Hypersoft set is a novel area of study which is established as an extension of soft set to handle its limitations. It employs a new approximate mapping called multi-argument approximate function which considers the Cartesian product of attribute-valued disjoint sets as its domain and the power set of universe as its co-domain. The domain of this function is broader as compared to the domain of soft approximate function. It is capable of handling the scenario where sub-attribute-valued sets are considered more significant than taking merely single set of attributes. In this study, notions of set inclusion, convex (concave) sets, strongly convex (concave) sets, strictly convex (concave) sets, convex hull, and convex cone are conceptualized for the multi-argument approximate function. Based on these characterized notions, some set-theoretic inequalities are established with generalized properties and results. The set-theoretic version of classical Jensen’s type inequalities is also discussed with the help of proposed notions.

1. Introduction

In order to provide the parameterization tool to fuzzy set-like models [1–3] for dealing with uncertain data, in 1999, Molodtsov [4] employed the concept of soft approximate function (sa-function) for defining a novel model soft set (s-set). This set employs sa-function which maps single set of parameters to power set of initial universe. Many scholars discussed rudiments of s-sets but the contributions of Ali et al. [5], Babitha and Sunil [6, 7], Ge and Yang [8], Li [9], Maji et al. [10], Pei and Miao [11], and Sezgin and Atagün [12] are considered prominent regarding the characterization of elementary properties and set-theoretic operations of s-sets. Abbas et al. [13] investigated the notions of soft points and discussed their limitations, comparisons, and challenges. The hybridized structures of s-set with fuzzy set, intuitionistic fuzzy set, and neutrosophic set were developed by the authors [14–16]. In numerous real-life situations, parameters are to be further classified into their respective parametric-valued disjoint sets. The existing s-set model is deficient for dealing with such sort of partitioning of parameters. Hypersoft set (hs-set) [17] is developed to generalize s-set to manage real-life scenarios with subparametric disjoint sets. In order to utilize hs-set in various areas of knowledge, Abbas et al. [18] and Saeed et al. [19] investigated the elementary properties and operations of hs-set. Ajay [20] defined alpha open hs-sets for their further use in the characterization of relevant topological spaces. Debnath [21], Yolcu and Ozturk [22], Yolcu et al. [23], and Kamacı [24, 25] discussed the applications of hs-set in decision-making process by using its hybridized models. Martin and Smarandache [26] developed a novel outlook called concentric plithogenic hypergraph based on the combination of plithogenic set and hs-set. Musa and Asaad [27] discussed the bipolarity of hs-set and investigated some of its properties and aggregation operations.

1.1. Motivation

Convexity has a vital function in optimization, image processing and classification, pattern recognition, and other sub-fields of computational geometry. In order to tackle optimization and other related problems for vague and uncertain data, notions of classical convexity and concavity (Lara et al. [28, 29]) under s-set environment were characterized initially by Deli [30]; then, this work was further extended by Ihsan et al. [31, 32], Rahman et al. [33], and Salih and Sabir [34] for further investigations on certain variants of convexity. But these notions are not sufficient for handling information with entitlement of multi-argument approximate function (maa-function); therefore, Rahman et al. [35] conceptualized the notions of convexity cum concavity under hs-set scenarios. It can be easily observed that the aforementioned models focused on the consideration of sa-function and maa-function while defining certain variants of convexity cum concavity and ignored certain other important properties and aspects of convexity cum concavity which may have key role in convex optimization, computational geometry, and other related problems. Moreover, various classical inequalities like Jensen’s inequalities are not discussed as particular cases. The main motive of the proposed study is to address the limitations and shortcomings of the abovementioned literature.

The major contributions of the paper are outlined as follows:

(1)
The classical notions of convex (concave) set, strictly convex (concave) set, strongly convex (concave) set, convex hull, and convex cone are generalized under hs-set environment with entitlement of maa-function.
(2)
The concept of set inclusion for maa-function is characterized and then some set-theoretic inequalities are established based on these set inclusions along with other proposed notions. It is verified that the classical inequalities for soft approximate function are also valid under hs-set environment with entitlement of maa-function.
(3)
Classical Jensen’s convex inequalities are generalized for maa-function and discussed as particular cases of proposed set-theoretic inequalities.
(4)
The vivid comparison is made with related existing research studies to judge the distinction of the proposed study.

The rest of the paper is organized as follows. Section 2 reviews the notions of some relevant definitions used in the proposed work. Section 3 presents notions of strictly and strongly convexity cum concavity on hs-set. It also presents some set-theoretic inequalities based on these notions. Section 4 concludes the paper with more future directions and scope.

2. Preliminaries

This section reviews few elementary definitions and results regarding s-set and hs-set. In order to deal with uncertainties, the models like Zadeh’s fuzzy set [3], Atanassov’s intuitionistic fuzzy set [1], and Smarandache’s neutrosophic set [2] are considered significant, but these models face inadequacy regarding parameterization modes; therefore, Molodtsov [4] presented the concept of s-set as a new mathematical model to equip fuzzy set-like models with parameterization tool.

Definition 1 (see [4].)Let be an initial universe and be a set of parameters; then, a sa-function is a mapping

(1)

and it is defined as

(2)

where

denotes the power set of

with k ≤ n. The pair

is known as s-set and represented by

. The collection of s-sets is denoted by Ω_ss.

Example 1. Let , , and ; then, approximate elements of sa-function for s-set are given as

(3)

and s-set

is stated as

(4)

Many researchers investigated the basic properties, aggregation operations, and relations of s-set, but the contributions of authors [6, 11, 15] are more significant in this regard. Some properties of s-set are stated below for proper understanding.

Definition 2 (see [10].)If , then

(1)
if and .
(2)
is stated as
(5)
(3)
is defined as
(6)

In many decision-making circumstances, it is observed that parameters need to be further partitioned into their respective parametric-valued sets. The s-set and its hybrids are not capable to tackle such kind of parametric classification. So, Smarandache [17] introduced a new model called hs-set which is capable to deal with such classification by employing a new approximate mapping, i.e., maa-function.

Definition 3 (see [17].)Let be an initial universe and be a set of parameters. The respective attribute-valued non-overlapping sets of each element of are

(7)

and

where every

is a n-tuple element of

with

, |⋅| denotes the cardinality of

then a maa-function is a mapping

(8)

and it is defined as

(9)

where

denotes the power set of

with k ≤ r. The pair

is known as hs-set and represented by

. The collection of all hs-sets is symbolized as ℧_hss.

See [18, 19] for more operational properties of hs-sets.

3. Notions of Convexity cum Concavity for maa-Function with Relevant Set-Theoretic Inequalities

In this section, we define Δ-sets and Ψ-sets and prove some results. Throughout the remaining paper, , II^⋅, II^°, and will play the role of Rⁿ, closed unit interval, open unit interval, and universal set, respectively. Let where with , for i ≠ j, and i, j ∈ {1,2,3, …, n} ; and .

Considering Definition 2, Rahman et al. [35] extended the work of Deli [30] and introduced the following notions of convexity cum concavity with multi-argument approximate settings.

Definition 4 (see [35].)If , then the of a hs-set is stated as

(10)

Definition 5 (see [35].)A hs-set on is known as convex hs-set (Δ-set) if

(11)

and

. Its collection is represented as ℍ^Δ.

Definition 6 (see [35].)A hs-set on is known as concave hs-set (Ψ-set) if

(12)

and

Salih and Sabir [34] discussed the properties of strongly and strictly convexity cum concavity by using approximate function of s-set, but in case of multi-argument approximate function, their presented work is not sufficient; therefore, the following generalized definitions are characterized with the entitlement of multi-argument approximate function which considers (a Cartesian product of attribute-valued disjoint sets) as its domain and maps it to power set of initial universe.

Definition 7. The hs-set on is called a strongly convex hs-set (Ω_schss) if

(13)

for every

and

Definition 8. The hs-set on is called a strongly concave hs-set (Ω_schss) if

(14)

for every

and

Definition 9. The hs-set on is called a strictly convex hs-set (Θ_schss) if

(15)

for every

and

Definition 10. The hs-set on is called a strictly concave hs-set (Θ_schss) if

(16)

for every

and

Note. (i) If we replace ⊇, ⊆, ⊃, ⊂, ∩, and ∪ by ≥, ≤, >, <, ∧, and ∨, respectively, in above equations, i.e., from (11) to (16), we get the same notions for fuzzy sets as described by Zadeh [3] and Chaudhuri [36] where ∧ and ∨ are min and max operators. (ii) Similarly, if we replace (Cartesian product of attribute-valued sets) by (a set of attributes) in , we get simple approximate function of s-set as given in Definition 1 and consequently the above definitions will be treated for s-set as discussed in [30, 34].

Example 2. Suppose an educational institution wants to recruit some teachers with the help of some defined indicators. Consider there are 10 candidates who applied for the job and thus form a universe of discourse . The candidates will be evaluated with the help of the following attributes:

(17)

and their respective attribute-valued sets are

(18)

For simplicity, we write

(19)

The hs-set is a function defined by the mapping where .

Since the Cartesian product of is a 3-tuple, we consider ; then, the function becomes . Also, consider ; then, the function becomes .

Now,

(20)

Let ; then, we have

(21)

which is again a 3-tuple. By using the decimal round off property, we get (12,16,3).

(22)

and from equations (20) and (22), we have

(23)

and from equations (22) and (23), we have

(24)

Definition 11. The convex hull of a hs-set is the smallest Δ-set over which contains , i.e.,

(25)

Definition 12. A hs-set is called a cone if for all and , we have

(26)

If is a Δ-set, then is called convex cone hs-set (cchs-set). The collection of all cchs-sets is represented by Ω^cchs.

Definition 13. The convex cone of a hs-set is the smallest cchs-set which contains , i.e.,

(27)

3.1. Set-Theoretic Inequalities for maa-Function

In this section, some set-theoretic inequalities are presented with discussion on their generalized results.

Theorem 1. A hs-set is a Δ-set if and only if for all and such that , we have

(28)

Proof. Let the sufficient condition hold. In particular, for any two elements and , then we have which implies that is a Δ-set according to Definition 5. Conversely, let be a Δ-set; then, for any n ∈ ℕ, define a set such that

(29)

By using induction on n ∈ ℕ, we prove the necessary condition as follows:

C-1: let n = 2; then, and such that . As is a Δ-set, .
C-2: let the result (29) be valid for n = k which implies that for and with , we have
(30)

Now prove the result (29) for n = k + 1 with conditions and with . Suppose there exists one (say ) such that . Let where for . Therefore, .

Hence, , and equation (30) implies that

(31)

Now, we apply C-1 for , i.e.,

(32)

which implies

(33)

Remark 1.

1.
The result presented in Theorem 1 may or may not be valid for n⟶∞.
(2)
If, in case, the result presented in Theorem 1 is valid for n⟶∞, then this result will be considered as generalized version of countably sub-additive sets under hs-set environment with entitlement of maa-function.
(3)
If only equality holds for countable case, then this result will be considered as generalized version of countably additive sets under hs-set environment with entitlement of maa-function.

Theorem 2. Let be Θ_schss. If there exists such that , then is a Δ-set.

Proof. Assume that and such that

(34)

If , then (34) contradicting is a Θ_schss.

If and , let and . Thus, by hypothesis,

(35)

Now

(36)

From (14), (15), and , it follows that

(37)

From (34), (36), , and Θ_schss condition, it follows that

(38)

(39)

Hence, (37) and (39) give a contradiction.

If and , let . Thus, by hypothesis,

(40)

From (34), (40), and , it follows that

(41)

On the other hand, gives

(42)

From (34), (42), , and Θ_schss condition, it follows that

(43)

Hence, (41) and (43) give a contradiction.

Theorem 3. Let be a Θ_scohss. If there exists such that , then is a Ψ-set.

Proof. This can easily be proved by following the procedure discussed in Theorem 2.

Theorem 4. Let be a Δ-set. If there exists such that , then is a Θ_scohss.

Proof. Assume that such that

(44)

If , then (44) gives

(45)

On the other hand, from the Δ-set condition, we have that

(46)

From (44) and (46), it follows that

(47)

which, together with

, yields

(48)

(49)

Thus, from (49) and the hypothesis,

(50)

More generally, for n ∈ {1,2,3, …}, it can easily be shown that

(51)

Let where for some n. Then, from (51), we see that

(52)

Also, let where for some n.

Then,

(53)

Now, if , then (53) and , which is a Δ-set, imply that

(54)

which contradicts (52).

If , then (53) and the hypothesis of the theorem imply that

(55)

This contradicts (49).

Theorem 5. Let be a Ψ-set. If there exists such that , then is a Θ_scohss.

Proof. This can easily be proved by following the procedure discussed in Theorem 4.

Theorem 6. Let be a Ω_schss. If there exists , such that

(56)

then

is a Δ-set.

Proof. Assume that such that

(57)

If , then (56) contradicts that is a Ω_schss.

If , then choose such that .

Let . Then, (56) implies that

(58)

(59)

According to (56), (58), and (59), we have

(60)

which contradicts that

is a Ω_schss.

Theorem 7. Let be a Ω_schss. If there exists , such that , then is a Ψ-set.

Proof. This can easily be proved by following the procedure discussed in Theorem 6.

Theorem 8. Let be a Δ-set. If there exists , such that , then is a Ω_schss.

Proof. Assume that such that

(61)

Thus, from (61) and the Δ-set condition, we get that

(62)

Furthermore, it can be easily seen that

(63)

where both

and

are of the forms

and

for choosing

On the other hand, from the Δ-set condition and our definition of and , we get

(64)

(65)

Therefore, from (63)–(65) and the hypothesis of the theorem, we get that

(66)

This contradicts (62).

Theorem 9. Let be a Ψ-set. If , such that , then is a Ω_scohss.

Proof. This can easily be proved by following the procedure discussed in Theorem 8.

Theorem 10. A hs-set is a convex cone iff for , ,

(i)
.
(ii)
.

Proof. Let be a cchs-set over ; then, part (i) is obviously verified. Consider . As is a cchs-set, by using Definition 5 with , we have

(67)

(68)

and combining equations (67) and (68), we get

(69)

which implies

(70)

Conversely, let the given conditions (i) and (ii) be satisfied. According to condition (i), is a cone. Now using both conditions for , we have

(71)

which shows that

is a Δ-set. Hence,

is cchs-set.

Theorem 11. A hs-set is a convex cone iff for , ,

(72)

Proof. By induction on n, is a cone when we take n = 1, i.e.,

(73)

The remaining part can be easily proved with the help of Theorem 1.

3.2. Jensen-Convex (Mid-Convex) Set and Its Inverse for maa-Function

In this part of the paper, we present classical versions of Jensen-convex sets and their inverse for multi-argument approximate functions. It can be seen that the nature of Jensen-convex set and its inverse is interchanged for maa-function in set-theoretic approach.

Definition 14. A Δ-set is said to be J-convex hs-set (Δ_J-set) over if

(74)

Definition 15. A Ψ-set is said to be J-concave hs-set (Ψ_J-set) over if

(75)

Remark 2. If ⊆ is replaced with ≤ and ∪ by + in equation (75), we get classical version of Jensen inequality.

Definition 16. A Ω_schss on is called a strongly J-convex hs-set if

(76)

for every

Definition 17. A Ω_schss on is called a strongly J-concave hs-set if

(77)

for every

Remark 3. If ⊂ is replaced with < and ∪ by + in equation (77), we obtain classical version of Jensen inequality for strong convexity.

Definition 18. A Θ_schss on is called a strictly J-convex hs-set if

(78)

for every

Definition 19. A Θ_schss on is called a strictly J-concave hs-set if

(79)

for every

Remark 4. If ⊂ is replaced with < and ∪ by + in equation (79), we obtain classical version of Jensen inequality for strict convexity.

3.3. Comparative Analysis

In this section, the proposed study is compared professionally with some existing relevant works in order to judge its advantageous aspects. This comparison is based on some important evaluating features like emphasis on deep observation of attributes (EDOA), consideration of maa-function (CMAAF), sa-function (CSAF), discussion on classical inequalities as particular cases (DCIPC), discussion on convex hull (DCH), discussion on convex cone (DCC), strictly cum strongly convexity (SCSCx), and strictly cum strongly concavity (SCSCc) which depict the advantages of the proposed study over the existing approaches which are presented in Table 1.

1. Comparison of the proposed approach with existing relevant approaches.

References	EDOA	CMAAF	CSAF	DCIPC	DCH	DCC	SCSCx	SCSCc
Deli [30]	×	×	✓	×	×	×	✓	✓
Ihsan et al. [31]	×	×	✓	×	×	×	×	×
Ihsan et al. [32]	×	×	✓	×	×	×	×	×
Majeed [37]	×	×	✓	×	✓	✓	×	×
Rahman et al. [35]	✓	✓	✓	×	×	×	×	×
Salih and Sabir [34]	×	×	✓	×	×	×	✓	✓
Proposed study	✓	✓	✓	✓	✓	✓	✓	✓

4. Conclusion

In this paper, the classical notions of convex (concave) sets, strictly convexity (concavity), strongly convexity (concavity), convex hull, convex cone, and set inclusion, are generalized for hypersoft set environment. Some set-theoretic inequalities based on these notions are established and discussed by using certain aspects of set inclusion. Although the proposed study provides a conceptual framework for handling convex optimization problems under uncertain scenario with deep observation of parameters, it has limitations for hypersoft set environment with fuzzy, intuitionistic fuzzy, and neutrosophic settings in the domain as well as in the range of maa-function. Therefore, future work may include the following:

(1)
The establishment of modular inequalities by considering the interval nature of uncertain data for maa-function.
(2)
The establishment of classical inequalities such as Ostrowski-type inequalities, Hadamard-type inequalities, Minkowski’s inequalities, and others under hs-set environment.
(3)
The extension of the proposed study for hybridized structures of hs-set like fhs-set, ifhs-set, nhs-set, and so on.
(4)
The extension of the proposed study for m-convex and m-concave sets under hs-set environment.
(5)
The implementation of the proposed framework for solving convex optimization problems and decision-making problems.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Open Research

Data Availability

No data were used to support this study.

References

1 Atanassov K. T., Intuitionistic fuzzy sets, Fuzzy Sets and Systems. (1986) 20, no. 1, 87–96, https://doi.org/10.1016/s0165-0114(86)80034-3, 2-s2.0-0002740725.
10.1016/S0165-0114(86)80034-3
Web of Science® Google Scholar
2 Smarandache F., Neutrosophy: Neutrosophic Probability, Set, and Logic: Analytic Synthesis and Synthetic Analysis, 1998, American Research Press, Rehoboth, BE, USA.
Google Scholar
3 Zadeh L. A., Fuzzy sets, Information and Control. (1965) 8, no. 3, 338–353, https://doi.org/10.1016/s0019-9958(65)90241-x, 2-s2.0-34248666540.
10.1016/S0019-9958(65)90241-X
CAS Web of Science® Google Scholar
4 Molodtsov D., Soft set theory-First results, Computers & Mathematics with Applications. (1999) 37, no. 4-5, 19–31, https://doi.org/10.1016/s0898-1221(99)00056-5.
10.1016/S0898-1221(99)00056-5
Web of Science® Google Scholar
5 Ali M. I., Feng F., Liu X., Min W. K., and Shabir M., On some new operations in soft set theory, Computers & Mathematics with Applications. (2009) 57, no. 9, 1547–1553, https://doi.org/10.1016/j.camwa.2008.11.009, 2-s2.0-63049126255.
10.1016/j.camwa.2008.11.009
Web of Science® Google Scholar
6 Babitha K. V. and Sunil J. J., Soft set relations and functions, Computers & Mathematics with Applications. (2010) 60, no. 7, 1840–1849, https://doi.org/10.1016/j.camwa.2010.07.014, 2-s2.0-77956394869.
10.1016/j.camwa.2010.07.014
Web of Science® Google Scholar
7 Babitha K. V. and Sunil J. J., Transitive closures and orderings on soft sets, Computers & Mathematics with Applications. (2011) 62, no. 5, 2235–2239, https://doi.org/10.1016/j.camwa.2011.07.010, 2-s2.0-80052259835.
10.1016/j.camwa.2011.07.010
Web of Science® Google Scholar
8 Ge X. and Yang S., Investigations on some operations of soft sets, World Academy of Science Engineering and Technology. (2011) 75, 1113–1116.
Google Scholar
9 Li F., Notes on soft set operations, ARPN Journal of systems and softwares. (2011) 1, no. 6, 205–208.
Google Scholar
10 Maji P. K., Biswas R., and Roy A. R., Soft set theory, Computers & Mathematics with Applications. (2003) 45, no. 4-5, 555–562, https://doi.org/10.1016/s0898-1221(03)00016-6, 2-s2.0-0037301143.
10.1016/S0898-1221(03)00016-6
Web of Science® Google Scholar
11 Pei D. and Miao D., From soft set to information system, Proceedings of the International Conference of Granular Computing, July 2005, Beijing, China, IEEE, 617–621.
Google Scholar
12 Sezgin A. and Atagün A. O., On operations of soft sets, Computers & Mathematics with Applications. (2011) 61, no. 5, 1457–1467, https://doi.org/10.1016/j.camwa.2011.01.018, 2-s2.0-79951724720.
10.1016/j.camwa.2011.01.018
Web of Science® Google Scholar
13 Abbas M., Guo Y., and Murtaza G., A survey on different definitions of soft points: limitations, comparisons and challenges, Journal of Fuzzy Extension and Application. (2021) 2, no. 4, 334–343.
Google Scholar
14 Maji P. K., Biswas R., and Roy A. R., Fuzzy soft sets, Journal of Fuzzy Mathematics. (2001) 9, no. 3, 589–602.
Google Scholar
15 Maji P. K., Biswas R., and Roy A. R., Intuitionistic fuzzy soft sets, Journal of Fuzzy Mathematics. (2001) 9, no. 3, 677–692.
Google Scholar
16 Maji P. K., Neutrosophic soft set, Annals of Fuzzy Mathematics and Informatics. (2013) 5, no. 1, 2287–2623.
Google Scholar
17 Smarandache F., Extension of soft set of hypersoft set, and then to plithogenic hypersoft set, Neutrosophic Sets and Systems. (2018) 22, 168–170.
Google Scholar
18 Abbas F., Murtaza G., and Smarandache F., Basic operations on hypersoft sets and hypersoft points, Neutrosophic Sets and Systems. (2020) 35, 407–421.
Google Scholar
19 Saeed M., Rahman A. U., Ahsan M., and Smarandache F., F. Smarandache, M. Saeed, M. Abdel-Baset, and M. Saqlain, An inclusive study on fundamentals of hypersoft set Theory and Application of Hypersoft Set, 2021, 1st edition, Pons Publishing House, Brussels, Belgium, 1–23.
Google Scholar
20 Ajay D. and Charisma J. J., An MCDM based on alpha open hypersoft sets and its application, Proceedings of the International Conference on Intelligent and Fuzzy Systems, August 2021, Istanbul, Turkey, Springer, 333–341.
Google Scholar
21 Debnath S., Fuzzy hypersoft sets and its weightage operator for decision making, Journal of Fuzzy Extension and Applications. (2021) 2, no. 2, 163–170.
Google Scholar
22 Yolcu A. and Ozturk T. Y., F. Smarandache, M. Saeed, M. Abdel-Baset, and M. Saqlain, Fuzzy hypersoft sets and it’s application to decision-making Theory and Application of Hypersoft Set, 2021, 1st edition, Pons Publishing House, Brussels, Belgium, 50–64.
Google Scholar
23 Yolcu A., Smarandache F., and Öztürk T. Y., Intuitionistic fuzzy hypersoft sets, Communications Faculty of Science University of Ankara Series A1 Mathematics and Statistics. (2021) 70, no. 1, 443–455, https://doi.org/10.31801/cfsuasmas.788329.
10.31801/cfsuasmas.788329
Google Scholar
24 Kamacı H. and Saqlain M., n-ary Fuzzy hypersoft expert sets, Neutrosophic Sets and Systems. (2021) 43, 180–211.
Google Scholar
25 Kamacı H., On hybrid structures of hypersoft sets and rough sets, International Journal of Modern Science and Technology. (2021) 6, no. 4, 69–82.
Google Scholar
26 Martin N. and Smarandache F., Concentric plithogenic hypergraph based on plithogenic hypersoft sets-a novel outlook, Neutrosophic Sets and Systems. (2020) 33, 78–91.
Web of Science® Google Scholar
27 Musa S. Y. and Asaad B. A., Bipolar hypersoft sets, Mathematics. (2021) 9, no. 15, https://doi.org/10.3390/math9151826.
10.3390/math9151826
Web of Science® Google Scholar
28 Lara T., Rosales E., and Sanchez J. L., New properties of m-convex functions, International Journal of Mathematical Analysis. (2015) 9, no. 15, 735–742, https://doi.org/10.12988/ijma.2015.412389, 2-s2.0-84978062387.
10.12988/ijma.2015.412389
Google Scholar
29 Lara T., Quintero R., and Rosales E., m-convexity and functional equations, Moroccan Journal of Pure and Applied Analysis. (2017) 3, no. 1, 56–62, https://doi.org/10.1515/mjpaa-2017-0005.
10.1515/mjpaa-2017-0005
Google Scholar
30 Deli I., Convex and concave sets based on soft sets and fuzzy soft sets, Journal of New Theory. (2019) 29, 101–110.
Google Scholar
31 Ihsan M., Rahman A. U., Saeed M., and Khalifa H. A. E.-W., Convexity-cum-concavity on fuzzy soft expert set with certain properties, International Journal of Fuzzy Logic and Intelligent Systems. (2021) 21, no. 3, 233–242, https://doi.org/10.5391/ijfis.2021.21.3.233.
10.5391/IJFIS.2021.21.3.233
Web of Science® Google Scholar
32 Ihsan M., Saeed M., and Rahman A. U., A rudimentary approach to develop context for convexity cum concavity on soft expert set with some generalized results, Punjab University Journal of Mathematics. (2021) 53, no. 9, 621–629.
Web of Science® Google Scholar
33 Rahman A. U., Saeed M., Arshad M., Ihsan M., and Ahmad M. R., (m, n)-convexity-cum-concavity on fuzzy soft set with applications in first and second sense, Punjab University Journal of Mathematics. (2021) 53, no. 1, 19–33.
10.52280/pujm.2021.530102
Web of Science® Google Scholar
34 Salih H. M. and Sabir P. O., Some new properties of convex and concave soft sets, Diyala Journal for Pure Sciences. (2018) 15, no. 9, 84–94.
Google Scholar
35 Rahman A. U., Saeed M., and Smarandache F., Convex and concave hypersoft sets with some properties, Neutrosophic Sets and Systems. (2020) 38, 497–508.
Google Scholar
36 Chaudhuri B. B., Concave fuzzy set: a concept complementary to the convex fuzzy set, Pattern Recognition Letters. (1992) 13, no. 2, 103–106, https://doi.org/10.1016/0167-8655(92)90040-7, 2-s2.0-38249015597.
10.1016/0167-8655(92)90040-7
Web of Science® Google Scholar
37 Majeed S. N., Some notions on convex soft sets, Annals of Fuzzy Mathematics and Informatics. (2016) 12, no. 4, 517–526.
Google Scholar

Citing Literature

All articles

[Retracted] Set-Theoretic Inequalities Based on Convex Multi-Argument Approximate Functions via Set Inclusion

Retraction(s) for this article

Retracted: Set-Theoretic Inequalities Based on Convex Multi-Argument Approximate Functions via Set Inclusion

Abstract

1. Introduction

1.1. Motivation

2. Preliminaries

3. Notions of Convexity cum Concavity for maa-Function with Relevant Set-Theoretic Inequalities

3.1. Set-Theoretic Inequalities for maa-Function

3.2. Jensen-Convex (Mid-Convex) Set and Its Inverse for maa-Function

3.3. Comparative Analysis

4. Conclusion

Conflicts of Interest

Open Research

Data Availability

References

Citing Literature

References

Information

About Wiley Online Library

Help & Support

Opportunities

Connect with Wiley

[Retracted] Set-Theoretic Inequalities Based on Convex Multi-Argument Approximate Functions via Set Inclusion

Retraction(s) for this article

Retracted: Set-Theoretic Inequalities Based on Convex Multi-Argument Approximate Functions via Set Inclusion

Abstract

1. Introduction

1.1. Motivation

2. Preliminaries

3. Notions of Convexity cum Concavity for maa-Function with Relevant Set-Theoretic Inequalities

3.1. Set-Theoretic Inequalities for maa-Function

3.2. Jensen-Convex (Mid-Convex) Set and Its Inverse for maa-Function

3.3. Comparative Analysis

4. Conclusion

Conflicts of Interest

Open Research

Data Availability

References

Citing Literature

References

Related

Information