Research article Special Issues

A new method to evaluate regular ternary semigroups in multi-polar fuzzy environment

  • Theory of m-polar fuzzy set deals with multi-polar information. It is used when data comes from m factors (m2). The primary objective of this work is to explore a generalized form of m-polar fuzzy subsemigroups, which is m-polar fuzzy ternary subsemigroups. There are many algebraic structures which are not closed under binary multiplication that is a reason to study ternary operation of multiplication such as the set of negative integer is closed under the operation of ternary multiplication but not closed for the binary multiplication. This paper, presents several significant results related to the notions of m-polar fuzzy ternary subsemigroups, m-polar fuzzy ideals, m-polar fuzzy generalized bi-ideals, m-polar fuzzy bi-ideals, m-polar fuzzy quasi-ideals and m-polar fuzzy interior ideals in ternary semigroups. Also, it is proved that every m- polar fuzzy bi-ideal of ternary semigroup is an m-polar fuzzy generalized bi-ideal of ternary semigroup but converse is not true in general. Moreover, this paper characterizes regular and intra-regular ternary semigroups by the properties of m-polar fuzzy ideals, m-polar fuzzy bi-ideals.

    Citation: Shahida Bashir, Ahmad N. Al-Kenani, Maria Arif, Rabia Mazhar. A new method to evaluate regular ternary semigroups in multi-polar fuzzy environment[J]. AIMS Mathematics, 2022, 7(7): 12241-12263. doi: 10.3934/math.2022680

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  • Theory of m-polar fuzzy set deals with multi-polar information. It is used when data comes from m factors (m2). The primary objective of this work is to explore a generalized form of m-polar fuzzy subsemigroups, which is m-polar fuzzy ternary subsemigroups. There are many algebraic structures which are not closed under binary multiplication that is a reason to study ternary operation of multiplication such as the set of negative integer is closed under the operation of ternary multiplication but not closed for the binary multiplication. This paper, presents several significant results related to the notions of m-polar fuzzy ternary subsemigroups, m-polar fuzzy ideals, m-polar fuzzy generalized bi-ideals, m-polar fuzzy bi-ideals, m-polar fuzzy quasi-ideals and m-polar fuzzy interior ideals in ternary semigroups. Also, it is proved that every m- polar fuzzy bi-ideal of ternary semigroup is an m-polar fuzzy generalized bi-ideal of ternary semigroup but converse is not true in general. Moreover, this paper characterizes regular and intra-regular ternary semigroups by the properties of m-polar fuzzy ideals, m-polar fuzzy bi-ideals.



    One of the important features of graph metric generator is that its different version can be introduced according to the required scenario or application. Up till now, a lot of research work has been carried out on metric generators and its various versions starting from Slater [14], Haray [7] and then contributed by a number of authors [2,3,4,5,6,7,8,9,10,11,12]. The notion of graph metric generator was primarily studied due to its basic property of identification of intruder in the network as all the nodes in the network can be uniquely localized by a certain set of vertices called metric generator. However, in the situation, when an intruder can approach the system not only through nodes but also by manipulating the connections between the nodes (i.e., edges), then a basic metric generator may not be able to locate the intruder. This leads to the motivation of constructing a metric generator having capabilities of distinguishing both vertices and edges so that this type of situation can be handled. Kelenc et al. [9] proposed a metric generator variant referred as mixed metric generator which can identified both vertices and edges of graph simultaneously. They analyzed different properties of mixed metric generator. In particular, they characterized the graphs of order n having mixed dimension 2 and n. They proved that graph has mixed dimension two if and only if it is a path graph and has mixed dimension n if and only if it is a complete graph. They also determined mixed dimension of some well- known families of graph like path graph, cycle graph, cartesian product with path graphs, etc. The mixed metric dimension of petersen graph was determined by Raza and Ji [13]. The mixed metric dimension for unicyclic graphs was investigated in [15]. In [1], the necessary and sufficient conditions for graphs of order at least 3 having mixed fault-tolerant generators are established. Moreover, a mixed fault-tolerant metric generator is determined for graphs having shortest cycle length at least 4. Danas et al. [6] presented three general lower bounds for mixed metric dimension of graphs. They also compare the new bounds with already existing bounds in literature.

    As the minimum mixed dimension for a simple connected graph JPn with n3 is three and one of such graph is cycle graph [9], so it is natural to seek all graphs having mixed dimension three. The focus of this paper is to characterize such graphs and to develop an algorithm to determine "whether a simple connected graph J with n3 vertices such that JPn has mixed dimension three or not?" We also classify all unicyclic graphs with mixed dimension three. To characterize the graphs having mixed dimension three and to develop algorithm for this, we use the idea of neihbourhood of vertices and vertex distance partitions which was used in [16] for characterization of graphs having metric dimension two. To recall, for a graph Q, the distance between two vertices is the length of the shortest path between them, whereas, the distance between a vertex x and an edge e=yz is given as d(x,e)=min{d(x,y),d(x,z)}. The subset M={m1,m2,,mk}V(Q) is referred as mixed metric generator or mixed generator, if distance vectors of any two members x,yV(Q)E(Q) are distinct, i.e., r(x|M)r(y|M), where distance vector r(x|M)=(d(x,m1),d(x,m2,,d(x,mk)). The smallest mixed generator is called mixed basis and the number of elements in mixed basis is called mixed dimension. It is represented as dimm(Q). For xV(Q), the collection {X0,X1,,Xk} is referred as distance partition of V(Q) relative to the vertex x if X0={x}and Xj={yV(Q):d(x,y)=j, for 1jk, where k is the eccentricity of x in Q. The sets X0,X1,,Xk are referred as distance partite sets. The open neighbourhood N(v) of vV(G) is defined as N(v)={xV(G):xv}. The eccentricity e(v) of any vertex vV(G) is the maximum distance of v in V(G), i.e., e(v)=maxxV(G)d(x,v). the vertex of degree one is referred as pendant vertex and vertex adjacent to pendant vertex is called support vertex. A unicyclic graph is a simple graph with exactly one cycle.

    Definition 2.1. Consider a graph G of order r1 with V(G)={v1,v2,,vr} and r paths Psi of length si with si1. Then the graph obtained from G by identifying a pendant vertex of a path Psi with viV(G) such that i=1,,r. Then this new graph is denoted by ΓG(Ps1,Ps2,,Psr).

    Example 2.1. Consider a wheel graph of order 8, where V(W8)={v1,v2,v3,v4,v5,v6,v7,v8}. Then G is the new graph obtained by identifying the pendent vertices of paths of lengths P2,P2,P3,P3 with vertices v2,v3,v5,v7 and path of length one with all remaining vertices ΓW8(P1,P2,P2,P1,P3,P1,P3,P1) as shown in Figure 1.

    Figure 1.  ΓW8 obtained by W8.

    Example 2.2. The unicycle graph Un=ΓC8(P3,P1,P2,P1,P1,P3,P1,P1) is obtained by C8 and identifying the pendant vertices of paths P3,P2 and P3 with vertices u1,u3, and u6, respectively. This is shown in Figure 2.

    Figure 2.  The unicycle graph Un=ΓC8(P3,P1,P2,P1,P1,P3,P1,P1).

    Remark. If Pm:v1v2vm is a path with end vertices v1 and vm. Then the graph Γpm(P1,,P1,Pr) is a path on m+r1 vertices for r1, whereas for r2 the graph Γpm(P1,Pr,P1,P1) is a tree which is not a path. The tree Γpm(P1,P2,P1,P1) is shown in the Figure 3.

    Figure 3.  The tree Γpm(P1,Pr,P1,P1).

    Remark. It can be easily seen that ΓG(Ps1,Ps2,,Psr)=Gri=1Psi such that GPsi=vi for all i=1,,r and PsiPsj=ϕ for all ij. Moreover ΓG(Ps1,Ps2,,Psr) is subgraph of rooted product of GaPt, where t=maxri=1si and a is a pendant vertex of Pt.

    Theorem 2.1. [9] If a graph G contains pendant vertices, then any mixed metric generator of G must contain all pendant vertices of G.

    Theorem 2.2. Let TnPn be a tree with n3 vertices. Then dimm(Tn)=3 if and only if TnΓPt(P1,,P1,Pr,P1,,P1), where Pr is attached to a vertex which is not pendant vertex such that r2 and n=t+r1 and t3.

    Proof. Suppose dimm(Tn)=3. Since TnPn, Tn contains at least three pendant vertices. As from Theorem 2.1, any mixed metric contains all the pendant vertices, Tn has mixed dimension three only if it contains exactly three pendant vertices, say v1, v2 and v3. If v1, v2 and v3 are attached to exactly one support vertex, say s, then we claim that TnK1,3 for otherwise there is a vertex s1 different from v1, v2 and v3 but adjacent to s. If s1 is a pendant vertex, then we have four pendant vertex, a contradiction. Thus there must be a vertex adjacent to s1, say s2. Now s2v1,v2,v3 or s for otherwise we have a cycle which contradict the fact that Tn is a tree. Continuing in this way, we have distinct vertices s1,s2,. But as we have finite number of vertices, this process must end and there ia an index k such that either sk=sj for some 1j<k or sk=v1,v2,v3, or s. In either case, we have a cycle, a contradiction. Hence if there is exactly one support vertex, then TnK1,3. Now we suppose that s and s are two support vertices attached to vertices v1 and v2, respectively. Let d(s,s)=h, then as v1 and v2 are pendant vertices, so d(v1,v2)=h+2. Let t=h+2. Then we have a path Pt of length t from v1 to v2. Since Tn is connected and v3 does not lie on the path Pt so the vertex v3 is linked to every vertex of path Pt through some path. Now let vi be the vertex on Pt which is nearer to v3. Clearly viv1,v2. Let d(vi,v3)=r i.e., we have a path Pr whose end vertex vi coincides with ith vertex of path Pt. Then the subgraph induced by V(Pt) and V(Pri) is isomorphic to ΓPt(P1,,Pri,P1). Clearly vi is not a pendant vertex of Pt. Now we claim that all the vertices of Tn either lie on path Pt or on Pr. For if (V(Pt)V(Pri))V(Tn)ϕ, then there exists a vertex w1 adjacent to some vertex of V(Pt)V(Pri). Without loss of generality, assume that w1 is adjacent to some vertex of Pt. If degree of w1 is one, than Tn has four pendant vertices, a contradiction. Therefore deg(w1)2. Since Tn does not contain any cycle, w1 is not adjacent to any vertex of V(Pt) and V(Pri). Thus we have a vertex w2Tn(V(Pt)V(Pr)). By continuing this process, we get vertices w1,w2,Tn(V(Pt)V(Pr)). Since Tn is finite, there exists some index k such that wj=wk for 1j<k. But then Tn contains a cycle, a contradiction. Hence GΓPt(P1,,P1,Pr,P1,,P1) such that Pr is not attached to pendant vertex of Pt and n=t+r1.

    Corollary 1. A tree Tn has mixed dimension 3 if and only if degree sequence of Tn is (1,1,12,,2(n4)times,3).

    Corollary 2. Given the degree sequence of tree Tn, it is decidable in time O(1) whether Tn has mixed metric dimension three.

    Proof. From Corollary 1, it can be seen that if we have degree sequence of a tree, we only need to check the first four elements and last two elements of degree sequence. Since a tree has always at least two pendant vertices, first two entries will always contain one's for otherwise it is not a degree sequence of a tree. Thus to determine whether a tree has mixed dimension three, we only have to check the third, fourth and last two elements of degree sequence. This completes the proof.

    Lemma 2.3. Let U be a unicyclic graph with dimm(U)=3. Then any cycle vertex has degree at most 3 and any non-cycle vertex has degree at most 2.

    Proof. Consider a unicyclic graph U, with a unique cycle of length n. Suppose that there exists a cycle vertex c1 such that deg(c1)>3. Now label the cycle vertices as {c1,c2,,cn}. Let v1 and v2 be two non-cycle vertices attached to cycle vertex c1. Then there exists two distinct pendant vertices l1 and l2 such that c1v1l1 and c1v2l2 are the shortest paths having lengths r and s, respectively. Then l1,l2 must belong to M. Since dimm(U)=3, there exists l1,l2mM such that M={l1,l2,m} is a mixed metric basis. First assume that m is any vertex of the maximal subtree containing c1. Then consider x=c1 and y=c1c2, where c2N(c1) be a cycle vertex. Since the vertex c1 is more closer to l1,l2 and m than c2, the coordinate vectors of x and y with reference to M are r(x|M)=(r,s,t)=r(y|M), a contradiction. Hence we may assume that m does not belong to the maximal subtree containing c1. Now suppose n is odd, then c=cn12 and c=cn+12(antipodal vertices) are equidistant from c1. Without loss of generality, assume c=cn12 is more closer to m than c. Then for a pair of elements x=c and y=cc, d(m,c)=d(m,cc). Since c,c are antipodal vertices, d(cc,c1)=d(c,c1)=d(c,c1). This further implies that d(l1,x)=d(l1,c1)+d(c1,c)=d(l1,c1)+d(c1,cc)=d(l1,y). Similarly d(l2,x)=d(l2,c1)+d(c1,c)=d(l2,c1)+d(c1,cc)=d(l2,y), i.e., r(c|M)=r(y|M), a contradiction. Now if n is even then the vertices cn2 and cn2+2 are equidistant from c1. Clearly e1=cn2cn2+1,e2=cn2+1cn2+2E(U) and d(e1,li)=d(e2,li)=d(li,cn2)=d(li,cn2+2) for i=1,2. Now if d(m,cn2+1)<d(m,cn2),d(cn2+2,m), then d(e1,m)=d(m,e2) which implies that r(e1|M)=r(e2|M), a contradiction. Thus without loss of generality, assume that m is more closer to cn2 than cn2+1 and cn2+2. But then r(e1|M)=r(cn2|M). All cases lead to a contradiction. Thus degree of cycle vertex is at most 3.

    Now let y be any non-cycle vertex with degree greater than 2. Then there exist at least two distinct pendant vertices l1,l2 such that there is exactly one path from y to li for i=1,2. By Theorem, l1,l2M. Since dimm(U)=3, M={l1,l2,m} is a mixed metric basis. If c1 is a first cycle vertex closer to y. Then using the same arguments as above, it can be shown that M is not mixed resolving set.

    Theorem 2.4. Let Un be a unicycle graph. Then dimm(Un)=3 if and only if either UnCn or UnΓCm(Pr,P1,,P1m1times), where m+r1=n (i.e., tadpole graph) or UnΓCm(Pr1,P1,P1itimes,Pr2, P1,P1m2itimes), where i0 and m+r1+r22=n or UnΓCm(Pr1,P1,P1itimes,Pr2,P1,P1jtimes,Pr3, P1,P1mij3times) such that m+r1+r2+r33=n and i+j+3s, where s=m/2+1 if m is even and s=(m+1)/2 otherwise.

    Proof. Suppose dimm(Un)=3. As from Theorem 2.1, any mixed metric contains all the pendant vertices, Un has metric dimension three only if it contains at most three pendant vertices. Now by Lemma 2.3, degree of each cycle vertex is at most 3. If all the cycle vertices have degree two than UnCn. Thus we may assume that there exists at least one cycle vertex of degree three. We claim that there are at most three cycle vertices whose degree is 3, for if there are more than three vertices, then there must exists at least four pendant vertices, a contradiction. Hence, there are at most three cycle vertices whose degree is 3.

    Let x be the cycle vertex of deg(x)3. Suppose Tx be the subtree attached at x containing unique cycle vertex x. We claim that Tx is a path, for if Tx is not a path then there must exists at least one vertex wTx such that deg(w)3. If w=x then deg(w) in Un is at least 5, which contradicts Lemma 2.3. If w is a non- cycle vertex with deg(w)3, then it again contradicts Lemma2.3. This further shows that Un is a graph in which a path is attached to all cycle vertices having degree at least 3. If there is only one cycle vertex with degree three, then it is easy to see that Un is a tadpole and UnΓCm(Pr,P1,,P1m1times). If Un contains two cycle vertex of degree three, then clearly, UnΓCm(Pr1,P1,P1itimes,Pr2, P1,P1m2itimes) such that m+r+t2=n. Now suppose Un contains exactly three cycle vertex of degree three. Then M={u,v,w} consisting of pendant vertices and UnΓCm(Pr1,P1,P1itimes,Pr2,P1,P1jtimes,Pr3, P1,P1mij3times), where m+r1+r2+r33=n. Let the vertices of Cm are ordered as v=v1,vm such that u=vi,w=vj. Suppose m is even but both i,jm/2. Now consider the edge e=vm/2vm/2+1 and the vertex vm/2. As d(v1,vm/2)=m/2 and d(v1,vm/2+1)=m/2+1, so d(v1,e)=d(v1,vm/2). Also as i,jm/2, so vm/2 is closer to vi=u and vj=w as compared to vm/2+1. Hence r(vm/2|M)=r(e|M), a contradiction. Now suppose m is odd but both i,j(n+1)/2. Using same arguments, it can be shown that r(v(m+1)/2|M)=r(v(m+1)/2V(m+3)/2|M). It completes the proof

    Lemma 3.1. Let M={v1,v2,v3} be a mixed metric basis of a graph G. Then |N(vi)N(vj)|3 for all distinct pair of vertices vi,vjM. Moreover any two distinct vertices in N(vi)N(vj) for ij are not adjacent.

    Proof. Consider distinct pair of vertices vi,vjM. Suppose d(vi,vk)=r for vkvi,vj. Now for any vertex wN(vi)N(vj), d(w,vi)=1=d(w,vj) so we have

    d(w,vk)1+r. (3.1)

    Now clearly, d(w,vk)r1 for otherwise we have a path viwvk of length less than r between vi and vk, a contradiction. Hence

    r1d(w,vk)r+1. (3.2)

    This further implies that only possibilities are d(w,vk)=r1,r,r+1. Now if there are more than three vertices in N(vi)N(vj), then as d(w,vi)=1=d(w,vj), the coordinate vectors of at least two vertices in N(vi)N(vj) with respect to M are same. This leads to a contradiction. Now for ij, let x,yN(vi)N(vj) be two distinct vertices such that xy. As d(x,vi)=d(y,vi)=1 and d(x,vj)=d(y,vj)=1. Since M is mixed basis, we have d(x,vk)d(y,vk). Suppose t=d(x,vk)<d(y,vk). Then r(x|M)=(1,1,t) and as x is more closer to vk than y so d(xy,vk)=d(x,vk)=t. But then d(xy|M)=(1,1,t)=d(x|M), i.e., x and the edge xy are not resolved by M, a contradiction.

    Lemma 3.2. Let M={v1,v2,v3} be a mixed metric basis of a graph G and {Vi0,Vi1,,Vie(vi)} be the distance partition of V(G) with respect to viM, where e(vi) is the eccentricity of vi. Then |V1iV2jV3k|1 for all 1ie(v1), 1je(v2) and 1ke(v3)

    Proof. Suppose on contrary there exists some integers re(v1), se(v2) and te(v3) such that |V1rV1sV1t|>1. Then there exists a,bV(G) such that a,bV1rV2sV3t. Then clearly

    d(a,v1)=r=d(b,v1),d(a,v2)=s=d(b,v1),d(a,v3)=t=d(b,v3),

    i.e., r(a|M)=r(b|M). This leads to a contradiction.

    Lemma 3.3. Let M={v1,v2,v3} be a mixed metric basis of a graph G and {Xi0,Xi1,,Xie(vi)} be the distance partition of V(G) with respect to viM. If for some i,j{1,2,3}, 0re(vi) and 0se(vj), the vertices a,bVirVjs, then a and b are not adjacent.

    Proof. Suppose the vertices a and b are adjacent, i.e., e=abE(G) and d(a,vk)=t for vkvi,vj. Then using the same arguments as used in the proof of Lemma 3.1, it can be seen that d(b,vk)=t1,t,t+1. Now d(b,vk)t for otherwise a,bVirVjsVkt which contradicts Lemma 3.2. If d(b,vk)=t1, then b is more closer to vk than a. In this case, d(b,vk)=d(e,vk). Now as a,bVirVjs, therefore d(b,vi)=r=d(e,vi) and d(b,vj)=s=d(e,vj), i.e., the vertex b and the edge e=ab are not resolved by M. Similarly if d(b,vk)=t+1, then it can be shown that the vertex a and the e=ab cannot be distinguished by M. This leads to a contradiction.

    Lemma 3.4. Let M={v1,v2,v3} be a mixed metric basis of a graph G and {Vi0,Vi1,,Vie(vi)} be the distance partition of V(G) with respect to viM. If for some 0re(vi), 0se(vj) and 0te(v3), the vertex aV1rV2sV3t and b(V1rV1(r+1))(V2sV2(s+1))(V3tV3(t+1)), then a and b are not adjacent.

    Proof. As aV1rV2sV3t and b(V1rV1(r+1))(V2sV2(s+1))(V3tV3(t+1)), so d(a,vi)d(b,vi) for i=1,2,3. Thus if a is adjacent to b, i.e., e=abE(G), then d(e,vi)=d(a,vi) for all i. This further implies that r(a|M)=r(e=ab|M) which contradicts the fact that M is mixed basis.

    Lemma 3.5. Let M={v1,v2,v3} be a mixed metric basis of a graph G and {Vi0,Vi1,,Vie(vi)} be the distance partition of V(G) with respect to viM. If for aV1rV2sV3t (0re(vi), 0se(vj) and 0te(v3)), there exist b,c(V1rV1(r+1))(V2sV2(s+1))(V3tV3(t+1)) such that at least one of b and c must belong to v1r, v2s and v3t then b and c are not adjacent.

    Proof. If a is equal to one of b and c, say a=c then from Lemma 3.4, a=c is not adjacent to b. Thus we may assume that ab,c. Suppose on contrary bc, i.e., e=bcE(G). Now d(a,v1)=r, d(a,v2)=s and d(a,v3)=t. Since at least one of b and c (end points of edge e) must belong to v1r, v2s and v3t, d(e,v1)=r, d(e,v2)=s and d(e,v3)=t. This implies that r(a|M)=(r,s,t)=r(e=ab|M), a contradiction to the assumption that M is a mixed basis of G.

    Lemma 3.6. Let M be a mixed metric basis of a graph G and {Vi0,Vi1,,Vie(vi)} be the distance partition of V(G) with respect to viM. If for a,cVi, there exists b,d(VirVi(r+1))(VjsVj(s+1))(VktVk(t+1)) (ijk) such that a,dVjs and b,cVkt, where 0se(vj), 0te(vk) then either ab or cd.

    Proof. Suppose on contrary, for a,cVi, there exists b,d(VirVi(r+1))(VjsVj(s+1))(VktVk(t+1)) (ijk) such that a,dVjs, b,cVkt but ab and cd, i.e., e1=ab,e2=cdE(G). Since a,cVi, a and c are more closer to vi, than b and d, respectively. This shows that d(e1,vi)=d(a,vi)=r and d(e2,vi)=d(c,vi)=r. Now as a,dVjs, so a and d are more closer to vj, than b and c, respectively. This implies that d(e1,vj)=d(a,vj)=s and d(e2,vj)=d(d,vj)=s. Similarly we can see that d(e1,vk)=d(b,vk)=t and d(e2,vk)=d(c,vj)=t. Thus if M={vi,v2,v3 is ordered mixed basis of G, then r(e1|M)=(r,s,t)=r(e2|M), a contradiction.

    Lemma 3.7. Let M={v1,v2,v3} be a mixed metric basis of a graph G and {Vi0,Vi1,,Vie(vi)} be the distance partition of V(G) with respect to viM. Then induced subgraph of any distance partition Vij of any vertex viM is triangle free.

    Lemma 3.8. Let M={v1,v2,v3} be a mixed metric basis of a graph G and {Vi0,Vi1,,Vie(vi)} be the distance partition of V(G) with respect to viM. Then the maximum degree of any vertex in induced subgraph of any distance partition Vij is at most 2.

    Lemma 3.9. Let M be a mixed metric basis of a graph G with |M|=3. Then the maximum degree of any vertex of M is at most 3.

    Proof. Let viM be any vertex and let M={vi,vj,vk} be ordered basis of G. Suppose d(vi,vj)=r and d(vi,vk)=t. Suppose aN(vi). Then clearly d(a,vj)=r,r1 or r+1 and d(a,vk)=t,t1 or t+1. We claim that if d(a,vj)=r, then d(a,vk) must be t1 for otherwise r(vi|M)=(0,r,t)=r(avi|M), i.e., the vertex vi and edge avi are not resolved by M. Similarly if d(a,vj)=r+1, then d(a,vk)=t1. Also we claim that the distinct vertices a,cN(vi) with representation r(a|M)=(1,r,t1) and r(c|M)=(1,r+1,t1) do not occur simultaneously for otherwise a,cVk(t1)Vi1, b=vi=dVktVi0 and a,dVjr. By Lemma 3.3, either a is not adjacent to b=vi or c is not adjacent to d=vi, a contradiction as both a,cN(vi). Now we claim that there are at most two vertices in N(vi) with distance r1 from vj, for if there are at least three distinct vertices a,b,cN(vj), then their distance representation must be (1,r1,t1), (1,r1,t) and (1,r1,t+1), respectively. But then b,cVj(r1)Vi1, e=vi=fVjrVi0 and b,e,fVkt. By Lemma 3.3, either b is not adjacent to e=vi or c is not adjacent to f=vi, a contradiction as both b,cN(vi). Thus N(vi) can contain at most three vertices; maximum 2 with distance r1 and one with distance r or r+1 (but not both). Thus maximum number of vertices in N(vi) is 3.

    Theorem 3.10. Let G be a connected graph which is not a tree having mixed metric dimension 3 and diameter D. Then G contains at most (D3+12D2)/2 vertices.

    Proof. For each vertex except basis vertex, the associated coordinate vector with positive coordinates (α,β,γ), where 1α,β,γD can be chosen in one of D3 ways. The coordinate vector corresponding to basis elements are (0,β1,γ1), (α2,0,γ2) and (α,β,0) for some 1α,β,γD. On the other hand, the coordinate vectors are also assigned to edges and these must be distinct from the coordinate vectors of vertices. All the edges except those which are incident with basis elements have positive integers in each coordinate of coordinate vectors. Therefore, these again must be chosen from possible D3 coordinate vectors. From Lemma 3.9, the maximum number of edges incident to three basis vertices are 9. Hence maximum number of coordinate vectors which can be assigned to edges and vertices must not exceed D3+3D2+9D2. Then this further implies that

    |V(G)|+|E(G)|D3+12D2 (3.3)

    As G is connected but not a tree, so it must contain at least |V(G)| edges. Thus (3.3) becomes

    |V(G)|D3+12D2|E(G)|D3+12D2|V(G)|

    Hence

    |V(G)|(D3+12D2)/2

    Theorem 3.11. Let G be a graph with n3 vertices such that G is not a tree and {Vi0,Vi1,,Vie(vi)} be the distance partition of V(G) with respect to viV(G), where e(vi) is the eccentricity of vi. Then mixed metric dimension of G is three if and only if there exists three vertices v1,v2 and v3 which satisfy the following conditions:

    1. |V1iV2jV3k|1 for all 1ie(v1), 1je(v2) and 1ke(v3).

    2. If for aV1rV2sV3t (0re(vi), 0se(vj) and 0te(v3)), there exists b,c(V1rV1(r+1))(V2sV2(s+1))(V3tV3(t+1)) such that at least one of b and c must belong to v1r, v2s and v3t then b and c are not adjacent.

    3. If for a,cVi, there exists b,d(VirVi(r+1))(VjsVj(s+1))(VktVk(t+1)) (ijk) such that a,dVjs and b,cVkt, where 0se(vj), 0te(vk) then either ab or cd.

    Proof. Let M={v1,v2,v3} be a mixed metric basis of a graph G. suppose on contrary condition (1) is not satisfied. Then there exists some integers re(v1), se(v2) and te(v3) such that |V1rV1sV1t|>1. Then there exists a,bV(G) such that a,bV1rV2sV3t. Then clearly

    d(a,v1)=r=d(b,v1),d(a,v2)=s=d(b,v1),d(a,v3)=t=d(b,v3),

    i.e., r(a|M)=r(b|M). This leads to a contradiction. Hence condition (1) holds. Also conditions (2) and 3 are satisfied from Lemma 3.5 and Lemma 3.6.

    Conversely suppose that there exists three vertices v1,v2 and v3 that satisfy all the conditions. We will show that the set M={v1,v2,v3} is mixed metric generator for G. Suppose on contrary there exists distinct elements x,yV(G)E(G) such that x and y are not resolved by any member of M. Then

    d(x,v1)=d(y,v1)=r,d(x,v2)=d(y,v2)=s,d(x,v3)=d(y,v3)=t. (3.4)

    If x,yV(G), then x,yV1rV2sV3t, i.e., |V1rV2sV3t|2, which contradicts condition (1). Hence for at least one viM, d(x,vi)d(y,vi). Now suppose xV(G) and y=y1y2E(G). Then from (3.4), we have xV1rV2sV3t and y1,y2(V1rV1(r+1))(V2sV2(s+1))(V3tV3(t+1)) such that at least one of y1 and y2 must belong to V1r, V2s and V3t. But then from condition (2), y1y2, which contradicts the assumption that y=y1y2 is an edge. Finally, assume that x=x1x2 and y=y1y2 are two distinct edges. There arise two cases:

    Case 1: The edges x and y are not adjacent, i.e., x1x2y1y2. Then from (3.4),

    x1,x2,y1,y2(V1rV1(r+1)(V2sV2(s+1))(V3tV3(t+1)). (3.5)

    Consider the edge x=x1x2, by pigeon hole principle, one of x1 and x2 is more closer to two of v1, v2 and v3. Without lose of generality, assume that x1 is more closer to v1 and v2 than x2, i.e., d(x,v1)=d(x1,v1)=r and d(x,v2)=d(x1,v2)=s. This implies that

    x1V1rV2s. (3.6)

    Now we claim that x1V3t for otherwise x1V1rV2sV3t and x2(V1rV1(r+1))(V2sV2(s+1))(V3tV3(t+1)). Then by condition (2), for a=x1=b and c=x2, x1=bc=x2, which contradicts the assumption that x=x1x2 is an edge. Thus

    x2V3t,x1V3(t+1). (3.7)

    Now consider the edge y=y1y2 and again by similar arguments, one of the end points of edge y, say, y1 is more closer to two of members of M than y2. There arise the following sub-cases:

    Case 1a:

    If y1 is more closer to v1 and v2 as compared to y2, then y1,x1V1rV2s. Again by using condition (2), it can be shown that y2V3t and y1V3(t+1). But these along with Eq (3.7) imply that x1,y1V3(t+1) so that x1,y1V1rV2sV3(t+1). This contradicts condition (1).

    Case1b:

    Now suppose y1V3t and y1 belong to exactly one of V1r and V2r. Without loss of generality assume that y1V1r. Then

    y1V1rV3t. (3.8)

    But by using condition (2), we have

    y2V2s,y1V2(s+1). (3.9)

    Now from Eqs (3.5), (3.6), (3.7), (3.8) and (3.9), we have for x1,y1V1r, there exists x2,y2(V2sV2(s+1))(V3tV3(t+1)) such that x1,y2V2s and x2,y1V3t. But then from condition (3), either x1x2 or y1y2, a contradiction.

    Case 2:

    Suppose the edges x=x1x2 and y1y2 are adjacent with their common vertex x1=y1. Then from (3.4),

    x1,x2,y2(V1rV1(r+1)(V2sV2(s+1))(V3tV3(t+1)). (3.10)

    We claim that the common vertex x1 is nearer to at least two of the vertices of M as compared to other vertices x2 and y2 for otherwise from (3.10), either x1V1(r+1)V2(s+1)V3(t+1) or x1 belongs to exactly one of V1r, V2s and V3t, say V1r. If x1V1(r+1)V2(s+1)V3(t+1), then from 3.4, d(x2,v1)=d(y2,v1)=r, d(x2,v2)=d(y2,v2)=s and d(x2,v3)=d(y2,v3)=t. This further implies that x2,y2V1rV2sV3t, a contradiction. Also if x1V1r but x1V2s,V3t, then as from (3.4), x and y are at same distance from v2 and v3, and from (3.10), the only possibility is

    x2,y2V2sV3t. (3.11)

    Now we claim that none of x2 and y2, belongs to V1r for if one of x2 and y2, say x2 belong to V1r, then from (3.11), x2V1rV2sV3t and x1,x2(V1rV1(r+1))(V2sV2(s+1))(V3(t+1)VV3t. Using condition (2), x1x2, a contradiction as x=x1x2 is an edge. Hence none of x2 and y2, belongs to V1r. Then from (3.10), x2,y2V1(r+1). But then x2,y2V1(r+1)V2s)V3t which contradicts condition (1). Hence x1 is closer to two of the vertices of M, say v1 and v2 as compared to vertices x2 and y2. Now using condition (2) and (3.10), we can write

    x1V3(t+1),x2,y2V3t (3.12)

    Also from (3.10), x2,y2(V1rV1(r+1))(V2sV2(s+1)). If x2,y2V1(r+1)V2(s+1), then this along with (3.12) imply that x2,y2V1(r+1)V2(s+1)V3t. This contradicts condition (1). Therefore one of x2 and y2 must belong to one of V1r and V2s. Suppose without loss of generality that x2V1r. Consider y1=x1,x2V1r, then there exists x1,y2(V1rV1(r+1))(V2sV2(s+1))(V3tV3(t+1)) such that y1,x1V2s and x2,y2V3t. By condition (3), either y1=x1toy2 or x2x1. This leads to a contradiction as x=x1x2 and y1y2=x1y2 are edges.

    All the possible cases lead to a contradiction. Hence M is a mixed metric generator. Therefore dimm(G)3. As GPn, dimm3. Hence dimm(G)=3.

    In this section, three algorithms are developed for determine whether or not "a tree, unicyclic graph and in general a simple connected graphs has mixed dimension three?". The algorithm for tree to have mixed metric dimension three using Theorem 2.2 and Corollary 1 is given in Algorithm 1.

    Algorithm 1 Determination of Tree having mixed dimension 3.
    Require: Degree sequence S of Tn
    Ensure:"Mixed Basis with three elements" or "the graph has mixed dimension 2 or greater than 3"
    2. if(S(3)>1)(n=2) then
    3. Tree is path graph having mixed dimension 2
    4. else if(S(3)=1)(S(4))>1(S(n)=3)(S(n1)<3) then
    5. Mixed metric basis consists of three pendant vertices 6. else
    7. Tree has mixed dimension greater than 3
    8. end if

    Theorem 4.1. The complexity of the algorithm to determine a mixed basis of dimension three for a graph J of order n and diameter D is D3(n(n1)4(n2)6).

    Proof. It is noted that the number of sets in any distance partition of V(J) is maximum δ. In the proposed algorithm, three distance partitions of V(J) with respect to three distinct vertices are considered at each step. Each set in one partition is compared with two distinct sets from two other partitions(one from each partition). Thus total number of set comparisons is D3(n(n1)(n2)6). Moreover, in each comparison of three sets, the element wise comparison is at most (n1)3. Thus the complexity of the algorithm is D3(n(n1)4(n2)6).

    Lemma 2.3 and Theorem 2.4 are used to develop an algorithm to find unicyclic graphs having mixed dimension 3. It is given in Algorithm 2.

    Algorithm 2 Determination of Unicycle graph having mixed dimension 3.
    Require: cycle length r, Adjacency matrix of Un such that first r rows are cycle vertices
    Ensure:"Mixed Basis with 3 elements" or " dimm(Un)3 "
    2. n number of rows of adjacency matrix
    3. for i=1:n do 4. s number of one's in ith row
    5. ifs=3 then 6. S the set of cycle vertices i with degree 3
    7. else ifs=1 then
    8. P the set of pendant vertices i
    9. else ifs>3 then
    10. L the set of vertices i with degree greater than 3
    11. end if
    12.end for
    13.if |P|=0=|S|=|L| then
    14. print "M={1,r/2,(r+4)/2} is mixed metric basis"
    15. else if(1|P|3)(1|S|3)(|L|=0)then
    16. C(1)S(1) Relabeling of cycle starting from S(1) (first cycle vertex of degree three) in array C
    17. fork=1:rdo
    18. ifC(k)r1 then
    19. C(k+1)C(k)+1
    20. else
    21. C(k+1)C(k)+1r
    22. end if
    23.end for
    24. if(|P|=1=|S|)(|L|=0) then
    25. Print "M{C(1),C(r/2),C((r+4)/2)} is mixed metric basis"
    26. else if(|P|=2)(|SC|=2)(|L|=0) then
    27. forj=1:r do
    28. ifC(j)=S(2)(jr/2) then
    29. Print "M=P(1),P(2),C((r+4)/2) is mixed basis"
    30. else
    31. Print "M=P(1),P(2),C(r/2) is mixed basis"
    32. end if
    33. end for
    34.else if(|P|=3)(|SC|=3)(|L|=0) then
    35. forj=1:r do
    36. if (S(2),S(3)C for j(r+4)/2))(S(2),S(3)C for jr/2) then
    37. Print "Graph has mixed dimension greater than three"
    38. else
    39. Print "M={P(1),P(2),P(3)} is mixed basis"
    40. end if
    41. end for
    42. end if
    43. else
    44. Print "Graph has mixed dimension greater than 3" 45. end if

    Algorithm 3 Graph with mixed dimension 3.
    Require: (n×n) distance matrix D of graph GPn
    Ensure:"Mixed Basis with three elements" or "the graph has mixed dimension greater than 3"
    2. n number of rows of distance matrix
    3. dmaximum entry in distance matrix
    4. r No. of rows with at most three 1s. (To determine elements with degrees at most 3)
    5. if(n(d3+12d)/2)(r3)then
    6. Construct the set W{j:Ones(D(j,:))3}
    7. forj,kW do
    8. Nj{i:D(j,i)=1},
    9. Nk{i:D(k,i)=1}
    10. if |NjNk|>3 or for xN(j),yN(k,) D(x,y)=1 then
    11. WW{i,j}
    12. end if
    13. end for
    14. for i,j,kW, a=1:max(D(i,:)),b=1:max(D(j,:)),c=1:max(D(k,:)) do
    15. Via={s:D(s,j)=a}, Vjb={s:D(s,j)=b}, Vkc={s:D(s,j)=c}
    16. Vi(a+1)={s:D(s,j)=a+1}, Vj(b+1)={s:D(s,j)=b+1}, Vk(c+1)={s:D(s,j)=c+1}
    17. U(ViaVi(a+1))(VjbVj(b+1))(VkcVk(c+1)) 18. if ViaVjbVkc2 then 19. WW{i,j,k}
    20. else
    21. for(sViaVjbVkc)(t,uU) do
    22. d=D(u,t)
    23. if ((ut)(ViaVjbVkc))(d=1) then
    24. WW{i,j,k}
    25. end if
    26. end for
    27. for (u,vVia)(w,xU) do
    28. d1=D(u,w),d2=D(v,x)
    29. if(u,xVjb)(v,wVkc)(d1=1)(d2=1) then
    30. WW{i,j,k}
    31. end if
    32. end for
    33. end if
    34. end if
    35. if|W|3 then
    36. List all 3-subsets of W. Each 3-subset is possible mixed basis for G
    37. else
    38. Print "Graph has mixed metric basis greater than 3"
    39. end if
    40. else
    41. Print "Graph has mixed metric dimension greater than 3"
    42. end if

    The Lemma 3.9, Lemma3.1, Theorem 3.10, and Theorem 3.11 provide the criteria to determine whether a simple connected graph has mixed metric dimension 3 or not. If a graph has mixed metric dimension three, then Theorem 3.11 also provide the mixed metric basis for the graph. With the help of these results, an algorithm is developed which determines whether a graph has mixed metric dimension three or not and if it exists then it also finds its mixed basis. The algorithm requires the distance matrix of a simple connected graph which is not a path graph. It results in a mixed basis of dimension three of graph, if exists. Otherwise indicates that graph has mixed metric greater than three.

    In this paper, it is shown that the mixed metric dimension of tree TnPn (n3) is 3 if and only if TnΓPt(P1,,P1,Pr,P1,,P1), where Pr is attached to a vertex which is not pendant vertex such that r2 and n=t+r1 and t3. For unicycle graph Un, it is proved that dimm(Un)=3 if and only if either UnCn or UnΓCm(Pr,P1,,P1m1times), where m+r1=n (i.e., tadpole graph) or UnΓCm(Pr1,P1,P1itimes,Pr2, P1,P1m2itimes), where i0 and m+r1+r22=n or UnΓCm(Pr1,P1,P1itimes,Pr2,P1,P1jtimes,Pr3, P1,P1mij3times) such that m+r1+r2+r33=n and i+j+3s, where s=m/2+1 if m is even and s=(m+1)/2 otherwise. Moreover, several lemmas related to order of graph, maximum degree of basis elements and distance partite sets of basis elements are presented. These lemmas are then used to find the necessary and sufficient conditions for a graph to have mixed metric dimension 3. Finally, three separate algorithms are developed for tree, unicyclic graphs and in general for simple connected graph JnPn with n3 to determine "whether these graphs have mixed dimension three or not?". If these graphs have mixed dimension three, then these algorithms provide the mixed basis of input graphs.

    The authors appreciate the valuable comments and remarks of anonymous referees which helped to greatly improve the quality of the paper. The corresponding author(Muhammad Javaid) is supported by the Higher Education Commission of Pakistan through the National Research Program for Universities (NRPU) Grant NO. 2016188/NRPU/R&D/HEC/20212021.

    The authors declare that they have no conflicts of interest.



    [1] S. S. Raju, K. V. R. Srinivas, Ternary semigroup of a lattice, Int. J. Pure Appl. Math., 119 (2018), 115–121.
    [2] D. H. Lehmer, A ternary analogue of abelian groups, Amer. J. Math., 54 (1932), 329–338. https://doi.org/10.2307/2370997 doi: 10.2307/2370997
    [3] A. Chronowski, Ternary semigroups and ternary algebras, In: Functional equations in mathematical analysis, New York: Springer, 2011,371–416. https://doi.org/10.1007/978-1-4614-0055-4_28
    [4] M. L. Santiago, S. S. Bala, Ternary semigroups, Semigroup Forum, 81 (2010), 380–388. https://doi.org/10.1007/s00233-010-9254-x doi: 10.1007/s00233-010-9254-x
    [5] J. Los, On the extending of models (Ⅰ), Fund. Math., 42 (1955), 38–54. https://doi.org/10.4064/fm-42-1-38-54 doi: 10.4064/fm-42-1-38-54
    [6] F. M. Sioson, Ideal theory in ternary semigroups, Math. Japon, 10 (1965), 63.
    [7] S. Bashir, M. Shabir, Prime left ideals in ternary semigroups, J. Adv. Res. Pure Math., 1 (2009), 1–13.
    [8] S. Bashir, M. Shabir, N. Rehman, Pure fuzzy ideals in ternary semigroups, Int. J. Algebra Stat., 1 (2012), 1–7.
    [9] S. Bashir, M. Shabir, Pure ideals in ternary semigroups, Quasigroups Related Syst., 17 (2009), 111–122.
    [10] M. Shabir, S. Bashir, Prime ideals in ternary semigroups, Asian-Eur. J. Math, 2 (2009), 141–154. https://doi.org/10.1142/S1793557109000121 doi: 10.1142/S1793557109000121
    [11] S. Bashir, X. K. Du, On weakly regular fuzzy ordered ternary semigroups, Appl. Math. Inform. Sci., 10 (2016), 2247–2254. https://doi.org/10.18576/amis/100627 doi: 10.18576/amis/100627
    [12] S. Bashir, X. K. Du, Intra-regular and weakly regular ordered ternary semigroups, Ann. Fuzzy Math. Inform., 13 (2017), 539–551.
    [13] S. Bashir, M. Fatima, M. Shabir, Regular ordered ternary semigroups in terms of bipolar fuzzy ideals, Mathematics, 7 (2019), 233. https://doi.org/10.3390/math7030233 doi: 10.3390/math7030233
    [14] S. Bashir, R. Mazhar, H. Abbas, M. Shabir, Regular ternary semirings in terms of bipolar fuzzy ideals, Comp. Appl. Math., 39 (2020), 319. https://doi.org/10.1007/s40314-020-01319-z doi: 10.1007/s40314-020-01319-z
    [15] S. Bashir, H. Abbas, R. Mazhar, M. Shabir, Rough fuzzy ternary subsemigroups based on fuzzy ideals with three-dimensional congruence relation, Comp. Appl. Math., 39 (2020), 90. https://doi.org/10.1007/s40314-020-1079-y doi: 10.1007/s40314-020-1079-y
    [16] 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
    [17] M. Shabir, S. Liaquat, S. Bashir, Regular and intra-regular semirings in terms of bipolar fuzzy ideals, Comp. Appl. Math., 38 (2019), 197. https://doi.org/10.1007/s40314-019-0974-6 doi: 10.1007/s40314-019-0974-6
    [18] M. Shabir, T. Abbas, S. Bashir, R. Mazhar, Bipolar fuzzy hyperideals in regular and intra-regular semihypergroups, Comp. Appl. Math., 40 (2021), 196. https://doi.org/10.1007/s40314-021-01574-8 doi: 10.1007/s40314-021-01574-8
    [19] M. Saqib, M. Akram, S. Bashir, Certain efficient iterative methods for bipolar fuzzy system of linear equations, J. Intell. Fuzzy Syst., 39 (2020), 3971–3985. https://doi.org/10.3233/JIFS-200084 doi: 10.3233/JIFS-200084
    [20] M. Saqib, M. Akram, S. Bashir, T. Allahviranloo, Numerical solution of bipolar fuzzy initial value problem, J. Intell. Fuzzy Syst., 40 (2021), 1309–1341. https://doi.org/10.3233/JIFS-201619 doi: 10.3233/JIFS-201619
    [21] M. Saqib, M. Akram, S. Bashir, T. Allahviranloo, A Runge-Kutta numerical method to approximate the solution of bipolar fuzzy initial value problems, Comp. Appl. Math., 40 (2021), 151. https://doi.org/10.1007/s40314-021-01535-1 doi: 10.1007/s40314-021-01535-1
    [22] M. A. Mehmood, M. Akram, M. G. Alharbi, S. Bashir, Optimization of LR-type fully bipolar fuzzy linear programming problems, Math. Prob. Eng., 2021 (2021), 1199336. https://doi.org/10.1155/2021/1199336 doi: 10.1155/2021/1199336
    [23] M. A. Mehmood, M. Akram, M. G. Alharbi, S. Bashir, Solution of fully bipolar fuzzy linear programming models, Math. Probl. Eng., 2021 (2021), 996189. https://doi.org/10.1155/2021/9961891 doi: 10.1155/2021/9961891
    [24] A. Mahboob, G. Muhiuddin, A new type of fuzzy prime subset in ordered semigroups, New Math. Nat. Comput., 17 (2021), 739–752. https://doi.org/10.1142/S1793005721500368 doi: 10.1142/S1793005721500368
    [25] J. J. Chen, S. G. Li, S. Q. Ma, X. P. Wang, m-Polar fuzzy sets: An extension of bipolar fuzzy sets, Sci. World J., 2014 (2014), 416530. https://doi.org/10.1155/2014/416530 doi: 10.1155/2014/416530
    [26] M. Sarwar, M. Akram, New applications of m-polar fuzzy matroids, Symmetry, 9 (2017), 319. https://doi.org/10.3390/sym9120319 doi: 10.3390/sym9120319
    [27] A. Al-Masarwah, m-polar fuzzy ideals of BCK/BCI-algebras, J. King Saud Univ.-Sci., 31 (2019), 1220–1226. https://doi.org/10.1016/j.jksus.2018.10.002 doi: 10.1016/j.jksus.2018.10.002
    [28] A. N. A. S. Al-Masarwah, A. G. Ahmad, A new form of generalized m-PF ideals in BCK/BCI-algebras, Ann. Commun. Math., 2 (2019), 11–16.
    [29] A. Al-Masarwah, A. G. Ahmad, m-Polar (α, β)-fuzzy ideals in BCK/BCI-algebras, Symmetry, 11 (2019), 44. doi: https://doi.org/10.3390/sym11010044 doi: 10.3390/sym11010044
    [30] A. Al-Masarwah, On (complete) normality of m-PF subalgebras in BCK/BCI-algebras, AIMS Mathematics, 4 (2019), 740–750. doi: https://doi.org/10.3934/math.2019.3.740 doi: 10.3934/math.2019.3
    [31] G. Muhiuddin, M. M. Takallo, R. A. Borzooei, Y. B. Jun, m-polar fuzzy q-ideals in BCI-algebras, J. King Saud Univ. Sci., 32 (2020), 2803–2809. https://doi.org/10.1016/j.jksus.2020.07.001 doi: 10.1016/j.jksus.2020.07.001
    [32] G. Muhiuddin, D. Al-Kadi, A. Mahboob, A. Albjedi, Interval-valued m-polar fuzzy positive implicative ideals in BCK-algebras, Math. Probl. Eng., 2021 (2021), 1042091. https://doi.org/10.1155/2021/1042091 doi: 10.1155/2021/1042091
    [33] G. Muhiuddin, D. Al-Kadi, Interval valued m-polar fuzzy BCK/BCI-algebras, Int. J. Comput. Intell., 14 (2021), 1014–1021. https://doi.org/10.2991/ijcis.d.210223.003 doi: 10.2991/ijcis.d.210223.003
    [34] M. Shabir, M. Aslam, F. Pervaiz, m-polar fuzzy ideals in LA-semigroups, (submitted for publication).
    [35] S. Bashir, S. Shahzadi, A. N. Al-Kenani, M. Shabir, Regular and intra-regular semigroups in terms of m-polar fuzzy environment, Mathematic, 9 (2021), 2031. https://doi.org/10.3390/math9172031 doi: 10.3390/math9172031
    [36] M. Shabir, Y. Nawaz, M. Aslam, Semigroups characterized by the properties of their fuzzy ideals with thresholds, World Appl. Sci. J., 14 (2021), 1851–1865.
    [37] G. Mohanraj, M. Vela, On generalized fuzzy generalized fuzzy Bi-ideals of ternary semigroups, Bull. Int. Math. Virtual Inst., 9 (2019), 441–453. https://doi.org/10.7251/BIMVI1903441M doi: 10.7251/BIMVI1903441M
    [38] N. Rehman, M. Shabir, Characterizations of Ternary Semigroups by (α,β)-fuzzy Ideals, World Appl. Sci. J, 18 (2012), 1556–1570. https://doi.org/10.5829/idosi.wasj.2012.18.11.596 doi: 10.5829/idosi.wasj.2012.18.11.596
    [39] E. H. Hamouda, A study on anti fuzzy interior ideals of ternary semigroups, Asian J. Fuzzy Appl. Math., 2 (2014), 83–88.
    [40] D. T. Ho, J. M. Garibaldi, Context modelling in fuzzy systems, In: WCCI 2012 IEEE World Congress on Computational Intelligence, 2012, Brisbane, Australia.
    [41] V. R. Daddi, Intra-regular ternary semigroups, Glob. J. Pure Appl. Math., 13 (2017), 7689–7694.
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