Neutrosophic sets have recently emerged as a tool for dealing with imprecise, indeterminate, inconsistent data, while soft sets may have the potential to deal with uncertainties that classical methods cannot control. Combining these two types of sets results in a unique hybrid structure, a neutrosophic soft set (NS-set), for working effectively in uncertain environments. This paper focuses on determining operations on NS-sets through two novel norms. Accordingly, the min−norm and max−norm are well-defined here for the first time to construct the intersection, union, difference, AND, OR operations. Then, the topology, open set, closed set, interior, closure, regularity concepts on NS-sets are introduced based on these just constructed operations. All the properties in the paper are stated in theorem form, which is proved convincingly and logically. In addition, we also elucidate the relationship between the topology on NS-sets and the fuzzy soft topologies generated by the truth, indeterminacy, falsity degrees by theorems and counterexamples.
Citation: Tram B.T. Tran, My-Phuong Ngo, Quang-Thinh Bui, Vaclav Snasel, Bay Vo. A new approach for operations on neutrosophic soft sets based on the novel norms for constructing topological structures[J]. AIMS Mathematics, 2022, 7(6): 9603-9626. doi: 10.3934/math.2022534
[1] | F. Müge Sakar, Arzu Akgül . Based on a family of bi-univalent functions introduced through the Faber polynomial expansions and Noor integral operator. AIMS Mathematics, 2022, 7(4): 5146-5155. doi: 10.3934/math.2022287 |
[2] | H. Thameem Basha, R. Sivaraj, A. Subramanyam Reddy, Ali J. Chamkha, H. M. Baskonus . A numerical study of the ferromagnetic flow of Carreau nanofluid over a wedge, plate and stagnation point with a magnetic dipole. AIMS Mathematics, 2020, 5(5): 4197-4219. doi: 10.3934/math.2020268 |
[3] | Rabha W. Ibrahim, Dumitru Baleanu . Fractional operators on the bounded symmetric domains of the Bergman spaces. AIMS Mathematics, 2024, 9(2): 3810-3835. doi: 10.3934/math.2024188 |
[4] | Misbah Iram Bloach, Muhammad Aslam Noor . Perturbed mixed variational-like inequalities. AIMS Mathematics, 2020, 5(3): 2153-2162. doi: 10.3934/math.2020143 |
[5] | Mohammad Faisal Khan . Certain new applications of Faber polynomial expansion for some new subclasses of υ-fold symmetric bi-univalent functions associated with q-calculus. AIMS Mathematics, 2023, 8(5): 10283-10302. doi: 10.3934/math.2023521 |
[6] | Meraj Ali Khan, Ali H. Alkhaldi, Mohd. Aquib . Estimation of eigenvalues for the α-Laplace operator on pseudo-slant submanifolds of generalized Sasakian space forms. AIMS Mathematics, 2022, 7(9): 16054-16066. doi: 10.3934/math.2022879 |
[7] | Muhammad Amer Latif, Humaira Kalsoom, Zareen A. Khan . Hermite-Hadamard-Fejér type fractional inequalities relating to a convex harmonic function and a positive symmetric increasing function. AIMS Mathematics, 2022, 7(3): 4176-4198. doi: 10.3934/math.2022232 |
[8] | Sheza. M. El-Deeb, Gangadharan Murugusundaramoorthy, Kaliyappan Vijaya, Alhanouf Alburaikan . Certain class of bi-univalent functions defined by quantum calculus operator associated with Faber polynomial. AIMS Mathematics, 2022, 7(2): 2989-3005. doi: 10.3934/math.2022165 |
[9] | Erhan Deniz, Hatice Tuǧba Yolcu . Faber polynomial coefficients for meromorphic bi-subordinate functions of complex order. AIMS Mathematics, 2020, 5(1): 640-649. doi: 10.3934/math.2020043 |
[10] | Yuanheng Wang, Muhammad Zakria Javed, Muhammad Uzair Awan, Bandar Bin-Mohsin, Badreddine Meftah, Savin Treanta . Symmetric quantum calculus in interval valued frame work: operators and applications. AIMS Mathematics, 2024, 9(10): 27664-27686. doi: 10.3934/math.20241343 |
Neutrosophic sets have recently emerged as a tool for dealing with imprecise, indeterminate, inconsistent data, while soft sets may have the potential to deal with uncertainties that classical methods cannot control. Combining these two types of sets results in a unique hybrid structure, a neutrosophic soft set (NS-set), for working effectively in uncertain environments. This paper focuses on determining operations on NS-sets through two novel norms. Accordingly, the min−norm and max−norm are well-defined here for the first time to construct the intersection, union, difference, AND, OR operations. Then, the topology, open set, closed set, interior, closure, regularity concepts on NS-sets are introduced based on these just constructed operations. All the properties in the paper are stated in theorem form, which is proved convincingly and logically. In addition, we also elucidate the relationship between the topology on NS-sets and the fuzzy soft topologies generated by the truth, indeterminacy, falsity degrees by theorems and counterexamples.
The Faber-Krahn inequality, named also the Rayleigh-Faber-Krahn inequality, states that the ball minimizes the fundamental eigenvalue of the Dirichlet Laplacian among bounded domains with fixed volume. It was conjectured by Lord Rayleigh [1] and then proved independently by Faber [2] and Krahn [3]. One of the extensions of this inequality is the result established by B. Schwarz [4] for nonhomogeneous membranes, which is stated as follows: Let λ1(p) be principal frequency of a nonhomogeneous membrane D with positive density function p, then
λ1(p⋆)≤λ1(p), | (1.1) |
where p⋆ is the Schwartz symmetrization of p and λ1(p⋆) is the first eigenvalue of the symmetrized problem in the disk D⋆. Our aim in this paper is to give a version of the B. Schwarz inequality for the case of bounded domains completely contained in a wedge of angle πα,α≥1. Such bounded domains are called wedge-like membranes. The method for proving our result requires a weighted version of decreasing rearrangement tailored to the case of wedge-like membranes. This technique was first introduced by Payne and Weinberger [5], and then studied and used to improve many classical inequalities by several authors, see for example [6,7,8,9,10]. An interesting feature of this method is that it leads to an improvement of classical inequalities for certain domains, as shown by Payne and Weiberger [5] and Hasnaoui and Hermi [11]. Also, the results of this method have the interpretation of being the usual results in dimension d=2α+2 for domains with axial or bi-axial symmetry, see [9,11,12]. We are also interested in a new version of the Banks-Krein inequality [13] where the numerical value of the lower bound of the first eigenvalue is given by the first positive root of an equation involving the Bessel function Jα.
Before stating our results, we need to introduce some notations and preliminary tools. Letting α≥1, we will denote by W the wedge defined in polar coordinates (r,θ) in R2 by
W={(r,θ)|0<r,0<θ<πα}. | (2.1) |
For τ≥0, we define the sector of radius τ by
Sτ={(r,θ)|0<r<τ,0<θ<πα}. | (2.2) |
We also define
h(r,θ)=rαsinαθ, | (2.3) |
which is a positive harmonic function in W vanishing on the boundary ∂W. From that, we introduce the weighted measure μ defined by
μ(D)=∫Ddμ=∫Dh2dx, | (2.4) |
for all bounded domains D⊂W.
Throughout this paper, we denote by λ1(w) the first eigenvalue of the problem
P1:{Δu+λwu=0 in Du=0 on ∂D, |
where D is a bounded domain completely contained in W and w is a positive continuous function on D. It has been shown that this problem has a countably infinite discrete set of positive eigenvalues, and the first eigenvalue λ1(w) is simple and has an eigenfunction u of constant sign, see for example [14]. We will assume that u>0 in D. Hence, the first eigenfunction can be represented as
u=vh, | (2.5) |
where v is a positive smooth function vanishing on ∂D∩W.
Now, we introduce the weighted rearrangement with respect to the measure μ, which is one of the principal tools in our work. Let f be a measurable function defined in D⊂W, and let Sr0 be the sector of radius r0 such that
μ(Sr0)=μ(D). |
Furthermore, we denote the sector with the same measure as a measurable subset A of D by A⋆. The distribution function of f with respect to the measure μ is defined by
mf(t)=μ({(r,θ)∈D;|f(r,θ)|>t}),∀t∈[0,ess sup|f|]. | (2.6) |
The decreasing rearrangement of f with respect to μ is given by
f∗(0)=ess sup|f|, |
f∗(s)=inf{t≥0;mf(t)<s},∀s∈(0,μ(D)]. |
The weighted rearrangement of f is the function f⋆ defined on the sector Sr0 by
f⋆(r,θ)=f∗(μ(Sr)). | (2.7) |
An explicit computation gives that μ(Sr)=π4α(α+1)r2α+2. Substituting this in (2.7), we obtain
f⋆(r,θ)=f∗(π4α(α+1)r2α+2). | (2.8) |
Since f⋆ is a radial and nonincreasing, it follows that its level sets are sectors centered at the origin and have weighted measure equal to mf(t). We will, by abuse of notation, write f⋆(r) instead of f⋆(r,θ). Recall that w is the density of the membrane D. Let w⋆ denotes the weighted rearrangement of w, and λ1(w⋆) denotes the lowest eigenvalue of the following symmetrized problem
P2:{Δz+λw⋆z=0 in Sr0z=0 on ∂Sr0. |
The following result compares the first eigenvalue of the problem P1 with that of the symmetrized problem P2.
Theorem 2.1. If w is a positive continuous function defined on D⊂W, then
λ1(w)≥λ1(w⋆). | (2.9) |
See the following section for a proof of the theorem. Note that Theorem 2.1 includes the Payne-Weinberger inequality [5] as the special case w=1. The result above is also a new version of the B. Schwarz inequality [4] for wedge-like membranes.
To state the second result in this paper, we need to assume that there is a real number P such that 0≤w⋆≤P. From that, we introduce the function ˉw defined in Sr0 by
ˉw(r,θ)={P, for r∈[0,ρ],0, for r∈(ρ,r0], |
where ρ is chosen such that ∫Sr0w⋆dμ=∫Sr0ˉwdμ.
Theorem 2.2. Assume that 0≤w⋆≤P. The first eigenvalue of the problem P2 satisfies the inequality
λ1(w⋆)≥λ1(ˉw), | (2.10) |
where λ1(ˉw) is the first eigenvalue of the problem
P3:{Δψ+λˉwψ=0 inSr0ψ=0on∂Sr0. |
The proof of Theorem 2.2 is detailed in the third section. In fact, this result together with the corollary below extend the Banks-Krein theorem [13] to the case of wedge like membranes. The classical version of our result was first proved for vibrating strings by Krein [15] and then extended to planar domains by Banks [13].
Corollary 2.3. Let 0≤w≤P. Then,
λ1(w)≥λ1(ˉw), | (2.11) |
where λ1(ˉw) is the first positive solution of the equation
Jα(√λ1(ˉw)Pρ)+√λ1(ˉw)PραJ′α(√λ1(ˉw)Pρ)1−(ρr0)2α1+(ρr0)2α=0. | (2.12) |
See the third section for a proof of the corollary.
At the end of this section, we give an appropriate variational characterization to the eigenvalue λ1(w) for the case of wedge-like domains. To begin, consider the functional space W(D,dμ) which is the set of measurable functions ϕ satisfying the following conditions:
(ⅰ) ∫D|∇ϕ|2dμ+∫D|ϕ|2dμ<+∞.
(ⅱ) There exists a sequence of functions ϕn∈C1(¯D) such that ϕn=0 on ∂D∩W and
limn→+∞∫D|∇(ϕ−ϕn)|2dμ+∫D|ϕ−ϕn|2dμ=0. |
For more details about this space, see [9].
Lemma 2.4. The first eigenvalue of the problem P1 can be defined via the weighted variational characterization
λ1(w)=minϕ∈W(D,dμ)∫D|∇ϕ|2dμ∫Dwϕ2dμ. | (2.13) |
The proof of Lemma 2.4 is detailed in the following section.
In this part, we will prove Theorem 2.1 by showing that the weighted symmetrization decreases the numerator and increases the denominator of the Rayleigh quotient. For the numerator, we have the following weighted version of the Pólya-Szegő inequality.
Proposition 3.1. Let f be a nonnegative function in W(D,dμ). Then, f⋆∈W(Sr0,dμ), and
∫D|∇f|2dμ≥∫Sr0|∇f⋆|2dμ. | (3.1) |
The complete and detailed proof of Proposition 3.1 is given in [9] for more general cases dμ=hkdx,k>1. For the denominator, we need the following lemma.
Lemma 3.2. Let D be a bounded domain completely contained in Wand f be a μ-integrable function defined in D. Let Ω be a measurable subset of D. Then,
∫Ωfdμ≤∫Sr1f⋆dμ, | (3.2) |
where Sr1 is the sector satisfying μ(Sr1)=μ(Ω).
Proof. If g denotes the restriction of f to Ω, we have
mg(t)=μ({(r,θ)∈D;|f(r,θ)|>t}∩Ω). |
Thus, if s∈[mf(t),μ(Ω)], then mg(t)<s. Hence,
{t≥0;mf(t)<s}⊂{t≥0;mg(t)<s} | (3.3) |
and so
inf{t≥0;mg(t)<s}≤inf{t≥0;mf(t)<s}, | (3.4) |
which is exactly the inequality g∗(s)≤f∗(s).
Thus,
∫Ωfdμ=∫Ωgdμ=∫μ(Ω)0g∗(s)ds≤∫μ(Ω)0f∗(s)ds. | (3.5) |
Now, by the change of variable s=π4α(α+1)r2α+2, we have
∫Sr1f⋆dμ=∫r10∫πα0f∗(π4α(α+1)r2α+2)r2α+1sin2αθdrdθ | (3.6) |
=π2α∫r10f∗(π4α(α+1)r2α+2)r2α+1dr | (3.7) |
=∫μ(Sr1)0f∗(s)ds | (3.8) |
=∫μ(Ω)0f∗(s)ds, | (3.9) |
which proves the lemma.
Now, we are finally in a position to complete the proof of Theorem 2.1. Recall the function v defined by (2.5). Let 0≤c0≤w≤c1≤∞ and χΩ denotes the characteristic function of a subset Ω of the domain D. Then,
∫Dwv2dμ=∫Dv2h2∫c10χ{w>t}dtdx | (3.10) |
=∫c10∫Dv2χ{w>t}h2dxdt | (3.11) |
=∫c00∫Dv2χ{w>t}h2dxdt+∫c1c0∫Dv2χ{w>t}h2dxdt | (3.12) |
=c0∫Dv2h2dx+∫c1c0∫{w>t}v2h2dxdt. | (3.13) |
By Lemma 3.2 and the fact that ∫Dv2h2dx=∫Sr0(v⋆)2h2dx, we obtain
∫Dwv2dμ≤c0∫Sr0(v⋆)2h2dx+∫c1c0∫{w>t}⋆(v2)⋆h2dxdt. | (3.14) |
Now, using the equalities (v⋆)2=(v2)⋆ and {w>t}⋆={w⋆>t} in the second term on the right-hand side of the last inequality, we deduce that
∫Dwv2dμ≤c0∫Sr0(v⋆)2h2dx+∫c1c0∫{w⋆>t}(v⋆)2h2dxdt | (3.15) |
=∫Sr0w⋆(v⋆)2dμ. | (3.16) |
The last equality was obtained by applying the same computation in (3.10) to v⋆ and w⋆. Finally, using Proposition 3.1 and inequality (3.15), we obtain that v⋆∈W(Sr0,dμ) and
λ1(w)=∫D|∇v|2dμ∫Dwv2dμ≥∫Sr0|∇v⋆|2dμ∫Sr0w⋆(v⋆)2dμ≥minϕ∈W(Sr0,dμ)∫Sr0|∇ϕ|2dμ∫Sr0w⋆ϕ2dμ=λ1(w⋆). |
The proof of Theorem 2.1 is now complete.
The following lemmas are essential for the proof of our theorem.
Lemma 3.3. Let f1, f2, and Φ be μ-integrable functions over D, let Ω1={(r,θ)∈D|f1(r,θ)≤f2(r,θ)} and Ω2={(r,θ)∈D|f1(r,θ)>f2(r,θ)}, and suppose
∫Df1dμ≥∫Df2dμ. | (3.17) |
If 0≤Φ(r1,θ1)≤Φ(r2,θ2) for all (r1,θ1)∈Ω1, (r2,θ2)∈Ω2, then
∫Df1Φdμ≥∫Df2Φdμ. | (3.18) |
The proof of this lemma is similar to the proof of Lemma 2.7 in [16].
Lemma 3.4. The eigenfunction z1 corresponding to the first eigenvalue λ1(w⋆) of the problem P2 can be written as z1=ξh, where ξ is a radial function, which is radially decreasing.
Proof. By Proposition 3.1 and inequality (3.15), it follows that
λ1(w⋆)=∫Sr0|∇ξ|2dμ∫Sr0w⋆ξ2dμ≥∫Sr0|∇ξ⋆|2dμ∫Sr0w⋆(ξ⋆)2dμ. | (3.19) |
Since ξ∈W(Sr0,dμ), then ξ⋆∈W(Sr0,dμ) and is an admissible function for the weighted variational formula (2.13). Using this and inequality (3.19), we see
λ1(w⋆)=∫Sr0|∇ξ⋆|2dμ∫Sr0w⋆(ξ⋆)2dμ, | (3.20) |
which means that ξ⋆h is an eigenfunction as well. Finally, the simplicity of the first eigenvalue λ1(w⋆) implies that z1=ξh=ξ⋆h, and so ξ=ξ⋆. Thus, ξ is radial and radially decreasing. This completes the proof of the lemma.
Now, setting Ω1={(r,θ)∈Sr0|ρ<r<r0,0<θ<πα} and Ω2={(r,θ)∈Sr0|0<r<ρ,0<θ<πα}, it is not difficult to check that the functions ˉw and w⋆ satisfy the same relationship as f1 and f2 of Lemma 3.3. Also, using Lemma 3.4, we obtain that ξ satisfies the same assumption as Φ, and then
∫Sr0w⋆ξ2dμ≤∫Sr0ˉwξ2dμ. | (3.21) |
Using the above inequality, we obtain
λ1(w⋆)=∫Sr0|∇ξ|2dμ∫Sr0w⋆ξ2dμ≥∫Sr0|∇ξ|2dμ∫Sr0ˉwξ2dμ≥minϕ∈W(Sr0,dμ)∫Sr0|∇ϕ|2dμ∫Sr0ˉwϕ2dμ=λ1(ˉw). |
This completes the proof of the theorem.
Inequality (2.11) follows immediately from Theorems 2.1 and 2.2. To prove the equality (2.12), we first proceed as in [8] to obtain that the eigenfunction corresponding to λ1(ˉw) is explicitly given by ψ1(r,θ)=R(r)sinαθ, where the function R is defined on [0,r0] by
R(r)={cJα(√λ1(ˉw)Pr), for r∈[0,ρ],˜c(r−α−r−2α0rα), for r∈(ρ,r0]. |
Here, the constants c and ˜c satisfy the continuity of the function R and of its derivative. Now, since R is continuous at r=ρ, we see that
cJα(√λ1(ˉw)Pρ)=˜c(ρ−α−r−2α0ρα). | (3.22) |
The continuity of the derivative of R at r=ρ gives
c√λ1(ˉw)PJ′α(√λ1(ˉw)Pρ)=−˜cαρ(ρ−α+r−2α0ρα). | (3.23) |
Thus,
˜c=−ρα√λ1(ˉw)PJ′α(√λ1(ˉw)Pρ)1ρ−α+r−2α0ρα. | (3.24) |
Finally, plugging Eq (3.24) into (3.22), we obtain the desired result. The proof of Corollary 2.3 is now complete.
The first eigenvalue of problem P1 can be characterized by the Rayleigh principle
λ1(w)=minφ∈H10(D)∫D|∇φ|2dx∫Dwφ2dx. | (3.25) |
Let ϕ∈W(D,dμ). Using the fact that Δh=0 and the divergence theorem, we obtain
∫D|∇(ϕh)|2dx=∫D|∇ϕ|2h2+|∇h|2ϕ2+2ϕh∇h⋅∇ϕdx=∫D|∇ϕ|2h2dx. | (3.26) |
Since the function ϕh belongs to the Sobolev space H10(D), then we can use it as a test function in the Rayleigh quotient (3.25). Applying (3.26), we get
λ1(w)≤∫D|∇(ϕh)|2dx∫Dwϕ2h2dx=∫D|∇ϕ|2dμ∫Dwϕ2dμ. | (3.27) |
Now, if we write the first eigenfunction as in (2.5) and substitute it into (3.25), we obtain
λ1(w)=∫D|∇u|2dx∫Dwu2dx=∫D|∇(vh)|2dx∫Dwv2h2dx=∫D|∇v|2h2dx∫Dwv2h2dx=∫D|∇v|2dμ∫Dwv2dμ, |
which proves the lemma.
The Dirichlet eigenvalues are known only for a limited number of regions, such as disks, sectors and rectangles. This lack of information has prompted many researchers to explore methods and techniques for estimating eigenvalues. In this paper, we have proved a new lower bound for the first Dirichlet eigenvalue of an arbitrarily shaped region with continuous mass density function and completely contained in a wedge. This lower bound has been given as the lowest positive root of the Eq (2.12). In our next projects, we aim to use the method of wedge like-membranes to improve the Z. Nehari inequality [17]. Additionally, We will adapt the increasing rearrangement techniques to offer a complementary results to those of this paper. Furthermore, the generalization of all these results to higher dimensions will be considered in future works.
The authors contributed equally and they both read and approved the final manuscript for publication.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number "NBU-FPEJ-2025-2941-01".
The authors declare that there is no conflict of interest.
[1] | J. Han, J. Pei, M. Kamber, Data mining: Concepts and techniques, New York: Elsevier, 2011. |
[2] |
R. H. Hariri, E. M. Fredericks, K. M. Bowers, Uncertainty in big data analytics: Survey, opportunities, and challenges, J. Big Data, 6 (2019), 44. https://doi.org/10.1186/s40537-019-0206-3 doi: 10.1186/s40537-019-0206-3
![]() |
[3] |
L. A. Zadeh, Fuzzy Sets, Inform. Control, 8 (1965), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X doi: 10.1016/S0019-9958(65)90241-X
![]() |
[4] |
W. L. Gau, D. J. Buehrer, Vague sets, IEEE T. Syst. Man Cyber., 23 (1993), 610–614. https://doi.org/10.1109/21.229476 doi: 10.1109/21.229476
![]() |
[5] |
D. Molodtsov, Soft set theory–First results, Comput. Math. Appl., 37 (1999), 19–31. https://doi.org/10.1016/S0898-1221(99)00056-5 doi: 10.1016/S0898-1221(99)00056-5
![]() |
[6] |
F. Smarandache, Neutrosophic set–A generalization of the intuitionistic fuzzy set, 2006 IEEE International Conference on Granular Computing, 2006, 38–42. https://doi.org/10.1109/GRC.2006.1635754 doi: 10.1109/GRC.2006.1635754
![]() |
[7] |
H. Sun, W. Lv, A. O. Khadidos, R. Kharabsheh, Research on the influence of fuzzy mathematics simulation model in the development of Wushu market, Appl. Math. Nonlinear Sci., 2021. https://doi.org/10.2478/amns.2021.2.00062 doi: 10.2478/amns.2021.2.00062
![]() |
[8] |
L. Zhang, X. Tian, Application of fuzzy mathematics calculation in quantitative evaluation of students' performance of basketball jump shot, Appl. Math. Nonlinear Sci., 2021. https://doi.org/10.2478/amns.2021.1.00074 doi: 10.2478/amns.2021.1.00074
![]() |
[9] |
Y. Wang, A. O. Khadidos, The Influence of X fuzzy mathematical method on basketball tactics scoring, Appl. Math. Nonlinear Sci., 2021. https://doi.org/10.2478/amns.2021.2.00057 doi: 10.2478/amns.2021.2.00057
![]() |
[10] |
Y. Zhang, M. Cui, L. Shen, Z. Zeng, Memristive fuzzy deep learning systems, IEEE T. Fuzzy Syst., 29 (2020), 2224–2238. https://doi.org/10.1109/TFUZZ.2020.2995966 doi: 10.1109/TFUZZ.2020.2995966
![]() |
[11] |
Y. Zheng, Z. Xu, X. Wang, The fusion of deep learning and fuzzy systems: A state-of-the-art survey, IEEE T. Fuzzy Syst., 2021. https://doi.org/10.1109/TFUZZ.2021.3062899 doi: 10.1109/TFUZZ.2021.3062899
![]() |
[12] |
Q. T. Bui, B. Vo, H. A. N. Do, N. Q. V. Hung, V. Snasel, F-Mapper: A Fuzzy Mapper clustering algorithm, Knowl.-Based Syst., 189 (2020), 105107. https://doi.org/10.1016/j.knosys.2019.105107 doi: 10.1016/j.knosys.2019.105107
![]() |
[13] |
Q. T. Bui, B. Vo, V. Snasel, W. Pedrycz, T. P. Hong, SFCM: A fuzzy clustering algorithm of extracting the shape information of data, IEEE T. Fuzzy Syst., 29 (2021), 75–89. https://doi.org/10.1109/TFUZZ.2020.3014662 doi: 10.1109/TFUZZ.2020.3014662
![]() |
[14] | F. Smarandache, A unifying field in logics: Neutrosophic logic, neutrosophic set, neutrosophic probability and statistics, American Research Press, 1999. |
[15] |
M. Das, D. Mohanty, K. C. Parida, On the neutrosophic soft set with rough set theory, Soft Comput., 25 (2021), 13365–13376. https://doi.org/10.1007/s00500-021-06089-2 doi: 10.1007/s00500-021-06089-2
![]() |
[16] | B. Vo, T. Tran, T. P. Hong, N. L. Minh, Using soft set theory for mining maximal association rules in text data, J. Univ. Comput. Sci., 22 (2016), 802–821. |
[17] | P. K. Maji, A neutrosophic soft set approach to a decision making problem, Ann. Fuzzy Math. Inform., 3 (2012), 313–319. |
[18] | P. K. Maji, Neutrosophic soft set, Ann. Fuzzy Math. Inform., 5 (2013), 157–168. |
[19] |
Deli, S. Broumi, Neutrosophic soft matrices and NSM-decision making, J. Intell. Fuzzy Syst., 28 (2015), 2233–2241. https://doi.org/10.3233/IFS-141505 doi: 10.3233/IFS-141505
![]() |
[20] |
S. Jha, R. Kumar, L. H. Son, J. M. Chatterjee, M. Khari, N. Yadav, et al., Neutrosophic soft set decision making for stock trending analysis, Evolving Syst., 10 (2019), 621–627. https://doi.org/10.1007/s12530-018-9247-7 doi: 10.1007/s12530-018-9247-7
![]() |
[21] |
Arockiarani, A fuzzy neutrosophic soft set model in medical diagnosis, 2014 IEEE Conference on Norbert Wiener in the 21st Century, 2014, 1–8. https://doi.org/10.1109/NORBERT.2014.6893943 doi: 10.1109/NORBERT.2014.6893943
![]() |
[22] |
J. S. Chai, G. Selvachandran, F. Smarandache, V. C. Gerogiannis, L. H. Son, Q. T. Bui, et al., New similarity measures for single-valued neutrosophic sets with applications in pattern recognition and medical diagnosis problems, Complex Intell. Syst., 7 (2021), 703–723. https://doi.org/10.1007/s40747-020-00220-w doi: 10.1007/s40747-020-00220-w
![]() |
[23] |
F. G. Lupiáñez, On neutrosophic sets and topology, Procedia Comput. Sci., 120 (2017), 975–982. https://doi.org/10.1016/j.procs.2018.01.090 doi: 10.1016/j.procs.2018.01.090
![]() |
[24] |
T. Y. Ozturk, Some structures on neutrosophic topological spaces, Appl. Math. Nonlinear Sci., 6 (2021), 467–478. https://doi.org/10.2478/amns.2020.2.00069 doi: 10.2478/amns.2020.2.00069
![]() |
[25] |
J. C. R. Alcantud, Soft open bases and a novel construction of soft topologies from bases for topologies, Mathematics, 8 (2020), 672. https://doi.org/10.3390/math8050672 doi: 10.3390/math8050672
![]() |
[26] |
G. Cantin, C. J. Silva, Influence of the topology on the dynamics of a complex network of HIV/AIDS epidemic models, AIMS Math., 4 (2019), 1145–1169. https://doi.org/10.3934/math.2019.4.1145 doi: 10.3934/math.2019.4.1145
![]() |
[27] |
M. E. Sayed, M. A. E. Safty, M. K. El-Bably, Topological approach for decision-making of COVID-19 infection via a nano-topology model, AIMS Math., 6 (2021), 7872–7894. https://doi.org/10.3934/math.2021457 doi: 10.3934/math.2021457
![]() |
[28] |
T. Bera, N. K. Mahapatra, Introduction to neutrosophic soft topological space, OPSEARCH, 54 (2017), 841–867. https://doi.org/10.1007/s12597-017-0308-7 doi: 10.1007/s12597-017-0308-7
![]() |
[29] |
T. Bera, N. K. Mahapatra, On neutrosophic soft topological space, Neutrosophic Sets Syst., 9 (2018), 299–324. https://doi.org/10.1016/j.fiae.2017.09.004 doi: 10.1016/j.fiae.2017.09.004
![]() |
[30] |
T. Y. Ozturk, Ç. G. Aras, S. Bayramov, A new approach to operations on neutrosophic soft sets and to neutrosophic soft topological spaces, Commun. Math. Appl., 10 (2019), 481–493. https://doi.org/10.26713/cma.v10i3.1068 doi: 10.26713/cma.v10i3.1068
![]() |
[31] |
Ç. G. Aras, T. Y. Ozturk, S. Bayramov, Separation axioms on neutrosophic soft topological spaces, Turk. J. Math., 43 (2019), 498–510. doi: 10.3906/mat-1805-110
![]() |
[32] |
G. A. Çiğdem, B. Sadi, Neutrosophic soft continuity in neutrosophic soft topological spaces, Filomat, 34 (2020), 3495–3506. https://doi.org/10.2298/FIL2010495G doi: 10.2298/FIL2010495G
![]() |
[33] |
T. Y. Ozturk, A. Benek, A. Ozkan, Neutrosophic soft compact spaces, Afr. Mat., 32 (2021), 301–316. https://doi.org/10.1007/s13370-020-00827-9 doi: 10.1007/s13370-020-00827-9
![]() |
[34] |
P. Revathi, K. Chitirakala, A. vadivel, Soft e-separation axioms in neutrosophic soft topological spaces, J. Phys.: Conf. Ser., 2070 (2021), 012028. doi: 10.1088/1742-6596/2070/1/012028
![]() |
[35] |
J. C. R. Alcantud, An operational characterization of soft topologies by crisp topologies, Mathematics, 9 (2021), 1656. https://doi.org/10.3390/math9141656 doi: 10.3390/math9141656
![]() |
[36] |
J. C. R. Alcantud, T. M. Al-shami, A. A. Azzam, Caliber and chain conditions in soft topologies, Mathematics, 9 (2021), 2349. https://doi.org/10.3390/math9192349 doi: 10.3390/math9192349
![]() |