Dr. Kannan Balakrishnan
https://dyuthi.cusat.ac.in:443/xmlui/handle/purl/2008
2017-03-29T11:02:15ZMedian graphs, the remoteness function, periphery transversals, and geodetic number two
https://dyuthi.cusat.ac.in:443/xmlui/handle/purl/4237
Median graphs, the remoteness function, periphery transversals, and geodetic number two
Kannan, Balakrishnan; Bostjan, Brešar; Manoj, Changat; Wilfried, Imrich; Sandi, Klavzar; Matjaz, Kovse; Ajitha, Subhamathi R
A periphery transversal of a median graph G is introduced as a set of vertices
that meets all the peripheral subgraphs of G. Using this concept, median graphs
with geodetic number 2 are characterized in two ways. They are precisely
the median graphs that contain a periphery transversal of order 2 as well as
the median graphs for which there exists a profile such that the remoteness
function is constant on G. Moreover, an algorithm is presented that decides
in O(mlog n) time whether a given graph G with n vertices and m edges is a
median graph with geodetic number 2. Several additional structural properties
of the remoteness function on hypercubes and median graphs are obtained and
some problems listed
University of Ljubljana
Institute of Mathematics, Physics and Mechanics
Department of Mathematics
Preprint series, Vol. 46 (2008), 1046
2008-03-25T00:00:00ZArea and Volume Calculation of Necrotic Tissue regions of heart using Interpolation
https://dyuthi.cusat.ac.in:443/xmlui/handle/purl/4236
Area and Volume Calculation of Necrotic Tissue regions of heart using Interpolation
Kannan, Balakrishnan; Narendra, Kuber Bodhey; Malu, G
This paper attempts to develop an improved tool,
which would read two dimensional(2D) cardiac MRI images and
compute areas and volume of the scar tissue. Here the
computation would be done on the cardiac MR images to
quantify the extent of damage inflicted by myocardial infarction
on the cardiac muscle (myocardium) using Interpolation
PROCEEDINGS OF ICETECT 2011
2011-01-01T00:00:00ZUsing Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds
https://dyuthi.cusat.ac.in:443/xmlui/handle/purl/4235
Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds
Kannan, Balakrishnan; Rafidha Rahiman, K A; Sherly, K B
In our study we use a kernel based classification
technique, Support Vector Machine Regression for predicting the
Melting Point of Drug – like compounds in terms of Topological
Descriptors, Topological Charge Indices, Connectivity Indices
and 2D Auto Correlations. The Machine Learning model was
designed, trained and tested using a dataset of 100 compounds
and it was found that an SVMReg model with RBF Kernel could
predict the Melting Point with a mean absolute error 15.5854 and
Root Mean Squared Error 19.7576
PROCEEDINGS OF ICETECT 2011
2011-01-01T00:00:00ZConsensus strategies for signed profiles on graphs
https://dyuthi.cusat.ac.in:443/xmlui/handle/purl/4234
Consensus strategies for signed profiles on graphs
Kannan, Balakrishnan; Manoj, Changat; Henry, Martyn Mulder; Ajitha, Subhamathi R
The median problem is a classical problem in Location Theory: one searches for a
location that minimizes the average distance to the sites of the clients. This is for desired
facilities as a distribution center for a set of warehouses. More recently, for obnoxious
facilities, the antimedian was studied. Here one maximizes the average distance to the
clients. In this paper the mixed case is studied. Clients are represented by a profile, which
is a sequence of vertices with repetitions allowed. In a signed profile each element is
provided with a sign from f+; g. Thus one can take into account whether the client
prefers the facility (with a + sign) or rejects it (with a sign). The graphs for which all
median sets, or all antimedian sets, are connected are characterized. Various consensus
strategies for signed profiles are studied, amongst which Majority, Plurality and Scarcity.
Hypercubes are the only graphs on which Majority produces the median set for all signed
profiles. Finally, the antimedian sets are found by the Scarcity Strategy on e.g. Hamming
graphs, Johnson graphs and halfcubes
Ars Math. Contemp. 6 (2013) 127–145
2012-06-15T00:00:00Z