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Ashok Kumar, A.
- Algorithms to Find Vertex-to-Clique Center in a Graph using BC-Representation
Abstract Views :117 |
PDF Views:4
Authors
Affiliations
1 Department of Computer Science, Alagappa Government Arts College, Karaikudi - 630003, IN
2 Research Department of Mathematics, St. Xaviers College (Autonomous), Palayamkottai − 627002, IN
3 Ananda College, Devakottai, IN
1 Department of Computer Science, Alagappa Government Arts College, Karaikudi - 630003, IN
2 Research Department of Mathematics, St. Xaviers College (Autonomous), Palayamkottai − 627002, IN
3 Ananda College, Devakottai, IN
Source
International Journal of Advanced Networking and Applications, Vol 4, No 6 (2013), Pagination: 1809-1811Abstract
In this paper, we introduce algorithms to find the vertex-to-clique (or (V, ζ ))-distance d(v, C ) between a vertex v and a clique C in a graph G, (V, ζ )-eccentricity e1 (v) of a vertex v, and (V, ζ )-center Z1(G) of a graph G usingBC - representation. Moreover, the algorithms are proved for their correctness and analyzed for their time complexity.Keywords
Clique, Distance, Eccentricity, Radius, Center, Binary Count.- Algorithm to Find Clique Graph
Abstract Views :148 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, Alagappa Government Arts College, Karaikudi 630003, IN
2 Research Department of Mathematics, St. Xaviers College(Autonomous), Palayamkottai 627002, IN
3 Ananda College, Devakottai, IN
1 Department of Computer Science, Alagappa Government Arts College, Karaikudi 630003, IN
2 Research Department of Mathematics, St. Xaviers College(Autonomous), Palayamkottai 627002, IN
3 Ananda College, Devakottai, IN
Source
International Journal of Advanced Networking and Applications, Vol 4, No 1 (2012), Pagination: 1501-1502Abstract
Let V = {1, 2, 3, …, n} be the vertex set of a graph G, ℘ (V) the powerset of V and A ∈ ℘ (V ). Then A can be represented as an ordered n-tuple (x1x2x3…xn) where xi = 1 if i ∈ A, otherwise xi = 0 (1≤ i ≤ n). This representation is called binary count (or BC) representation of a set A and denoted as BC(A). Given a graph G of order n, every integer m in S = {0, 1, 2, …, 2n-1} corresponds to a subset A of V and vice versa. In this paper we introduce and discuss a sequential algorithm to find the clique graph K(G) of a graph G using the BC representation.Keywords
Binary Count, Clique, Clique Graph, Powerset.- Algorithm to Find all Cliques in a Graph
Abstract Views :117 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, St. Xavier’s College (Autonomous), Palayamkottai-627002, IN
2 Research Department of Mathematics, St. Xavier’s College (Autonomous), Palayamkottai-627002, IN
1 Department of Computer Science, St. Xavier’s College (Autonomous), Palayamkottai-627002, IN
2 Research Department of Mathematics, St. Xavier’s College (Autonomous), Palayamkottai-627002, IN
Source
International Journal of Advanced Networking and Applications, Vol 2, No 2 (2010), Pagination: 597-601Abstract
Let V={1, 2, 3, . . . , n} be the vertex set of a graph G, P(V) the powerset of V and A∈P(V). Then A can be represented as an ordered n-tuple (x1x2x3 . . .xn) where xi=1 if i∈A, otherwise xi=0 (1≤i≤n). This representation is called binary count (or BC) representation of a set A and denoted as BC(A). Given a graph G of order n, it is shown that every integer m in S={0, 1, 2, . . . , 2n-1} corresponds to a subset A of V and vice versa. We introduce algorithms to find a subset A of the vertex set V={1, 2, 3, . . . , n} of a graph G that corresponds to an integer m in S={0, 1, 2, . . . , 2n-1}, verify whether A is a subset of any other subset B of V and also verify whether the sub graph <A> induced by the set A is a clique or not using BC representation. Also a general algorithm to find all the cliques in a graph G using BC representation is introduced. Moreover we have proved the correctness of the algorithms and analyzed their time complexities.Keywords
Adjacency Matrix, Binary Count, Clique, Powerset, Subset.- Robust Lossless Secure Image Steganography Using Spiral Scan
Abstract Views :160 |
PDF Views:0
Authors
Affiliations
1 Department of CSE, Y.S.R.Engineering College of YV University, Proddatur, Andhra Pradesh, IN
2 Department of Physics, Y.S.R.Engineering College of YV University, Proddatur, Andhra Pradesh, IN
1 Department of CSE, Y.S.R.Engineering College of YV University, Proddatur, Andhra Pradesh, IN
2 Department of Physics, Y.S.R.Engineering College of YV University, Proddatur, Andhra Pradesh, IN
Source
International Journal of Advanced Networking and Applications, Vol 9, No 5 (2018), Pagination: 3596-3600Abstract
Steganography is the principles and techniques of embedding data within other data. Cryptography is the principles and techniques of changing the data one form to another form. Image Steganography is the process of hiding data within an image. Steganography along with encryption techniques provides an additional security to the data. Several techniques exist for image steganography, in this work, a new lossless image steganography technique along with cryptographic method is presented. Lossless compression is a class of algorithms that allows the original data to be perfectly reconstructed from the compressed data. Present work concentrates the lower nibble of pixels in the cover image for embedding the information; further encryption techniques will be applied. It is not possible for the hacker to retrieve the secured data from the cover image.Keywords
Compression, Cover Image, Cryptography, Lossless, Nibble, Steganograpy.References
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