• 제목/요약/키워드: edge dominating set

검색결과 5건 처리시간 0.016초

NOVEL DECISION MAKING METHOD BASED ON DOMINATION IN m-POLAR FUZZY GRAPHS

  • Akram, Muhammad;Waseem, Neha
    • 대한수학회논문집
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    • 제32권4호
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    • pp.1077-1097
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    • 2017
  • In this research article, we introduce certain concepts, including domination, total domination, strong domination, weak domination, edge domination and total edge domination in m-polar fuzzy graphs. We describe these concepts by several examples. We investigate some related properties of certain dominations in m-polar fuzzy graphs. We also present a decision making method based on domination in m-polar fuzzy graphs.

ON A VARIANT OF VERTEX EDGE DOMINATION

  • S.V. SIVA RAMA RAJU
    • Journal of applied mathematics & informatics
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    • 제41권4호
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    • pp.741-752
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    • 2023
  • A new variant of vertex edge domination, namely semi total vertex edge domination has been introduced in the present paper. A subset S of the vertex set V of a graph G is said to be a semi total vertex edge dominating set(stved - set), if it is a vertex edge dominating set of G and each vertex in S is within a distance two of another vertex in S. An stved-set of G having minimum cardinality is said to be an γstve(G)- set and its cardinality is denoted by γstve(G). Bounds for γstve(G) - set have been given in terms of various graph theoretic parameters and graphs attaining the bounds have been characterized. In particular, bounds for trees have been obtained and extremal trees have been characterized.

DOUBLE VERTEX-EDGE DOMINATION IN TREES

  • Chen, Xue-Gang;Sohn, Moo Young
    • 대한수학회보
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    • 제59권1호
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    • pp.167-177
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    • 2022
  • A vertex v of a graph G = (V, E) is said to ve-dominate every edge incident to v, as well as every edge adjacent to these incident edges. A set S ⊆ V is called a double vertex-edge dominating set if every edge of E is ve-dominated by at least two vertices of S. The minimum cardinality of a double vertex-edge dominating set of G is the double vertex-edge domination number γdve(G). In this paper, we provide an upper bound on the double vertex-edge domination number of trees in terms of the order n, the number of leaves and support vertices, and we characterize the trees attaining the upper bound. Finally, we design a polynomial time algorithm for computing the value of γdve(T) for any trees. This gives an answer of an open problem posed in [4].

THE OUTER-CONNECTED VERTEX EDGE DOMINATION NUMBER OF A TREE

  • Krishnakumari, Balakrishna;Venkatakrishnan, Yanamandram Balasubramanian
    • 대한수학회논문집
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    • 제33권1호
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    • pp.361-369
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    • 2018
  • For a given graph G = (V, E), a set $D{\subseteq}V(G)$ is said to be an outer-connected vertex edge dominating set if D is a vertex edge dominating set and the graph $G{\backslash}D$ is connected. The outer-connected vertex edge domination number of a graph G, denoted by ${\gamma}^{oc}_{ve}(G)$, is the cardinality of a minimum outer connected vertex edge dominating set of G. We characterize trees T of order n with l leaves, s support vertices, for which ${\gamma}^{oc}_{ve}(T)=(n-l+s+1)/3$ and also characterize trees with equal domination number and outer-connected vertex edge domination number.

Finger Vein Recognition Based on Multi-Orientation Weighted Symmetric Local Graph Structure

  • Dong, Song;Yang, Jucheng;Chen, Yarui;Wang, Chao;Zhang, Xiaoyuan;Park, Dong Sun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제9권10호
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    • pp.4126-4142
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    • 2015
  • Finger vein recognition is a biometric technology using finger veins to authenticate a person, and due to its high degree of uniqueness, liveness, and safety, it is widely used. The traditional Symmetric Local Graph Structure (SLGS) method only considers the relationship between the image pixels as a dominating set, and uses the relevant theories to tap image features. In order to better extract finger vein features, taking into account location information and direction information between the pixels of the image, this paper presents a novel finger vein feature extraction method, Multi-Orientation Weighted Symmetric Local Graph Structure (MOW-SLGS), which assigns weight to each edge according to the positional relationship between the edge and the target pixel. In addition, we use the Extreme Learning Machine (ELM) classifier to train and classify the vein feature extracted by the MOW-SLGS method. Experiments show that the proposed method has better performance than traditional methods.