q-Rung Orthopair Fuzzy Matroids with Application to Human Trafficking

*e theory of q-rung orthopair fuzzy sets (q-ROFSs) is emerging for the provision of more comprehensive and useful information in comparison to their counterparts like intuitionistic and Pythagorean fuzzy sets, especially when responding to the models of vague data with membership and non-membership grades of elements. In this study, a significant generalized model q-ROFS is used to introduce the concept of q-rung orthopair fuzzy vector spaces (q-ROFVSs) and illustrated by an example. We further elaborate the q-rung orthopair fuzzy linearly independent vectors. *e study also involves the results regarding q-rung orthopair fuzzy basis and dimensions of q-ROFVSs.*emain focus of this study is to define the concepts of q-rung orthopair fuzzymatroids (q-ROFMs) and apply them to explore the characteristics of their basis, dimensions, and rank function. Ultimately, to show the significance of our proposed work, we combine these ideas and offer an application. We provide an algorithm to solve the numerical problems related to human flow between particular regions to ensure the increased government response action against frequently used path (heavy path) for the countries involved via directed q-rung orthopair fuzzy graph (q-ROFG). At last, a comparative study of the proposed work with the existing theory of Pythagorean fuzzy matroids is also presented.


Introduction
Graph theory and combinatorial geometry are known to have a lot of common grounds, particularly with regard to their basic concepts. Making use of these similarities, a host of research has been conducted for further exploration and development of these fields. Whitney [1] was the one to initiate the fundamental concept of matroids. By doing so, he laid the foundation of an extremely vast field of matroid theory that connected several basic tools like linear algebra, graph, and combinatorial theory. is matroid theory has been widely applied by researchers in different scientific areas.
Zadeh [2], in 1965, for the first time introduced fuzzy logics and defined fuzzy sets (FSs). ese sets were known for real-life data, uncertainties, and vague information. Soon after its introduction, fuzzy set theory became popular among researchers and came up as a new field. Later, Attanssov [3] expanded the concept of FSs and introduced the intuitionistic fuzzy sets (IFSs) with the help of membership and non-membership values of elements, the sum of which was not being more than 1. ese IFSs are effectively applied in theoretical as well as practical problems such as optimization, decision making, and graphical ones in numerous fields. e idea of these sets was further extended by Feng et al. [4] to give intuitionistic fuzzy soft sets (IFSSs).
ey also presented several new operations to generalize the concept of intuitionistic fuzzy soft sets (IFSSs). While solving some decision-making problems, it was observed that the sum of both membership and non-membership values of elements exceeded one; however, some of their squares remained less than one. To overcome such issues, Yager [5,6] put forward the idea of IFSs with the introduction of new Pythagorean fuzzy sets. Some useful notions and results for FSs, IFSs, PFSs, and other types of fuzzy sets have been presented in the literature [7][8][9][10][11][12][13][14]. It seems difficult to solve the problems when the sum of the square of the membership and non-membership values of elements exceeds 1. We are unable to handle such kind of information by means of PFSs. Yager [15] introduced q-ROFSs in which the sum of the qth power of membership and nonmembership values of elements is bounded by one. After that, q-ROFSs are frequently used in decision making as q-ROFSs widened the range of acceptable pairs rather than IFSs and PFSs with the parameter "q" adjustment. Recently, Garg used q-ROFSs to introduce a novel concept of connection number-based q-rung orthopair fuzzy set (CN-q-ROFS), defined some operation laws, and proposed a method to handle multiattribute group decision-making (MAGDM) problems [16]. Subsequently, in [17], he introduced the idea of q-connection numbers for interval-valued q-rung orthopair fuzzy set and used it to develop a method for solving multiattribute group decision-making (MAGDM) problems. At present, several studies paid close attention to the information regarding q-ROFSs and provided different novel methods [18][19][20][21]. e graphical representation of objects has been a subject of great interest for scientists. Recently, a number of studies have involved both fuzzy and graph theories to deal with the optimization related problems in the presence of vague data. e idea of fuzzy graphs came from Kaufmann [22], while some basic concepts related to fuzzy graphs, such as cycles and paths, were characterized by Rosenfield et al. [23]. Akram and Naz [24] further used these concepts and proposed a new work to find the energy of PFGs with their applications. eir work was mainly focused on operations of fuzzy graphs (FGs), IFGs, PFGs, and their different types. ey also provided q-ROF competition graphs and studied their applications. Sitara et al. [25] introduced the notion of q-rung picture fuzzy graph structures and provided an algorithm to describe their proposed model. e refining of the idea of hypergraphs given by Kaufmann was carried out by Lee-Kwang and Lee. In addition, different researchers investigated numerous features of FGs and fuzzy hypergraphs based on different FSs [26][27][28][29][30][31]. In 1988, the concept of matroids in terms of FSs was linked and defined as G-V fuzzy matroids by Goetschel and Voxman [32]. Later on, bases and circuits of the fuzzy matroids were also defined by them [33][34][35][36]. As time progressed, different FSs were used to define different fuzzy matroids, and their properties were also discussed by different researchers [37][38][39][40][41][42]. Recently, we proposed the idea of Pythagorean fuzzy matroids (PFMs) and described their application to decision making [43]. A lot of work based on FSs, IFSs, and PFSs regarding matroid theory has been discussed in the literature, but matroids based on q-ROFSs are still unattended. e existing models, namely, IFMs and PFMs, are insufficient to deal with different decision-making problems which contain membership and nonmembership values of elements whose sum of their squares is greater than 1. is drawback of existing structures motivates us to present this work. e motivations of our work are as follows: (1) e q-ROFS is a generalized form of some existing models, including IFSs and PFSs. On setting q � 1 and q � 2, we get IFSs and PFSs, respectively, as special cases of q-ROFSs. (2) e existing IFMs and PFMs fail to deal with the information involving membership and nonmembership values whose sum of their squares is not less than 1. (3) Due to the more flexible approach of q-ROFSs, the developed q-ROFMs can solve many decisionmaking problems and overcome deficiencies of existing models such as IFMs and PFMs.
e main contributions of this work are as follows: (1) Our work illustrates q-ROFVSs with an illustrative example. (2) Most importantly, the notion of q-ROFMs is defined and characterized with its basis and dimension. (3) is study also provides various results regarding q-ROFMs. (4) Ultimately, an algorithm is developed to find an optimal solution along with a particular application. (5) To check the validity of our proposed work, a comparative analysis with an existing model is also given.
In this work, we present the idea of q-ROFVSs with a numerical example and discuss their bases and dimensions. We also discuss the q-rung orthopair fuzzy linearly independent vectors. We further combined the q-ROFSs with the fuzzy matroids and named them as q-ROFMs. We investigate the concepts of circuits, basis, and rank for q-ROFMs. Note that for q � 1 and q � 2, our proposed q-ROFMs are reduced to IFMs and PFMs, respectively. We also proposed an application of our work regarding human trafficking between different regions which supports them to find a heavy path used by the traffickers so that they can increase their government response action against this path by using a directed graph having q-rung orthopair fuzzy information. In the end, we give concluding remarks with some of the future directions. e contents of this article are summarized as follows. In Section 2, we recall some fundamental definitions including crisp matroids with rank function, q-ROFSs with their score functions, and some basic operations defined on q-ROFSs. In Section 3, we first propose q-ROFVSs and then q-ROFMs. We also discuss some of their basic properties in this section. In Section 4, we explore an application and develop an algorithm to illustrate the importance of our work. In Section 5, we provide the numerical comparison of our developed algorithm with the existing PFM approach [43]. In Section 6, we provide some conclusive remarks with future directions.

2
Discrete Dynamics in Nature and Society

Preliminaries
Our interest in this section is to discuss the theory of matroids and valuable concepts related to matroid theory to understand the proposed work better. Although matroids are defined differently by using various sets, here we write a simple definition of crisp matroids.
Definition 1 (see [1]). Let A ≠ ϕ be a set of finite elements and P(A) denote the power set of A. For I ⊂ P(A), a nonempty family of subsets, the pair M � (A, I) is called a matroid (or crisp matroid) if it satisfies the following: (1) ϕ ∈ I.
(3) If I 1 , I 2 ∈ I with |I 2 | < |I 1 |, then another subset I 3 ∈ I exists such that I 2 ⊂ I 3 ⊆ I 1 ∪ I 2 , where |I| shows the number of elements of I.
e element I ∈ I is called independent set in M. Also, I is known as maximal independent in M if we do not have such I ′ ∈ I that contains I.
Definition 2 (see [1]). Suppose that M is a matroid and I ∈ I. If I is maximal independent in M, then I is called base of M and B(M) represents the family of all bases. (1) en, R A is called rank function of M.
Definition 5 (see [2]). Consider a non-empty set X. e fuzzy set F is defined as (2) e mapping σ F : X ⟶ [0, 1] assigns the membership value of x ∈ X to F and FS(X) represents the family of all fuzzy sets on X.
Definition 6 (see [32]). Let X be a non-empty finite universe of discourse and F ⊆ FS(X). For any fuzzy sets F 1 , F 2 , F 3 ∈ FS(X), the collection F satisfies the following: for any x ∈ X, and union is defined as e pair FM(X) � (X, F) is called a fuzzy matroid and F is the subfamily of all independent FSs of the matroid FM(X).

q-Rung Orthopair Fuzzy Vector Spaces
is section illustrates the concept of q-ROFVSs with their basis and dimension and presents q-rung orthopair fuzzy linearly dependent and independent vectors. Here, we also present matroids based on q-ROFSs and discuss their properties regarding circuits, basis, and their rank function. Katsaras and Liu [44] introduced the hybrid concept of fuzzy vector spaces and discussed their characteristics. Later, many researchers applied different fuzzy sets to the elementary concepts of vector spaces. Here, we use q-ROFSs to generalize the Pythagorean fuzzy vector spaces [43] and define q-ROFVSs.
Definition 11. Let X be a non-empty finite vector space over the field F .
for scalars a, b ∈ F and for any x, y ∈ X, we have Here, the set of all q-ROFVSs over X is denoted by the pair X � (X, ζ).
e following proposition illustrates that membership and non-membership functions assign unchanged values under scalar multiplication in q-ROFVSs. Proposition 1. Let X � (X, ζ) be a q-ROFVS. e following two properties hold for each x, y ∈ X: Proof. e proof of properties (15) and (18) is very straightforward (see Definition 11). □ Proposition 2. Let x, y ∈ X with ζ + (x) ≠ ζ + (y) and ζ − (x) ≠ ζ − (y); then, we have Proof. To prove, from Definition 11, let a � b � 1 and hence □ Definition 12. Let X be a non-empty finite universe and X be a q-ROFVS over F. en, the set of vectors . , x r is linearly independent.
(2) For any a 1 , a 2 , . . . , a r ⊂ F, we have 4 Discrete Dynamics in Nature and Society . en, the set α i r i�1 is linearly and q-ROF linearly independent.
Proof. By using the induction on r, the statement is true for r � 1. We suppose that the statement is true for r.
and contradicts that α i r+1 i�1 has distinct values and hence is linearly independent. Propositions 1 and 2 show that α i r i�1 is q-ROF linearly independent. □ Definition 13. Let X � (X, ζ) be a q-ROFVS and B � β j r j�1 , where each β j ∈ X. en, the set B is called q-ROF basis in X, if it satisfies (1) e set B is basis in X.
(2) For scalars a 1 , a 2 , . . . , a r ⊂ F, we have Definition 14. Let X ≠ ϕ and X be a q-ROFVS having basis B. en, the dimension of q-ROFVS is given by It is easy to see that dim q is a function from the class of all Proof. We use Proposition 2: Since ζ + (x) > ζ + (y), then Now we write which implies the result Since ζ + (x) > ζ + (y), then which proves that ζ + (x + y) � ζ + (y). Similarly, we use Proposition 2: Since ζ − (x) < ζ − (y), then Now we write which implies the result Since which proves that ζ − (x + y) � ζ − (y). e following example illustrates Definition 11 clearly.

Proposition 5.
Let X � (X, ζ) be a q-ROFVS and Q be a subset of Q(X) containing q-ROF linearly independent column vectors in X. e pair (X, Q) is a q-ROFM on X.
Proof. Suppose that X is a non-empty set containing column labels of a q-ROF matrix, and ζ x represents a q-ROF submatrix containing those columns which are labeled in X. Consider a set Q of q-ROF linearly independent column vectors of ζ x , i.e., For any submatrix ζ . It is easy to see from Definitions 11 and 15 that (X, Q) is OM(X).
Note that σ ∉ Q is called dependent q − ROFS. Note that the elements of Q c (QM) follow the properties: (1) ϕ ∉ Q c (QM).
Definition 17. Let QM(X) � (X, Q) be a q-ROFM. Consider an element ζ i ∈ Q; then, ζ i is called maximal independent set in a matroid QM(X) if there does not exist ζ j ∈ Q that contains ζ i . A maximal independent set in QM(X) is called q-ROF base or basis of QM(X). e collection of all q-ROF basis is defined as Note that although q-ROF basis contains all the independent sets in QM(X), there exist some QM(X) that do not have q-ROF basis. 6 Discrete Dynamics in Nature and Society Example 2. Let Q(X) be a family of all q-ROFSs defined on a non-empty set X. en, for a positive integer i and |X| � j with i ≤ j, the set Q is defined as e pair (X, Q) � (UF) i j is called q-ROF uniform matroid. Note that the subfamilies of all q-ROFSs of X with the sizes i + 1 and i are called the q-ROF circuits and q-ROF basis of (UF) i j , respectively.

Definition 20.
e q-cut level set for 0 < q ≤ 1 of a q-ROFS ζ ∈ Q(X) is a crisp set which is defined as follows: Theorem 1. Let QM(X) � (X, Q) be a q-ROFM and Q q be a collection of all q-cut levels of q-ROF independent sets where 0 < q ≤ 1, i.e., en, M q � (X, Q q ) is a crisp matroid on X.
Proof. e proof is very straight forward from Definition 20, and Q q is a collection of crisp subsets of X. en, for each 0 < q ≤ 1, we have M q � (X, Q q ). □ Definition 21. Let X ≠ ϕ be a finite universe and QM(X) be a q-ROFM. en, we have a finite sequence q 1 < q 2 < · · · < q n such that (1) q 0 � 0, q n ≤ 1.
(2) Q r is non − empty, if 0 < r ≤ q n , and Q r is empty, if r > q n .

Corollary 1. From eorem 1 and Definition 21, for
Theorem 2. Let 0 � q 0 < q 1 < q 2 < · · · < q n ≤ 1 be a finite fundamental sequence and (X, Q q 1 ), (X, Q q 2 ), . . . , (X, Q q n ) be finite sequence of crisp matroids regarding this fundamental sequence. For each q i− 1 < q ≤ q i (i � 1, 2, . . . , n), we assume Q q � Q q i and for q n < q ≤ 1, Q q � ϕ. en the pair Proof. It is easy to see that ϕ ∈ Q as Q q � ϕ for q n < q ≤ 1. Now, assume that ζ 1 ∈ Q, and ζ 2 ∈ Q(X) such that ζ 2 ⊆ ζ 1 . It is clear from definition of Q that for each 0 < q ≤ 1, Q q (ζ 1 ) ∈ Q q , so Q q (ζ 2 ) ⊆ Q q (ζ 1 ), and since we have that (X, Q q ) is a crisp matroid for Q q , it means Q l (ζ 2 ) ∈ Q l , and hence ζ 2 ∈ Q and proves (18) of Definition 15. Now, let It is easy to observe from definition of η that Q η contains the support of both ζ 1 and ζ 2 . Note that Q η contains independent subsets; then, there exists an independent subset I ∈ Q η satisfying (1) I contains supp(ζ 2 ), for all x ∈ X.
Let us define ζ 3 as which shows that ζ 3 is q − ROFS and satisfies (20) Proof. It is easy to deduce from definition of Q that Q ⊂ Q. For ζ ∈ Q, let ] i t i�1 be a non-zero q-rung orthopair fuzzy range with ] i � (] * i , ] i ′ ) and order ] 1 > ] 2 > · · · > ] t > 0. One can notice that for each 1 ≤ i ≤ t and ζ ∈ Q, we have that To prove Q ⊂ Q, we define a q-rung orthopair fuzzy set φ ∈ Q(X) for each 1 ≤ i ≤ t and u ∈ X as Discrete Dynamics in Nature and Society 7 Here to show ζ ∈, we use induction process. For each 1 ≤ i ≤ t, we consider Using the induction method, we have supp( If n l− 1 + 1 � n l , then we have that ∪ l i�1 φ i is also an independent set in QM. But, if n l− 1 + 1 < n l , then to move further, we define q-ROFS as From Definition 19, Φ 2 ∈ Q(X), and from Φ 2 ⊂ φ l , Φ 2 ∈ Q is an independent set in QM. Similarly, define another q-ROFS as is also an independent set in QM for n l− 1 + 1 � n l . But, if n l− 1 + 1 < n l , then to proceed further, we obtain a new q-ROFS e next result is the direct consequence of eorem 1.4 discussed in [34]. en, ζ ∈ Q(X) if and only if for each q ∈ R + (ζ) we have Q q (ζ) ∈ Q q . Theorem 4. Let ζ ∈ Q be a q-rung orthopair fuzzy base of a q − ROFM (X, Q). en, for each x ∈ X, ϱ ζ (x) � 0.

Case Study: Human Trafficking.
With a rapid increase in the population of different continents, including Asia and Africa, it is unable to stop human trafficking. Many reasons are behind this, such as poverty, greed for a handsome job, unemployment, low literacy rate, labor, and sexual exploitation. Studies show that millions of people are trapped in this modern slavery, and it is a dilemma that people do not realize they are getting trapped due to unawareness. Still, we are unable to get exact statistics due to unreported cases of human trafficking. An important application is to study human trafficking in different countries and provide a guess about a more suitable way or path that the traffickers can use.
Consider n and m number of countries, say C 1 , C 2 , . . . , C n and C 1 ′ , C 2 ′ , . . . , C m ′ , respectively. e traffickers will move from C 1 to any of the other C i countries. However, C 1 is not connected with other C i 's directly or the human trafficking ratio is almost ignorable between them due to multiple factors. e traffickers move in groups and collect their other victims from the countries C j ′ where j � 1, 2, . . . , m, to move any one of the countries C i where i � 2, 3, . . . , n. If C 1 is the starting point, they will definitely move to any of the other C i (where i � 2, . . . , n) for their next station. e state agencies seek to solve this puzzle used by 8 Discrete Dynamics in Nature and Society the traffickers for trafficking across different stations. e procedure to track their way or path regarding given information is described in Algorithm 1.
We take some data to find the illegal immigration routes used by traffickers for human trafficking. is model is in the form of directed fuzzy graphs. e edge between the two (1) Input: (i) Consider two finite sets of countries C 1 , C 2 , . . . , C n and C 1 ′ , C 2 ′ , . . . , C m ′ , and each C i and C j ′ mark a vertex of the graph G.
(ii) Mark all the edges and give the direction between the countries regarding given q-rung orthopair fuzzy information and given problem, that is, for each 1 ≤ i ≤ mn, a i (ζ + i , ζ − i ) represents a directed edge that represents the flow between two countries. ere is no edge between any of the C i 's and also no edge between C j 's.
(2) Calculate the score function S q (a i ) � (1/2)(1 (3) Find all the edge sets of length mn − 1 and remove all the cycles of length mn − 1. mn and l � 1, 2, . . . , n m m n . (5) Determine B′ by removing all X l from B such that X l 's are not spanning paths (the path runs through each vertex of the graph exactly once). (6) Reduce the set B′ to B″ by removing all the spanning paths from B′ which are not the cases according to the directions given in the data.
where each a i k Output: in the last step, the heaviest path will help us to take suitable measures according to flow of human trafficking given in the data. ALGORITHM 1: Selection of the heaviest path. Table 1: 3-Rung orthopair fuzzy information of human trafficking between regions from Figure 1.

Serial no.
Connections  Figure 1: q-Rung orthopair fuzzy graph representation of human trafficking data.
Discrete Dynamics in Nature and Society countries is represented by the q-rung fuzzy information. e membership part shows the ratio of human trafficking from one country to another.
e non-membership part shows parameters of measurements taken by the concerned governments to stop human trafficking (government response action to reduce flow). We use the data from the model given by Mordeson and Mathew [45]. e data of illegal human flow between different regions are given in Table 1 [45]. In this study, we consider five regions: the Middle East (V 1 ), West Central Europe (V 2 ), East Asia and 5 and E � a 1 , a 2 , a 3 , a 4 , a 5 , a 6 represent the set of vertices and directed edges of graph G, respectively (see Figure 1). We can analyze the flow of human trafficking by these directed edges. Our task is to find a frequently used path by the traffickers so that relevant governments can take some measures to stop this abuse. We calculate the score functions of all the given q-ROF information (see Table 1). To engage all the five concerned governments, it is easy to see from Figure 1 that four edges need to pass through all vertices, and such total possibilities are 15. However, cycles of length four cannot be the choice while solving such a puzzle. e remaining 12 edge sets are maximal independent sets that can be useful to find the most used path by the traffickers. We say B, i.e., B � a 1 , a 2 , a 3 , a 5 , a 1 , a 2 , a 3 , a 6 ,  a 1 , a 2 , a 4 , a 5 , a 1 , a 2 , a 4 , a 6 , a 2 , a 3 , a 4 , a 5 , a 2 , a 3 , a 5 , a 6 ,  a 2 , a 3 , a 4 , a 6 , a 2 , a 4 , a 5 , a 6 , a 1 , a 3 , a 4 , a 5 , a 1 , a 3 , a 5 , a 6 , a 1 , a 4 , a 5 , a 6 , a 1 , a 3 , a 4 , a 6 }. In the next step, the set B reduces to B ′ by removing maximal independent sets which are not spanning paths, i.e., B ′ � a 1 , a 2 , a 3 , a 6 , a 1 , a 2 , a 4 , a 5 , a 2 , a 3 , a 4 , a 5 , a 2 , a 3 , a 5 , a 6 , a 1 , a 4 , a 5 , a 6 , a 1 , a 3 , a 4 , a 6 }. However, the graph shown in Figure 1 is directed, so four more maximal independent sets are deleted due to absurdity in these cases. So, only T 1 � a 2 , a 3 , a 4 , a 5 and T 2 � a 1 , a 4 , a 5 , a 6 are the cases. Table 2 shows that T 2 � a 1 , a 4 , a 5 , a 6 is the heaviest path with the score function 2.436. We find that given five countries should increase their government response action against this obtained path used frequently by the traffickers. ey should take some measures to minimize human trafficking.

Comparison of Given Model with Intuitionistic and Pythagorean Fuzzy Models
IFSs and PFSs are known to be the special cases of q-ROFSs. e IFS was first introduced by Atanassov [3], and then PFS was discussed later by Yager [15]. e constructions of IFSs and PFSs show the importance of membership and nonmembership functions in various real-life problems. However, there is a limitation in these models; that is, they fail to solve decision-making problems having information in which the sum or sum of the squares of membership and nonmembership values is greater than 1. To overcome these issues in more complicated information, q-ROFSs were introduced by Yager [15]. After introducing vector spaces and matroids based on PFSs in [43], in this study, we propose q-ROFVSs and q-ROFMs. is section provides the comparative analysis with PFMs and Algorithm 1 discussed  Table 3: Fuzzy information of connections between cities and their score functions (see Figure 2 in [43]).   in [43] (Section 4) to prove the efficiency of q-ROFMs and our proposed Algorithm 1, . It can be seen easily that the exiting method used to solve an application discussed in [43] (see Section 4) fails to solve the developed application in this study (see Section 4). us, for comparison, we use the dataset from Application (Section 4, Figure 2) [43]. en, for q � 2, 3, 4 and q � 5, we compute the score functions (see Table 3). From Tables 4-7, it can be easily seen that for any q, the spanning path a 1 , a 3 , a 6 attains the minimum value.
us, our proposed technique is more flexible and generalized as it allows the decision makers to choose different values of q according to the given fuzzy information. Moreover, we have used directed graphs, spanning trees, and maximal independent sets to propose a particular algorithm (Algorithm 1, ) that can be helpful in solving the human trafficking-related problems.

Conclusion
e study was carried out to enhance the real-life efficiency of some important models by curbing the issues of imprecise and vague information. Since the fuzzy sets are known to have the capacity to provide different models and tools for handling such information, q-ROFS is more suitable than IFS and PFS as it increases the space containing acceptable orthopair by increasing the value of parameter q. In this study, we have proposed vector spaces based on q-ROFSs and subsequently named them as q-ROFVSs. We have also discussed q-ROFVSs with an illustrative numerical example and developed some relevant results like basis and dimension. e q-ROF linearly independent vectors are also discussed. Furthermore, we have introduced q-ROFMs with their characteristics. We have extended some of the results based on IFM and PFM to q-ROFM. We have also discussed the notions of circuits, basis, and rank function for q-ROFMs. Finally, we have concluded the proposed work with a real-life application of decision making regarding human trafficking between different countries. For that, we used a directed graph with q-rung orthopair fuzzy information and combined it with the concept of maximal independent sets of edges of the graph to find the heaviest path. To enhance the capability of the q-ROFMs, we have provided a comparative analysis with an existing model. We are of the view that the given study would help the concerned countries in deciding the action response in a suitable path for the reduction of human flow. e major limitation of the proposed model is that it fails when the objects are evaluated concerning multiple parameters from more than one expert. In other words, there is no parametrization tool present in the initiated approach. Moreover, we are also interested to broaden our work to (a) q-rung orthopair fuzzy soft matroids, (b) fuzzy N-soft matroids, and (c) spherical fuzzy N-soft matroids. is will illustrate more exclusive results based on the given fuzzy matroids and will be helpful in figuring out more real-life problems.
Data Availability e data that support the findings of this study are available on request from the corresponding author.

Conflicts of Interest
e authors declare that they have no conflicts of interest.