α,v)-convex IFSEs on S.

Proof.

Suppose that Y is (α,v)-convex IFSEs. If ϵ1,ϵ2S and ρP(Z), then qifses(Y)(ϵ1)ρ and qifses(Y)(ϵ2)ρ.

By the convexity of Y, we have qifses(Y)(αϵ1+v(1α)ϵ2)qifses(Y)(ϵ1)qifses(Y)(ϵ2)so Yρ is (α,v)-convex IFSEs S.

Conversely, suppose that Yρ is (α,v)-convex IFSEs for each α(0,1].

Then, for ϵ1,ϵ2S, Yρ is (α,v)-convex IFSEs for ρ =qifses(Y)(ϵ1)qifses(Y)(ϵ2).

qifses(Y)(ϵ1)ρ and qifses(Y)(ϵ2)ρ, we have ϵ1Yρ and ϵ2Yρ, hence αϵ1+v(1α)ϵ2Yρ.

qifses(Y)(αϵ1+v(1α)ϵ2)qifses(Y)(ϵ1)qifses(Y)(ϵ2),

Y is (α,v)-convex IFSEs.

Theorem 3.3

L is (α,v)-concave IFSEs while L is (α,v)-convex IFSEs.

Proof.

Assume that ∃ϵ1,ϵ2S,α(0,1].

L is (α,v)-convex IFSEs, qifses(L)(αϵ1+v(1α)ϵ2)qfses(L)(ϵ1)qifses(L)(ϵ2)or Zqifses(L)(αϵ1+v(1α)ϵ2)Z{qifses(L)(ϵ1)qifses(L)(ϵ2)}If qifses(L)(ϵ1)qifses(L)(ϵ2), then we may write (3) Zqifses(L)(αϵ1+v(1α)ϵ2)Zqifses(L)(ϵ2)(3) If qifses(L)(ϵ1)qifses(L)(ϵ2), then we may write (4) Zqifses(L)(αϵ1+v(1α)ϵ2)Zqifses(L)(ϵ1)(4) From Equations (Equation1) and (Equation2), we have (5) Zqifses(L)(αϵ1+v(1α)ϵ2){Zqifses(L)(ϵ1)Zqifses(L)(ϵ2)}.(5) So, L is (α,v)-concave IFSEs.

Theorem 3.4

P is (α,v)-convex IFSEs while P is (α,v)-concave IFSEs.

Proof.

Assume that ∃ϵ1,ϵ2S,α(0,1].

P is (α,v)-concave IFSEs, qifses(P)(αϵ1+v(1α)ϵ2)qifses(P)(ϵ1)qifses(P)(ϵ2)or Zqifses(P)(αϵ1+v(1αϵ)2)Z{qifses(P)(ϵ1)qifses(P)(ϵ2)}If qifses(P)(ϵ1)qifses(P)(ϵ2) then we may write (6) Zqifses(P)(αϵ1+(1α)ϵ2)Zqifses(P)(ϵ1)(6) If qifses(P)(ϵ1)qifses(P)(ϵ2) (7) Zqifses(P)(αϵ1+v(1α)ϵ2)Zqifses(P)(ϵ2)(7) from (Equation4) and (Equation5), we have (8) Zqifses(P)(αϵ1+v(1α)ϵ2){Zqifses(P)(ϵ1)Zqifses(P)(ϵ2)}(8) which shows that P is (α,v)-convex IFSEs.

Theorem 3.5

M is (α,v)-concave IFSEs on S iff for each α(0,1] and ρP(Z), Mρ is (α,v)-concave IFSEs on S.

Proof.

Suppose that M is (α,v)-concave IFSEs. If ϵ1,ϵ2S and ρP(Z), then qifses(M)(ϵ1)ρ and qifses(M)(ϵ2)ρ.

By the concavity of M, we have qifses(M)(αϵ1+v(1α)ϵ2)qifses(M)(ϵ1)qifses(M)(ϵ2)hence Mρ is a (α,v)-concave IFSEs.

Conversely suppose that Mρ is (α,v)-concave IFSEs for each α(0,1].

Subsequently, for ϵ1ϵ2S, Mρ is (α,v)-concave IFSEs for ρ =qifses(M)(ϵ1)qifses(M)(ϵ2).

qifses(M)(ϵ1)ρ and qifses(M)(ϵ2)ρ,

we have ϵ1Mρ and ϵ2Mρ,

hence αϵ1+v(1α)ϵ2Mρ.

,qifses(M)(αϵ1+v(1α)ϵ2)qifses(M)(ϵ1)qifses(M)(ϵ2),

it is clear that M is (α,v)-concave IFSEs.

Theorem 3.6

P1P2 is a (α,v)-concave IFSEs while P1 and P2 are (α,v)-concave IFSESs.

Proof.

Assume ∃ϵ1,ϵ2S and α(0,1], and P3=P1P2. qifses(P3)(αϵ1+v(1α)ϵ2)=qifses(P1)(αϵ1+v(1α)ϵ2)qifses(P2)(αϵ1+v(1α)ϵ2)P1 and P2 are (α,v)-concave ifse-sets, so (9) qifses(P1)(αϵ1+v(1α)ϵ2)qifses(P1)(ϵ1)qifses(P1)(ϵ2)(9) (10) qifses(P2)(αϵ1+v(1α)ϵ2)qifses(P2)(ϵ1)qifses(P2)(ϵ2)(10) By applying union operation, we have qifses(P3)(αϵ1+v(1α)ϵ2) {(qifses(P1)(ϵ1)qifses(P1)(ϵ2))(qifses(P2)(ϵ1)qifses(P2)(ϵ2))}.qifses(P3)(αϵ1+v(1α)ϵ2)qifses(P3)(ϵ1)qifses(P3)(ϵ2).Here we have proved the union of two concave IFSESs, it can be proved for more than two concave ifse-sets. Hence we can generalized this result up to any countable number of concave IFSESs.

4. (α,v)-Convex and (α,v)-Concave intuitionistic fuzzy soft expert sets in 1st and 2st-Sense

This portion will present the concept of (α,v)-convex and (α,v)-concave IFSESs in 1nd and 2nd-sense with some proved results.

Rahman et al. [Citation44] have discussed (m; n) convexity with 1st sense for a fuzzy environment and now an extension is made by introducing this work in an intuitionistic fuzzy environment.

Definition 4.1

An IFSEs is named as a (α,v)-convex IFSEs in 1st-sense if qifses(αϵ1+v(1αδ)ϵ2)qfses(ϵ1)qifses(ϵ1)for α,δ(0,1], v[0,1] and ϵ1,ϵ2S.

Example 4.1

Referring to Example (3.1) with E={b1,b2,b3}={1,2,3}, and X={f,g,h}={1,2,3},1=qifses(1,1,1)={w1<0.5,0.5>,w2<0.2,0.6>,w3<0.4,0.6>,w4<0.1,0.8>}, 2=qifses(1,2,1)={w1<0.4,0.5>,w2<0.2,0.1>,w3<0.4,0.5>,w4<0.2,0.6>}

Take α = 0.6, v = 1, δ = 0.5 and ϵ1=(1,1,1), ϵ2=(1,2,1) then qifses(αϵ1+1(1αδ)ϵ2)=qifses(0.6(1,1,1)+1(10.60.5)(1,2,1)=qifses((0.6,0.6,0.6)+0.22 (1,2,1))=qifses((0.6,0.6,0.6)+(0.22,0.44,0.22))=qifses(0.28,0.50,0.28). By applying the decimal round off property, we get {0.28,0.50,0.28}={0,1,0} and also (0,1,0)=(b1,f,0) qifses(0,1,0)={w1<0.5,0.5>,w2<0.3,0.7>,w3<0.4,0.6>,w4<0.2,0.8>} and qifses(ϵ1)qifses(ϵ2)=qifses(1,1,1)qifses(1,2,1)={w1<0.5,0.5>,w2<0.2,0.6>,w3<0.4,0.6>,w4<0.1,0.8>} {w1<0.4,0.5>,w2<0.2,0.1>,w3<0.4,0.5>,w4<0.2,0.6>}={w10.4,0.5,w2<0.2,0.6>,w3<0.4,0.6>,w4<0.1,0.8>}.

It is clear that qifses(αϵ1+(1α)ϵ2)qifses(ϵ1)qifses(ϵ2).

Rahman et al. [Citation44] have discussed (m; n) concavity with 1st sense for fuzzy environment and now extension is made by introducing this work in intuitionistic fuzzy environment.

Definition 4.2

An IFSEs is named as a (α,v)-concave IFSEs in 1st-sense if qifses(αϵ1+v(1αδ)ϵ2)qifses(ϵ1)qifses(ϵ2)for α,δ(0,1], v[0,1] and ϵ1,ϵ2S.

Rahman et al. [Citation44] have discussed (m; n) convexity with 2st sense for fuzzy environment and now extension is made by introducing this work in intuitionistic fuzzy environment.

Definition 4.3

An IFSEs is named as a (α,v)-convex IFSEs in 2nd-sense if qifses(αϵ1+v(1α)δϵ2)qifses(ϵ1)qfses(ϵ2)for α,δ(0,1], v[0,1] and ϵ1,ϵ2S.

Example 4.2

Referring to Example (3.1), Referring to Example (3.1) with E={b1,b2,b3}={1,2,3}, and X={f,g,h}={1,2,3}, 1=qifses(1,1,1)={w1<0.5,0.5>,w2<0.2,0.6>,w3<0.4,0.6>,w4<0.1,0.8>}, 2=qifses(1,2,1)={w1<0.4,0.5>,w2<0.2,0.1>,w3<0.4,0.5>,w4<0.2,0.6>}.

Take α = 0.6, v = 1, δ = 0.5, and ϵ1=(1,1,1), ϵ2=(1,2,1) then qifses(αϵ1+1(1α)δϵ2) = qifses(0.6(1,1,1)+1(10.6)0.5(1,2,1))=qifses((0.6,0.6,0.6)+0.63(1,2,1)) = qifses((0.6,0.6,0.6)+(0.63,1.26,0.63)) = qfses(0.69,1.32,0.69).

By applying the decimal round off property, we get {0.69,1.32,0.69}={1,1,1} and also (1,1,1)=(b1,f,1) qifses(1,1,1)={w1<0.5,0.5>,w2<0.2,0.6>,w3<0.4,0.6>,w4<0.1,0.8>} and qifses(ϵ1)qifses(ϵ2) = qifses(1,1,1)qifses(1,2,1)={w1<0.5,0.5>,w2<0.2,0.6>,w3<0.4,0.6>,w4<0.1,0.8>} {w1<0.4,0.5>,w2<0.2,0.1>,w3<0.4,0.5>,w4<0.2,0.6>}={w1<0.4,0.5>,w2<0.2,0.6>,w3<0.4,0.6,w4<0.1,0.8>}.

It is clear that qifses(αϵ1+(1α)ϵ2)qifses(ϵ1)qifses(ϵ2).

Rahman et al. [Citation44] have discussed (m; n) concavity with 1st sense for fuzzy environment and now extension is made by introducing this work in intuitionistic fuzzy environment.

Definition 4.4

An IFSEs is named as a (α,v)-concave IFSEs in 2nd-sense if qifses(αϵ1+v(1α)δϵ2)qifses(ϵ1)qifses(ϵ1)for α,δ(0,1], v[0,1] and ϵ1,ϵ2S.

Theorem 4.1

P1P2 is a (α,v)-convex IFSEs in 1st-sense while P1 and P2 are (α,v)-convex IFSESs in 1st-sense.

Proof.

Assume ∃ϵ1,ϵ2S and α(0,1], and G3=P1P2. qifses(P3)(αϵ1+v(1αδ)ϵ2)=qifses(P1)(αϵ1+v(1αδ)ϵ2)qifses(P2)(αϵ1+v(1αδ)ϵ2)P1 and P2 are (α,v)- convex IFSESs in 1st-sense, so (11) qifses(P1)(αϵ1+v(1αδ)ϵ2)qifses(P1)(ϵ1)qifses(P1)(ϵ1)(11) (12) qifses(P2)(αϵ1+v(1αδ)ϵ2)qifses(P2)(ϵ1)qifses(P2)(ϵ1)(12) By applying the intersection operation, we get (13) qifses(P3)(αϵ1+v(1αδ)ϵ2){(qifses(P1)(ϵ1)qifses(P1)(ϵ1))(qifses(P2)(ϵ1)qifses(P2)(ϵ1))}(13) qifses(P3)(αϵ1+v(1αδ)ϵ2)qfses(P3)(ϵ1)qifses(P3)(ϵ1)

hence the result is proved.

Remark 4.1

If {Pk:k{1,2,3,}} is any collection of (α,v)-convex IFSESs in 1st-sense, then the kIPk is a (α,v)-convex IFSEs in 1st-sense.

Theorem 4.2

P1P2 is a (α,v)-convex IFSEs in 2st-sense while P1 and P2 are (α,v)-convex IFSESs in 2st-sense.

Proof.

Assume ∃ϵ1,ϵ2S and α(0,1], and P3=P1P2. qifses(P3)(αϵ1+v(1α)δϵ2)=qIFSEs(P1)(αϵ1+v(1α)δϵ2)qIFSEs(P2)(αϵ1+v(1α)δϵ2)P1 and P2 are (α,v)-convex IFSESs in 2st-sense, so (14) qIFSEs(P1)(αϵ1+v(1α)δϵ2)qIFSEs(P1)(ϵ1)qIFSEs(P1)(ϵ1)(14) (15) qIFSEs(P2)(αϵ1+v(1α)δϵ2)qIFSEs(P2)(ϵ1)qIFSEs(P2)(ϵ1)(15) By applying the union operation, we get (16) qIFSEs(P3)(αϵ1+v(1α)δϵ2){(qIFSEs(P1)(ϵ1)qfses(P1)(ϵ2))(qIFSEs(P2)(ϵ1)qIFSEs(P2)(ϵ2))}qIFSEs(P3)(αϵ1+v(1α)δϵ2)qIFSEs(P3)(ϵ1)qIFSEs(P3)(ϵ1)(16) hence the result is proved.

Remark 4.2

If {Pk:k{1,2,3,}} is any collection of (α,v)-convex IFSESs in 2st-sense sense, then the kIPk is a (α,v)-convex IFSEs in 2st-sense sense.

Theorem 4.3

L is a (α,v)-convex IFSEs in 1st-sense on S iff for each α(0,1] and ρP(Z), Lρ is (α,v)-convex IFSEs in 1st-sense on S.

Proof.

Suppose that L is (α,v)-convex IFSEs in 1st-sense. If ϵ1,ϵ2S and ρP(Z), then qfses(L)(ϵ1)ρ and qfses(L)(ϵ2)ρ.

By the convexity of D, we have qIFSEs(L)(αϵ1+v(1αδ)ϵ2)qIFSEs(L)(ϵ1)qIFSEs(L)(ϵ2)so Lρ is (α,v)-convex IFSEs in 1st-sense.

Conversely, suppose that Lρ is (α,v)-convex IFSEs in 1st-sense for each α(0,1].

Then, for ϵ1,ϵ2S, Lρ is (α,v)-convex IFSEs in 1st-sense for ρ =qIFSEs(L)(ϵ1)qIFSEs(L)(ϵ2).

qIFSEs(L)(ϵ1)ρ and qIFSEs(L)(ϵ2)ρ, we have ϵ1Lρ and ϵ2Lρ, hence αϵ1+v(1αδ)ϵ2Dρ.

qIFSEs(L)(αϵ1+v(1αδ)ϵ2)qIFSEs(L)(ϵ1)qIFSEs(L)(ϵ2), which implies that L is (α,v)-convex IFSEs in 1st-sense.

Theorem 4.4

L is a (α,v)-convex IFSEs in 2st-sense on S iff for each α(0,1] and ρP(Z), Lρ is (α,v)-convex IFSEs in 2st-sense on S.

Proof.

Suppose that L is (α,v)-convex IFSEs in 2st-sense. If ϵ1,ϵ2S and ρP(Z), then qIFSEs(L)(ϵ1)ρ and qIFSEs(L)(ϵ2)ρ.

By the convexity of L, we have qIFSEs(L)(αϵ1+v(1α)δϵ2)qIFSEs(L)(ϵ1)qIFSEs(L)(ϵ2)so Lρ is (α,v)-convex IFSEs in 2st-sense.

Conversely, suppose that Lρ is (α,v)-convex ifse-set in 2st-sense for each α(0,1].

Then, for ϵ1,ϵ2S, Lρ is (α,v)-convex ifse-set in2st-sense.

For ρ =qIFSEs(L)(ϵ1)qIFSEs(L)(ϵ2).

qIFSEs(L)(ϵ1)ρ and qIFSEs(L)(ϵ2)ρ, we have ϵ1Lρ and ϵ2Lρ, hence αϵ1+v(1α)δϵ2Lρ.

qIFSEs(L)(αϵ1+v(1α)δϵ2)qIFSEs(L)(ϵ1)qIFSEs(L)(ϵ2), which implies that L is (α,v)-convex IFSEs in 2st-sense.

Theorem 4.5

O is (α,v)-concave IFSEs in 1st-sense while O is (α,v)-convex IFSEs in 1st-sense.

Proof.

Assume that ∃ϵ1,ϵ2S,α(0,1].

O is (α,v)-convex IFSEs in 1st-sense, qIFSEs(O)(αϵ1+v(1αδ)ϵ2)qIFSEs(O)(ϵ1)qIFSEs(O)(ϵ2)or ZqIFSEs(O)(αϵ1+v(1αδ)ϵ2)Z{qIFSEs(O)(ϵ1)qIFSEs(O)(ϵ2)}If qIFSEs(O)(ϵ1)qIFSEs(O)(ϵ2), then we may write (17) ZqIFSEs(O)(αϵ1+v(1αδ)ϵ2)ZqIFSEs(O)(ϵ2)(17) If qIFSEs(O)(ϵ1)qIFSEs(O)(ϵ2), then we may write (18) ZqIFSEs(O)(αϵ1+v(1αδ)ϵ2)ZqIFSEs(O)(ϵ1)(18) From Equations (Equation17) and (Equation18), we have (19) ZqIFSEs(O)(αϵ1+v(1αδ)ϵ2){ZqIFSEs(O)(ϵ1)ZqIFSEs(O)(ϵ2)}.(19) So, O is (α,v)-concave IFSEs in 1st-sense.

Theorem 4.6

O is (α,v)-concave IFSEs in 2st-sense while O is (α,v)-convex IFSEs in 2st-sense.

Proof.

Assume that ∃ϵ1,ϵ2S,α(0,1].

O is (α,v)-convex IFSEs in 2st-sense, qIFSEs(O)(αϵ1+v(1α)δϵ2)qIFSEs(O)(ϵ1)qIFSEs(O)(ϵ2)or ZqIFSEs(O)(αϵ1+v(1α)δϵ2)Z{qIFSEs(O)(ϵ1)qIFSEs(O)(ϵ2)}If qIFSEs(O)(ϵ1)qIFSEs(O)(ϵ2), then we may write (20) ZqIFSEs(O)(αϵ1+v(1α)δϵ2)ZqIFSEs(O)(ϵ2)(20) If qIFSEs(O)(ϵ1)qIFSEs(O)(ϵ2), then we may write (21) ZqIFSEs(O)(αϵ1+v(1α)δϵ2)ZqIFSEs(O)(ϵ1)(21) From Equations (Equation20) and (Equation21), we have (22) ZqIFSEs(O)(αϵ1+v(1α)δϵ2){ZqIFSEs(O)(ϵ1)ZqIFSEs(O)(ϵ2)}.(22) So, O is (α,v)-concave IFSEs in 2st-sense.

Theorem 4.7

Æ is (α,v)-convex IFSEs in 1st-sense while Æ is (α,v)-concave IFSEs in 1st-sense.

Proof.

Assume that ∃ϵ1,ϵ2S,α(0,1].

Æ is (α,v)-concave IFSEs in 1st-sense, qIFSEs(Æ)(αϵ1+v(1αδ)ϵ2)qIFSEs(Æ)(ϵ1)qIFSEs(Æ)(ϵ2)or ZqIFSEs(Æ)(αϵ1+v(1αδ)ϵ2)Z{qIFSEs(Æ)(ϵ1)qIFSEs(Æ)(ϵ2)}If qIFSEs(Æ)(ϵ1)qIFSEs(Æ)(ϵ2) then we may write (23) ZqIFSEs(Æ)(αϵ1+(1αδ)ϵ2)ZqIFSEs(Æ)(ϵ1)(23) If qIFSEs(Æ)(ϵ1)qIFSEs(Æ)(ϵ2) (24) ZqIFSEs(Æ)(αϵ1+v(1αδ)ϵ2)ZqIFSEs(Æ)(ϵ2)(24) From (Equation23) and (Equation24), we have (25) ZqIFSEs(Æ)(αϵ1+v(1αδ)ϵ2){ZqIFSEs(Æ)(ϵ1)ZqIFSEs(Æ)(ϵ2)}(25) which shows that Æ is (α,v)-convex IFSEs in 1st-sense.

Theorem 4.8

Æ is (α,v)-convex IFSEs in 2st-sense while Æ is (α,v)-concave IFSEs in 2st-sense.

Proof.

Assume that ∃ϵ1,ϵ2S,α(0,1].

Æ is (α,v)-concave IFSEs in 2st-sense, qIFSEs(Æ)(αϵ1+v(1α)δϵ2)qIFSEs(Æ)(ϵ1)qIFSEs(Æ)(ϵ2)or ZqIFSEs(Æ)(αϵ1+v(1α)δϵ2)Z{qIFSEs(Æ)(ϵ1)qIFSEs(Æ)(ϵ2)}If qIFSEs(Æ)(ϵ1)qIFSEs(Æ)(ϵ2) then we may write (26) ZqIFSEs(Æ)(αϵ1+(1α)δϵ2)ZqIFSEs(Æ)(ϵ1)(26) If qIFSEs(Æ)(ϵ1)qIFSEs(Æ)(ϵ2) (27) ZqIFSEs(Æ)(αϵ1+v(1α)δϵ2)ZqIFSEs(Æ)(ϵ2)(27) From (Equation26) and (Equation27), we have (28) ZqIFSEs(Æ)(αϵ1+v(1α)δϵ2){ZqIFSEs(Æ)(ϵ1)ZqIFSEs(Æ)(ϵ2)}(28) which shows that Æ is (α,v)-convex IFSEs in 2st-sense.

Theorem 4.9

Œ is (α,v)-concave IFSEs in 1st-sense on S iff for each α(0,1] and ρP(Z), Œρ is (α,v)-concave IFSEs in 1st-sense on S.

Proof.

Suppose that Œ is (α,v)-concave IFSEs in 1st-sense. If ϵ1,ϵ2S and ρP(Z), then qIFSEs(Œ)(ϵ1)ρ and qIFSEs(Œ)(ϵ2)ρ.

By the (α,v)-concavity of Œ in 1st-sense, we have qIFSEs(Œ)(αϵ1+v(1αδ)ϵ2)qIFSEs(Œ)(ϵ1)qIFSEs(Œ)(ϵ2)hence Œρ is a (α,v)-concave IFSEs in 1st-sense.

Conversely, suppose that Œρ is (α,v)-concave IFSEs in 1st-sense for each α(0,1].

Subsequently, for ϵ1,ϵ2S, Œρ is (α,v)-concave IFSEs in 1st-sense for ρ =qIFSEs(Œ)(ϵ1)qIFSEs(Œ)(ϵ2).

qIFSEs(Œ)(ϵ1)ρ and qIFSEs(Œ)(ϵ2)ρ, we have ϵ1Œρ and ϵ2Œρ, hence αϵ1+v(1αδ)ϵ2Œρ qIFSEs(Œ)(αϵ1+v(1αδ)ϵ2)qIFSEs(Œ)(ϵ1)qIFSEs(Œ)(ϵ2), it is clear that Œ is (α,v)-concave IFSEs in 1st-sense.

Theorem 4.10

Œ is (α,v)-concave IFSEs in 2st-sense on S iff for each α(0,1] and ρP(Z),

Œρ is (α,v)-concave IFSEs in 2st-sense on S.

Proof.

Suppose that Œ is (α,v)-concave IFSEs in 2st-sense. If ϵ1,ϵ2S and ρP(Z), then qIFSEs(Œ)(ϵ1)ρ and qIFSEs(Œ)(ϵ2)ρ.

By the (α,v)-concavity of Œ in 2st-sense, we have qIFSEs(Œ)(αϵ1+v(1α)δϵ2)qIFSEs(Œ)(ϵ1)qIFSEs(Œ)(ϵ2)hence Œρ is a (α,v)-concave IFSEs in 2st-sense.

Conversely suppose that Œρ is (α,v)-concave IFSEs in 2st-sense for each α(0,1].

Subsequently, for ϵ1,ϵ2S, Œρ is (α,v)-concave IFSEs in 2st-sense for ρ =qIFSEs(Œ)(ϵ1)qIFSEs(Œ)(ϵ2).

qIFSEs(Œ)(ϵ1)ρ and qIFSEs(Œ)(ϵ2)ρ, we have ϵ1Œρ and ϵ2Œρ, hence αϵ1+v(1α)δϵ2Œρ., qIFSEs(Œ)(αϵ1+v(1α)δϵ2)qIFSEs(Œ)(ϵ1)qIFSEs(Œ)(ϵ2), it is clear that Œ is (α,v)-concave IFSEs in 2st- sense.

Theorem 4.11

P1P2 is a (α,v)-concave IFSEs in 1st-sense while P1 and P2 are (α,v)-concave IFSEss in 1st-sense.

Proof.

Assume ∃ϵ1,ϵ2S and α(0,1], and P3=P1P2. qIFSEs(P3)(αϵ1+v(1αδ)ϵ2)=qIFSEs(P1)(αϵ1+v(1αδ)ϵ2)qIFSEs(P2)(αϵ1+v(1αδ)ϵ2)P1 and P2 are (α,v)-concave IFSEss in 1st-sense, so (29) qIFSEs(P1)(αϵ1+v(1αδ)ϵ2)qIFSEs(P1)(ϵ1)qIFSEs(P1)(ϵ2)(29) (30) qIFSEs(P2)(αϵ1+v(1αδ)ϵ2)qIFSEs(P2)(ϵ1)qIFSEs(P2)(ϵ2)(30) By applying the union operation, we get (31) qIFSEs(P3)(αϵ1+v(1αδ)ϵ2){(qIFSEs(P1)(ϵ1)qIFSEs(P1)(ϵ2))(qIFSEs(P2)(ϵ1)qIFSEs(P2)(ϵ2))}(31) qIFSEs(P3)(αϵ1+v(1αδ)ϵ2)qIFSEs(P3)(ϵ1)qIFSEs(P3)(ϵ2).

Here we have proved the union of two concave IFSEss in 1st-sense, it can be proved for more than two concave IFSEss in 1st-sense. Hence we can generalized this result up to any countable number of concave IFSEss in 1st-sense.

5. Strongly and strictly convex (concave)

This section contains definitions of strongly convex IFSEs, strictly convex IFSEs, strongly concave IFSEs, strictly concave IFSEs and describes their characteristics.

Salleh and Sabir [Citation43] conceptualized some characteristics of convexity(concavity) like strictly and strongly in s-set environment. Now these properties (Definitions 5.1, 5.2, 5.3, 5.4) have been generalized for intuitionistic fuzzy environment.

Definition 5.1

The IFSEs over S are called strongly convex IFSEs if qIFSEs(αϵ1+(1α)ϵ2)qIFSEs(ϵ1)qIFSEs(ϵ2)for each ϵ1,ϵ2S, ϵ1ϵ2 and αΓ0 =(0, 1).

Definition 5.2

The IFSEs over S are called strongly concave IFSEs if qIFSEs(αϵ1+(1α)ϵ2)qIFSEs(ϵ1)qIFSEs(ϵ2)for each ϵ1,ϵ2S, ϵ1ϵ2 and αΓ0.

Definition 5.3

The IFSEs over S are called strictly convex IFSEs if qIFSEs(αϵ1+(1α)ϵ2)qIFSEs(ϵ1)qIFSEs(ϵ2)for each ϵ1,ϵ2S, qIFSEs(ϵ1)qIFSEs(ϵ2) and αΓ0.

Definition 5.4

The IFSEs on S are said to be strictly concave IFSEs if qIFSEs(αϵ1+(1α)ϵ2)qIFSEs(ϵ1)qIFSEs(ϵ2)for each ϵ1,ϵ2S, qIFSEs(ϵ1)qIFSEs(ϵ2) and αΓ0.

Theorem 5.1

Suppose (qIFSEs,S) be a strictly convex IFSEs. If ∃αΓ0ϵ1, ϵ2 S such that qIFSEs(αϵ1+(1α)ϵ2)qIFSEs(ϵ1)qIFSEs(ϵ2)then (qIFSEs,S) is a convex IFSEs.

Proof.

Assume that qIFSEs(ϵ1)qIFSEs(ϵ2) and ∃ϵ1,ϵ2S, δΓ0 (32) ZqIFSEs(T)(δϵ1+(1δ)ϵ2)Z{qIFSEs(T)(ϵ1)qIFSEs(T)(ϵ2)}(32) if qIFSEs(ϵ1)qIFSEs(ϵ2), then (30) contradicts that (qIFSEs,S) is a strictly convex IFSEs.

Now if we take qIFSEs(ϵ1)=qIFSEs(ϵ2) and δ[0,α], suppose ϵ3=δαϵ1+(1δα)ϵ2 and τ=(1δ1)(1α1)1. So qIFSEs(T)(δϵ1+(1δ)ϵ2)=qIFSEs(T)(α(δαϵ1+(1δα)ϵ2)+(1δ)ϵ2)=qIFSEs(T)(αϵ3+(1α)ϵ2){qIFSEs(T)(ϵ1)qIFSEs(T)(ϵ2)}(33) qIFSEs(T)(δϵ1+(1δ)ϵ2){qIFSEs(T)(ϵ1)qIFSEs(T)(ϵ2)}(33) now (34) qIFSEs(T)(ϵ3)=qIFSEs(T)(δαϵ1)+(1δαϵ2)=qIFSEs(T)(τϵ1+(1τ)×(δϵ1+(1δ)ϵ2))(34) From (Equation32), (Equation33) and qIFSEs(ϵ1)=qIFSEs(ϵ2), we have (35) qIFSEs(T)(δϵ1+(1δ)ϵ2)qIFSEs(ϵ3),(35) also from (Equation31), (Equation32) qIFSEs(ϵ1)=qIFSEs(ϵ2) and strictly convex IFSEs condition, we have (36) qIFSEs(T)(δϵ1+(1δ)ϵ2)qIFSEs(ϵ3),(36) or (37) Z qIFSEs(T)(δϵ1+(1δ)ϵ2)Z qIFSEs(ϵ3),(37) thus (Equation34) and (Equation36) contradict the fact.

If qIFSEs(ϵ1)=qIFSEs(ϵ2) and δ[α,1], suppose ϵ4=δα1αϵ1+1δ1αϵ2 then qIFSEs(T)(δϵ1+(1δ)ϵ2)={qIFSEs(T)(αϵ1+(1α)ϵ4)qIFSEs(T)(ϵ1)qIFSEs(T)(ϵ4)} (38) qIFSEs(T)(δϵ1+(1δ)ϵ2){qIFSEs(T)(ϵ1)qIFSEs(T)(ϵ4)}(38) From (Equation32), (Equation37) and qIFSEs(ϵ1)=qIFSEs(ϵ2), we have (39) qIFSEs(T)(δϵ1+(1δ)ϵ2)qIFSEs(ϵ4),(39) Also, (δϵ1+(1δ)ϵ2)=(αϵ1+(1α)ϵ4) becomes ϵ4=11α(δϵ1+(1δ)ϵ2)α1αϵ1=11α(δϵ1+(1δ)ϵ2)α1αϵ1(1δ(δϵ1+(1δ)ϵ2)1δδϵ2)=δα(1α)δ(δϵ1+(1δ)ϵ2)+(1δα(1α)δ)ϵ2 (40) ϵ4=δα(1α)δ(δϵ1+(1δ)ϵ2)+(1δα(1α)δ)ϵ2(40) now from (Equation32), (Equation39) qIFSEs(ϵ1)=qIFSEs(ϵ2) and strictly convex IFSEs condition, we have (41) Z qIFSEs(T)(δϵ1+(1δ)ϵ2)Z qIFSEs(ϵ4)(41) Hence (Equation39) and (Equation40) contradict the fact.

Theorem 5.2

Suppose (qIFSEs,S) be a strictly concave IFSEs. If ∃αΓ0ϵ1, ϵ2 S such that qIFSEs(αϵ1+(1α)ϵ2)qIFSEs(ϵ1)qIFSEs(ϵ2)then (qIFSEs,S) is a concave IFSEs.

Proof.

Assume that qIFSEs(ϵ1)qIFSEs(ϵ2) and ∃ϵ1,ϵ2S, δΓ0 (42) ZqIFSEs(T)(δϵ1+(1δ)ϵ2)Z{qIFSEs(T)(ϵ1)qIFSEs(T)(ϵ2)}(42) if qIFSEs(ϵ1)qIFSEs(ϵ2), then (Equation42) contradicts that (qIFSEs,S) is a strictly concave IFSEs.

now if we take qIFSEs(ϵ1)=qIFSEs(ϵ2) and δ[0,α], suppose ϵ3=δαϵ1+(1δα)ϵ2 and τ=(1δ1)(1α1)1. so qIFSEs(T)(δϵ1+(1δ)ϵ2)=qIFSEs(T)(α(δαϵ1+(1δα)ϵ2)+(1δ)ϵ2)=qIFSEs(T) (αϵ3+(1α)ϵ2){qIFSEs(T)(ϵ1)qIFSEs(T)(ϵ2)}

(43) qIFSEs(T)(δϵ1+(1δ)ϵ2){qIFSEs(T)(ϵ1)qIFSEs(T)(ϵ2)}(43) now qIFSEs(T)(ϵ3)=qIFSEs(T)(δαϵ1)+(1δαϵ2) (44) =qIFSEs(T)(τϵ1+(1τ)(δϵ1+(1δ)ϵ2))(44) From (Equation41), (Equation43) and qIFSEs(ϵ1)=qIFSEs(ϵ2), we have (45) qIFSEs(T)(δϵ1+(1δ)ϵ2)qIFSEs(ϵ3),(45) now from (Equation42), (Equation44) qIFSEs(ϵ1)=qIFSEs(ϵ2) and strictly concave se-set condition, we have (46) qIFSEs(T)(δϵ1+(1δ)ϵ2)qIFSEs(ϵ3),(46) or (47) Z qIFSEs(T)(δϵ1+(1δ)ϵ2)Z qIFSEs(ϵ3),(47) thus (Equation46) and (Equation47) contradicts the fact.

If qIFSEs(ϵ1)=qIFSEs(ϵ2) and δ[α,1], suppose ϵ4=δα1αϵ1+1δ1αϵ2 then qIFSEs(T)(δϵ1+(1δ)ϵ2)=qIFSEs(T)(αϵ1+(1α)ϵ4){qIFSEs(T)(ϵ1)qIFSEs(T)(ϵ4)} (48) qIFSEs(T)(δϵ1+(1δ)ϵ2){qIFSEs(T)(ϵ1)qIFSEs(T)(ϵ4)}(48) From (Equation42), (Equation48) and qIFSEs(ϵ1)=qIFSEs(ϵ2), we have (49) qIFSEs(T)(δϵ1+(1δ)ϵ2)qIFSEs(ϵ4),(49) Also, (δϵ1+(1δ)ϵ2)=(αϵ1+(1α)ϵ4) becomes ϵ4=11α(δϵ1+(1δ)ϵ2)α1αϵ1=11α(δϵ1+(1δ)ϵ2)α1αϵ1(1δ(δϵ1+(1δ)ϵ2)1δδϵ2)=δα(1α)δ(δϵ1+(1δ)ϵ2)+(1δα(1α)δ)ϵ2 (50) ϵ4=δα(1α)δ(δϵ1+(1δ)ϵ2)+(1δα(1α)δ)ϵ2(50) Now from (Equation42), (Equation49), qIFSEs(ϵ1)=qIFSEs(ϵ2) and strictly concave IFSEs condition, we have (51) Z qIFSEs(T)(δϵ1+(1δ)ϵ2)Z qIFSEs(ϵ4),(51) Hence (Equation49) and (Equation5) contradict the fact.

Theorem 5.3

Suppose (qIFSEs,S) be a convex IFSEs. If ∃αΓ0ϵ1, ϵ2 S with qIFSEs(ϵ1)qIFSEs(ϵ2) qIFSEs(αϵ1+(1α)ϵ2)qIFSEs(ϵ1)qIFSEs(ϵ2)then (qIFSEs,S) is a strictly convex IFSEs.

Proof.

Assume ϵ1,ϵ2S,δΓ0 such that (52) Z qIFSEs(δϵ1+(1δ)ϵ2)Z{qIFSEs(ϵ1)qIFSEs(ϵ2)}(52) If qIFSEs(ϵ1)qIFSEs(ϵ2), then above equation becomes (53) Z qIFSEs(δϵ1+(1δ)ϵ2)Z qIFSEs(ϵ1),(53) But by the convexity condition, we have (54) qIFSEs(δϵ1+(1δ)ϵ2){qIFSEs(ϵ1)qIFSEs(ϵ2)}(54) By Equations (Equation52) and (Equation53), we get (55) qIFSEs(δϵ1+(1δ)ϵ2)={qIFSEs(ϵ1)qIFSEs(ϵ2)}.(55) Continuing with qIFSEs(ϵ1)qIFSEs(ϵ2), we get (56) qIFSEs(δϵ1+(1δ)ϵ2)=qIFSEs(ϵ2)(56) or (57) qIFSEs(δϵ1+(1δ)ϵ2)qIFSEs(ϵ1)(57) By supposition and (Equation56), we have (58) qIFSEs(αϵ1+(1α)(δϵ1+(1δ)ϵ2))qIFSEs((δϵ1+(1δ)ϵ2))(58) We generalize it for taking n{1,2,3,,} (59) qIFSEs(αnϵ1+(1αn)(δϵ1+(1δ)ϵ2))qIFSEs((δϵ1+(1δ)ϵ2))(59) Suppose ϵ3=(τϵ1+(1τ)ϵ2) with τ=δαnδ+αnΓ0 for any value of n.

Then we see from above equation qIFSEs(ϵ3)=qIFSEs(τϵ1+(1τ)ϵ2)=qIFSEs(αnϵ1+(1αn)(δϵ1+(1δ)ϵ2))qIFSEs(δϵ1+(1δ)ϵ2) (60) qIFSEs(ϵ3)qIFSEs(δϵ1+(1δ)ϵ2)(60) Also, suppose ϵ4=(γϵ1+(1γ)ϵ2) with γ=δαn+11α+δαn1αΓ0 for any n. we have (61) qIFSEs(δϵ1+(1δ)ϵ2)=qIFSEs(αϵ3+(1α)ϵ4)(61) Also when, qIFSEs(ϵ3)qIFSEs(ϵ4), subsequently by using the convexity of IFSEs and Equation (Equation61) we have ZqIFSEs(ϵ3)ZqIFSEs(δϵ1+(1δ)ϵ2) which contradicts Equation (Equation59).

Now when ZqIFSEs(ϵ3)ZqIFSEs(ϵ4), subsequently by using the supposition of theorem and Equation (Equation53), we have qIFSEs(δϵ1+(1δ)ϵ2){qIFSEs(ϵ3)qIFSEs(ϵ4)} {qIFSEs(ϵ1)qIFSEs(ϵ2)}{qIFSEs(ϵ1)qIFSEs(ϵ2)}=qIFSEs(ϵ1) qIFSEs(δϵ1+(1δ)ϵ2)qIFSEs(ϵ1) which contradicts Equation (Equation61).

Corollary 5.1

Suppose (qIFSEs,S) be a concave IFSEs. If ∃αΓ0ϵ1, ϵ2 S with qIFSEs(ϵ1)qIFSEs(ϵ2) qIFSEs(αϵ1+(1α)ϵ2)qIFSEs(ϵ1)qIFSEs(ϵ2)then (qIFSEs,S) is a strictly concave IFSEs.

Proof.

This is simple by taking the complement of the above equations.

Theorem 5.4

Suppose (qIFSEs,S) be a strongly convex soft fuzzy expert set. If ∃αΓ0ϵ1, ϵ2 S with (62) qIFSEs(αϵ1+(1α)ϵ2)qIFSEs(ϵ1)qIFSEs(ϵ2)(62) then (qIFSEs,S) is a convex IFSEs.

Proof.

Assume ϵ1,ϵ2S,δΓ0 such that (63) Z qIFSEs(δϵ1+(1δ)ϵ2)Z {qIFSEs(ϵ1)qIFSEs(ϵ2)}(63) If ϵ1ϵ2 then Equation (Equation62) contradicts the fact that (qIFSEs,S) is a convex IFSEs.

If ϵ1=ϵ2, select δδ1 Γ0 with δ=αδ1+(1α)δ1.

Suppose ϵ1=δ1ϵ1+(1δ1)ϵ2, ϵ2=δ2ϵ1+(1δ2)ϵ2

Equation (Equation62) becomes (64) Z qIFSEs(ϵ1)Z {qIFSEs(ϵ1)qIFSEs(ϵ2)}(64) (65) Z qIFSEs(ϵ2)Z {qIFSEs(ϵ1)qIFSEs(ϵ2)}(65) By Equations (Equation62), (Equation63) and (Equation64), we have qIFSEs(αϵ1+(1α)ϵ2){qIFSEs(ϵ1)qIFSEs(ϵ2)}{qIFSEs(ϵ1)qIFSEs(ϵ2)}{qIFSEs(ϵ1)qIFSEs(ϵ2)}=qIFSEs(ϵ1)qIFSEs(ϵ2)And this contradicts that (qIFSEs,S) is a strongly convex IFSEs.

Corollary 5.2

Suppose (qIFSEs,S) be a strongly concave IFSEs. If ∃αΓ0ϵ1, ϵ2 S with (66) qIFSEs(αϵ1+(1α)ϵ2)qIFSEs(ϵ1)qIFSEs(ϵ2)(66) then (qIFSEs,S) is a concave IFSEs.

Proof.

This can be done by using the proof of the above Theorem 5.4. In Theorem 5.4, definition of strongly convex IFSEs has been used and to prove this corollary just make use of strongly concave IFSEs.

Theorem 5.5

Suppose (qIFSEs,S) be a convex IFSEs. If ∃αΓ0ϵ1, ϵ2 such that ϵ1ϵ2 S then qIFSEs(αϵ1+(1α)ϵ2)qIFSEs(ϵ1)qIFSEs(ϵ2)then (qIFSEs,S) is a strongly convex IFSEs.

Proof.

Assume ∃ (ϵ1ϵ2) ϵ1,ϵ2,δΓ0 such that (67) Z qIFSEs(δϵ1+(1δ)ϵ2)Z {qIFSEs(ϵ1)qIFSEs(ϵ2)}(67) By the condition of convex IFSEs and from above equation, we have (68) qIFSEs(δϵ1+(1δ)ϵ2)={qIFSEs(ϵ1)qIFSEs(ϵ2)}(68) Moreover, it can be written as (69) (αϵ1+(1α)ϵ2)=(δϵ1+(1δ)ϵ2)(69) While ϵ1=(δϵ1+(1δ)ϵ2), ϵ2=(δϵ1+(1δ)ϵ2) with selecting δΓ0

Also by the definition of convexity of IFSEs and above defining ϵ1,ϵ2, we get (70) qIFSEs(ϵ1){qIFSEs(ϵ1)qIFSEs(ϵ2)}(70) (71) qIFSEs(ϵ2){qIFSEs(ϵ1)qIFSEs(ϵ2)}(71) Using Equations (Equation68), (Equation69), (Equation70) and given statement, we have qIFSEs(δϵ1+(1δ)ϵ2)=qIFSEs(αϵ1+(1α)ϵ2){qIFSEs(ϵ1)qIFSEs(ϵ2)}{qIFSEs(ϵ1)qIFSEs(ϵ2)}{qIFSEs(ϵ1)qIFSEs(ϵ2)}=qIFSEs(ϵ1)qIFSEs(ϵ2)which contradicts to Equation (Equation67).

Corollary 5.3

Suppose (qIFSEs,S) be a concave IFSEs. If ∃αΓ0ϵ1, ϵ2 such that ϵ1ϵ2 S then qIFSEs(αϵ1+(1α)ϵ2)qIFSEs(ϵ1)qIFSEs(ϵ2)then (qIFSEs,S) is a strongly concave IFSEs.

Proof.

This can be done by using the proof of the above Theorem 5.5. In Theorem 5.5, definition of strongly convex IFSEs with certain conditions has been used and to prove this corollary just make use of strongly concave IFSEs with the same certain conditions.

6. Convex hull and convex cone

In this section, a new concept of convex hull and cone for IFSEs (6.1 and 6.2) has been developed with the help of existing concept of convex hull and cone on s-set by Majeed [Citation42]. The notation X(Z) will be used for collection of IFSEss.

Definition 6.1

The convex hull of an IFSEs (C,S) can be defined as

Conh (C,S)= ¯(T,)(C,S){(T,S):(T,S) ∈ X(Z) are IFSEs }.

Theorem 6.1

Let (T,S)X(Z) then its convex hull is given by as ConhC(ϵ)=ˆmNˆPK(ϵ,m)¯{C(v):vP}where K(ϵ,m)={i=1m{ϵ1,ϵ2,,ϵm}S : ϖ[0, 1]with i=1mϖ=1, i=1mϖiϵi=1}.

Proof.

Using definition of convex hull of (H,S), we have convhC(ϵ)= ¯T(ϵ):C(ϵ)T(ϵ) such that (T,S)X(Z). Now define the IFSEs as convhC~(ϵ)=ˆmNˆPK(ϵ,m)¯{C(v):vP}now we have to show that convhC(ϵ)=convhC~(ϵ). To prove this, we will show convhC(ϵ)convhC~(ϵ)andthen convhC(ϵ)C~(ϵ).As we know that every IFSEs is a convex IFSEs. Then by using the definition of convex IFSEs, we have T(ϵ)ˆmNˆPK(ϵ,m)¯{C(v): vP}convhC~(ϵ)Using the intersection to the left side of the above relation, we get ¯C(ϵ)T(ϵ)T(ϵ)convhC~(ϵ)convhC(ϵ)convhC~(ϵ).Now we will prove that convhC(ϵ)convhC~(ϵ). It is enough to show that (C~,S) is a convex IFSEs. Actually, since convh(C, S) is the smallest convex IFSEs containing (C, S), convh(C,S)(C~,S). Suppose ϵ=i=1qviϵi and ϵ=i=1lγiϵi such that {ϵ1,ϵ2,,ϵq}K(ϵ,q) and {ϵ1,ϵ2,,ϵl}K(ϵ,l), with i=1qϵi =1 and i=1lϵi =1. Therefore, C~(αϵ+(1α)ϵ)=C~(αi=1qviϵi+(1α)i=1lγiϵi)such that αi=1qviϵi+(1α)i=1lγiϵi=1 and {ϵ1,ϵ2,ϵ3,,ϵq,ϵ1,ϵ2,ϵ3,,ϵl}C(αϵ+(1α)ϵ,l+q). As S=Rn, so αϵ+(1α)ϵS=Rn. Using the definition of IFSEs described above to ϵ,ϵ and to x = αϵ+(1α)ϵ, we have ˆl+qNˆDVK(x,l+q)¯i=1l+q{C(xi):xiDV}ˆqNˆDK(ϵ,q)¯i=1q{C(ϵi):ϵiD}ˆlNˆVK(ϵ,l)¯i=1l{C(ϵi):ϵiV},i.e. C~(αϵ+(1α)ϵ)C~(ϵ)C~(ϵ).hence the required result is proved.

Definition 6.2

Let (C,S) be IFSEs, then it is called a cone if C(αϵ)C(ϵ)for all ϵS and α>0.

If the IFSEs is convex, then it is named as a convex cone.

Theorem 6.2

A IFSEs (C,S) is said to be convex cone iff for every ϵ,ϵ S and α>0

  1. C(αϵ)C(ϵ)

  2. C(ϵ+ϵ)C(ϵ)C(ϵ).

Proof.

Suppose (C,S) be an IFSEs, then by definition statement (1) is proved.

Now to prove 2nd statement take α=12 and for any ϵ,ϵS, then by definition of convex cone C(12ϵ+12ϵ)C(ϵ)C(ϵ)and C(2(12ϵ+12ϵ))C(12ϵ+12ϵ)From the above equations, we have C(ϵ+ϵ)=C(2(12ϵ+12ϵ))C(12ϵ+12ϵ)C(ϵ)C(ϵ)C(ϵ+ϵ)C(ϵ)C(ϵ)hence the 2nd result is proved. Conversely, we have to show that (C,S) is convex IFSEs.

By first condition (C,S) is cone. By making use of 1st and 2nd conditions with α[0,1], it is clear that (C,S) is convex IFSEs.

i.e. C(αϵ+(1α)ϵ)C(αϵ)C((1α)ϵ)H(ϵ)H(ϵ)C(αϵ+(1α)ϵ)C(ϵ)C(ϵ).Which shows that (C,S) is convex IFSEs.

Corollary 6.1

If (C,S) is IFSEs then it is a convex cone iff C(i=1mαiϵi)i=1mC(ϵi)for all {ϵ1,ϵ2,,ϵi}S and αi>0.

7. Comparison

A comparison analysis of the model has been shown in Table . In this table, some prominent characteristics of existing models have been compared with proposed structure. These characteristics include membership function (M˙Vˇ), non-membership (N˙M˙Vˇ), single argument approximate function (SˆF˙) and multi-decisive opinion (M˙D¯O¨). The Yes and No will be denoted by Yˇ and N˘ in the following Table . From Table , it is clear that our proposed model is more generalized than the above described models.

Table 1. Comparison with different models.

8. Conclusions

In this article, (α,v)-convexity cum concavity, (α,v)-convexity cum concavity in 1st-sense and 2nd-sense, convex hull and convex cone are developed for IFSEs. Several classical axiomatic properties and operational results have been generalized under IFSEs. Although the proposed framework is an intelligent approach for convex optimization and parameterization with entitlement of fuzzy membership grades and intuitionistic fuzzy non-membership grades yet it has limitations regarding the consideration of neutral grades; therefore, the future work includes the extension of this study for such environments to address its limitations. Moreover, the generalization of various other variants of classical convexity may also a part of its future work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

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