Hypersoft set theory represents an advanced version to soft set theory, offering enhanced capabilities for addressing uncertainty. By combining hypersoft set theory with nearness approximation spaces, a novel mathematical model known as near hypersoft set emerges. This hybrid model enables improved decision-making accuracy. In this study, our focus is on selecting an object from a product containing a function parameter set described by a distinct Cartesian feature with multiple arguments. Furthermore, we define fundamental features and topology on this set.
Various models have been developed to handle uncertainty, each designed for specific problem scenarios. As a result, different set concepts have been introduced. Pawlak [1] introduced the concept of rough sets, which involves examining sets based on objects and their features. Building upon this concept, Peters [2, 3] introduced near sets, which explore sets that are in proximity to each other in terms of these features. Another notable set concept is the soft set, proposed by Molodtsov [4], which has been extensively studied in both theory and practice [4-9]. The soft set concept offers flexibility in defining objects, allowing researchers to utilize various parameters according to their specific needs. Consequently, it greatly facilitates decision-making processes and finds wide-ranging applications in diverse fields [10]. Chen [11] focused on parameter reduction in soft sets and investigated problem implementations. Subsequently, Babita [12, 13] introduced the concept of soft set relation as a sub-soft set of the Cartesian product of soft sets. They also explored related concepts such as equivalent soft set relation, partition, composition, and function. By replacing the function in soft sets with a multi-row function defined by the Cartesian product of parameter sets, a complex state of the parameter set is obtained. This concept, known as hypersoft set, was introduced by Smarandache [14] in 2018. Since then, it has been extensively studied in both theory and practice [15-18]. Hypersoft set extends the concept of soft set to incorporate n different sets of parameters with multiple independent variables [14]. Hypersoft set is more adaptable than soft set and proves to be highly effective in decision-making problems. The generalization of soft sets provided by hypersoft set has attracted considerable attention, leading to investigations by researchers such as Saeed et al. [19] and Abbas et al. [20], who described and studied various operations on hypersoft sets. Moreover, hypersoft sets have recently found successful applications in numerous fields [10, 21-25].
On the other hand, Feng and Li [26] introduced a new notion by combining the concepts of soft set and near set. Similarly, Tasbozan et al. [27] integrated the concepts of near set and soft set. These concepts have further been developed and applied in the field of topology [28, 29]. Specifically, the concept of near soft set arises based on the existence of equivalence classes in soft sets within nearness approximation spaces. Accordingly, if the equivalence classes in the soft set exhibit similarity, these sets can be considered close to each other.
In this study, we provide the necessary definitions in the initial part and subsequently introduce the concept of near hypersoft sets by incorporating set characteristics that are in proximity to hypersoft sets. The set of parameters is expressed in a more complex manner as a Cartesian product. By employing this new parameter set, we can impose parameter restrictions within the nearness approximation space in the context of hypersoft set, which is a complex structure. Consequently, objects in the hypersoft set can be constrained to desired properties. By establishing the hypersoft set structure in nearness approximation spaces, we develop its topology and examine specific properties within this space. In the topology of this set, referred to as near hypersoft set, concepts such as set interior and neighborhood closure are investigated.
Preliminary
Soft sets, near soft sets and hyper soft set
Let be an objects set, be a set of parameters that define properties on objects and is the power set of .
Definition 1. A soft set(SS) over is a represented by (T, D), where D ⊆ T and [7].
Definition 2. Consider a nearness approximation space , where D is a non-empty subsets of and (T, D) is a SS over . Then, lower and upper near approximation operators are defined as follows:
and
The SSNr ((T, D)) satisfying
is called a near soft set(NSS) [27].
Definition 3. Let C1, C2, . . . , Cn be pairwise disjoint parameters, Di ⊆ Ci for i = 1, 2, . . . , n and T be a mapping given by . A pair (T, D1 × D2 × . . . × Dn) is refered to as a hypersoft set (HSS) over and (T, D) = {(β, T (β)) : β ∈ D} is a hypersoft set (T, D) [15].
Definition 4. Let τ be the collection of HSS over . τ is called a hypersoft topology on if the following conditions are satisfied:
(1) (∅ , D), ϵτ,
(2) If (T1, D) , (T2, D) ∈ τ then (T1, D) ∩ (T2, D) ∈ τ,
(3) If (Ti, D) ∈ τ then .
Then is called a hypersoft topological space over [15].
Hyper soft sets on near aproximation space
Definition 5. Let be a set of objects, D1, D2, . . . , Dn be disjoint pairwise parameters, be a nearness approximation space, Di be a non-empty subsets of , D = D1 × D2 × . . . × Dn, qi ∈ Di, i = 1, 2, . . . , 2n and [x] (qi)r be equivalence classes denoted by the subscript r for the cardinality of the restricted subset (qi) r. Consider a hypersoft set (T, D) over , where T is a mapping given by and represents the power set of . Then, the lower and upper near approximation operators are defined as follows:
and
The near hyper soft set denoted by is the Nr ((T, D)) satisfying
Example 6. Let represent a set of five individuals and be a set of parameters, where D1, D2, D3, D4 represent tall, IQ, weight and strong, respectively. Instance values of the qi, i = 1, 2, 3, 4 functions are as follows:
Let (K, D) be defined as
representing a SS. Then,
for q2 ∈ D and
for D1, D2 ∈ D.
The pair (K, D) is a NSS since it satisfies
Properties of objects
y1
y2
y3
y4
y5
b11
1
0
0
1
0
b12
1
0
0
1
0.5
b21
0
1
0
0
1
b22
0
1
1
0
1
Let b11, b12, b21, b22 stand for taller than 1.70, shorter than 1.70, greater than 100, less than 100 respectively where D1 = {b11, b12}, D2 = {b21, b22} and
Let D = D1 × D2, qi ∈ D where
and T is a mapping given by
can be written. Then
is a where
and
Then (T, D) hyper soft set is a near hyper soft set since BndNr(D) ((T, D)) ≥0 is satisfied.
Definition 7. Let and be a defined over a common universe . We define as a near hypersoft subset(NHSs) of , if the following conditions hold:
(1) A ⊆ D,
(2) T (β) ⊆ L (β) for all β ∈ A.
We denote this relationship as . is considered a superset of , if is a subset of (T, A).
Definition 8. Let be a mapping such that for all β ∈ D. Then is called the complement of .
Definition 9. If for all β∈ D, T (β) = ∅ then is referred to as a relative null , denoted as (∅ , D).
Definition 10. A defined over is considered a relative whole , denoted as , if for all β ∈ D, .
Definition 11. The difference of and , defined over a common universe , is defined as
where C = A ∩ D and for all β ∈ C,M (β) = T (β) ∖ L (β). is also considered a .
Definition 12. The union of and , defined over a common universe , is defined as , where C = A ∩ D and for all β ∈ C, M (β) = T (β) ∪ L (β). is also a .
Definition 13. The intersection of and , defined over a common objects , is defined as where C = A ∩ D and for all β ∈ C, M (β) = T (β) ∩ L (β). is also a .
Definition 14. Let be a over and Y≠ ∅ be a subset of . The of over T, denoted as (TY, D) is defined as TY (β)= T ∩ Y (β) for each β ∈ D. In other words .
Definition 15. Let be the collection of over . If the following conditions satisfies:
(1) , ,
(2) If , then ,
(3) If then ,
then is referred as a near hypersoft topological space over and the members of are called near hyper soft open sets in .
Example 16. Let represent a set of five individuals and D = {D1, D2} as in Example 6. and are over , defined as follows:
and
Thus, we write
Thus, the collection forms a on .
Definition 17. Let be a over . If , then is referred to as a near hypersoft closed set in .
Definition 18. Let be an initial universe, D be the set of parameters, , and be the collection of all s that can be defined over . and are referred to as the indiscrete topology and discrete topology on , respectively.
Definition 19. Let and be over . If , then is said to be finer than .
Definition 20. Let be a over , be a over and n∈ . Then is said to be a neighborhood of n if there exists a that is a such that n∈ .
Proposition 21. Let be a over , then
(1) If is a of n∈ , then n∈ ,
(2) Each n∈ has a ,
(3) If and are s of some n∈ , then ∩ is also a of n,
(4) If is a of n∈ and , then is also a of n∈ .
Proof. (1) This follows directly from the definition 20.
(2) For any n∈ , n∈ and since , so n∈ which means that is a of n.
(3) Let and be the of n∈ . Then there exist , ∈ such that and n∈ . Since n∈ and , it implies that n∈ and ∈ .
Therefore, we have n∈ , D)∩ ⊆ . Thus, is a of n.
(4) Let be a of n∈ and . By definition 20, is a such that n∈ ⊆ . Thus, n∈ ⊆ . Therefore, is a of n. ■ Definition 22. Let be a over and . Then {: is a topology on Y.
Definition 23. Let be a and be a over . Then
is called the near hypersoft closure of .
Proposition 24. Let be a space and be a over . Then
(1) is the smallest containing ,
(2) is a if and only if .
Proof. (1)It is obvious from the definition 23.
(2) Let be a . So, is the smallest over , contains and hence . Contrarily, suppose that . By (1), is a , so is a over . ■ Proposition 25. Let be a over and , be over . Then
(1)
and
,
(2) ,
(3)
implies
,
(4) = ∪ ,
(5) ⊆ ∩ ,
(6) = .
Proof. (1) and (2) are obvious from the definition 23.
(3) From (2), ⊆ . Since , we have . But is a . is a containing . Since is the smallest over containing , so we have ⊆ .
(4) Since
and
from (3), we have
and
Hence,
Now, since and are s, ∪ is also . Also,
and
implies that
Thus, ∪ is a containing ∪ .
Since is the smallest containing ∪ , we have
Hence,
(5) Since
∩ ⊆
and
∩ ⊆ , then
and
Therefore,
(6) Since is a , therefore from (2), we have
■ Example 26. Let us consider the near hypersoft topological space in example 16 and let be near hypersoft set defined as follows:
Then
are near hypersoft open set and near hypersoft closed sets, respectively. Thus, we write
Hence
Definition 27. Let be a over , be a over and n∈ . If there exists a which is a such that ⊆ , then n is said to be a interior point of .
Definition 28. Let be a over . Then
is called interior of .
Proposition 29. Let be a and be a over . Then
(1) is the largest contained in ,
(2) is a if and only if .
Proof. (1) It is obvious from the definition 28.
(2) Let be a . Then is the largest subset of . But from (1), is the largest subset of . Hence, = . Conversely, let = . By (1), is a and therefore is also . ■ Proposition 30. Let be a space over and let be over . Then
(1) and ,
(2) ,
(3) implies ,
(4) ,
(5) ,
(6) .
Proof. (1) It is obvious from the definition 28.
(2) Let n∈ , then n is a interior point of . This implies that is a of n. Then n∈ . Hence, .
(3) Let . Then n is a interior point of and so is a of n. Since ⊆ , is also is a of n. This implies that n∈ . Thus,
(4) Since
and
from (3),
and
This implies that ⊆ . Let . Then and . Hence n is a interior point of each of the near hypersoft sets and . It follows that and are of n so that their intersection is also is a of n. Hence, n∈ . Thus,
Therefore,
(5) From (3), ⊆ implies
and ⊆ implies
Hence, ⊆
(6) From is the . Hence, from (2) of the same proposition, . ■ Definition 31. Let be a over and be a over . Then ⊆ .
Proposition 32. Let be a over and be over . Then
(1) ,
(2) = ,
(3) =,
(4) = ,
(5) ⊆ ∖ .
Proof. (1) From the definition 23, we get
(2)From the definition 8, we get
(3) It can be proved similarly from the definitions 8, 23 and 28.
(4) It can be proved similarly from the definitions 8, 23 and 28.
(5) From the definitions 8 and 28, we get
■ Example 33. Let us consider the near hypersoft topological space and near hypersoft set in example 26.
Definition 34. Let be a over , then which is a boundary of is denoted by and is defined as
Proposition 35. Let be a over and be a over . Then
(1) ⊆ ,
(2) ⊆ ,
(3)
(4) ,
(5) ⊆ .
Proof. (1) From
hence,
(2) From the definition 34, we get
(3) From the definition 34, we get
(4)From the definition 34, we get
(5)From the definition 34, we get
■ Example 36. Let us consider the near hypersoft topological space in example 16 and let , , be near hypersoft sets defined as follows:
Then
are near hypersoft open set and near hypersoft closed sets, respectively. Thus, we write
Hence
and
where
For , we can write
Similarly, the accuracy of all propositions in the article can be demonstrated.
Conclusions
In this study, we have explored hyper-soft sets in nearness approximation spaces and introduced the concept of near hypersoft sets. By considering the Cartesian product of features, we can identify objects that possess specific features from a larger set of objects. This clustering approach enables the identification of objects with similar properties, allowing us to select the best products that meet our desired complex features. Consequently, we have gained the ability to make informed choices based on our specific needs. Moreover, we have established a topology on this space and derived several definitions and propositions.
By conducting a thorough investigation into hypersoft sets within approximation spaces, we have successfully introduced the concept of near hypersoft sets. Utilizing the Cartesian product of features, we have devised a method to identify objects possessing specific features among a diverse range of objects. This clustering approach has facilitated the grouping of objects with similar properties, enabling us to select the most suitable products that align with our desired complex features. As a result, we have gained the capability to make targeted choices according to our specific requirements. Additionally, we have established a topology within this space and formulated various definitions and propositions.
References
1.
PawlakZ.Rough sets, International Journal of Information andComputer Science11 (1982), 341–356.
2.
PetersJ.F.Near sets, Special theory about nearness of objects, Fundamenta Informaticae75 (2007), 407–433.
3.
PetersJ.F.Near sets, General theory about nearness of objects, Applied Mathematical Sciences1 (2007), 2609–2629.
4.
MolodtsovD.Soft set theory-first results, Computers andMathematics with Applications37 (1999), 19–31.
5.
MajiP.K., BiswasR., RoyA.R.Soft set theory, Computersand Mathematics with Applications45 (2003), 555–562.
6.
ZorlutunaI., AkdagM., MinW.K., AtmacaS.Remarks on softtopological spaces, Annals of Fuzzy Mathematics andInformatics3 (2012), 171–185.
7.
CagmanN., KarakasS., EnginogluS.Soft topology, Computers and Mathematics with Applications62 (2011), 351–358.
8.
AliM.I., FengF., LiuX., MinW.K., ShabirM.On some newoperations in soft set theory, Computers and Mathematics withApplications57 (2009), 1547–1553.
9.
SezginA., AtagunA.O.On operations of soft sets, Computers and Mathematics with Applications61 (2011), 1457–1467.
SagvanM.Y., AsaadB.A.Topological structures via bipolarhypersoft sets, Journal of Mathematics2022 (2022).
19.
SaeedM., AhsanM., SiddiqueM., AhmadM.A study of thefundamentals of hypersoft set theory, International Journal ofScientific Engineering and Research11 (2020), 230.
20.
AbbasM., MurtazaG., SmarandacheF.Basic operations onhypersoft sets and hypersoft point, Neutrosophic Sets and Systems35 (2020), 407––421.
21.
RahmanA., SaeedM., HafeezA.Theory of bijective hypersoft setwith application in decision making, Punjab University Journalof Mathematics53 (2021), 7.
22.
SaeedM., RahmanA., AhsanM., SmarandacheF.An inclusive studyon fundamentals of hypersoft set, Theory and Application ofHypersoft Set1 (2021), 1–23.
23.
MusaS.Y., AsaadB.A.Connectedness on bipolar hypersofttopological spaces, Journal of Intelligent and Fuzzy Systems43 (2022), 4095–4105.
24.
YolcuA., SmarandacheF., OzturkT.Y.Intuitionistic fuzzyhypersoft sets, Communications Faculty of Sciences Universityof Ankara Series A1: Mathematics and Statistics70 (2021), 443–455.
25.
RahmanA.U., SaeedM., SmarandacheF.Convex and concavehypersoft sets with some properties, Neutrosophic Sets andSystems38 (2020), 497–508.
TasbozanH., IcenI., BagirmazN., OzcanA.F.Soft Sets and SoftTopology on Nearness approximation spaces, Filomat31(2017), 4117–4125.
28.
TasbozanH., BagirmazN.Near Soft Continuous and Near SoftJP-Continuous Functions, Electronic Journal of MathematicalAnalysis and Applications9 (2021), 16–171.
29.
TasbozanH.Near Soft Connectedness, Afyon Kocatepe UniversityJournal of Science and Engineering20 (2020), 815–818.