Ram Chandra Barik

Work place: Department of Computer Science & Engineering, Vikash Institute of Technology

E-mail: ramchbarik@gmail.com


Research Interests: Information-Theoretic Security, Image Processing, Network Security, Information Security, Computer Networks, Image Manipulation, Image Compression, 2D Computer Graphics, Computer Graphics and Visualization, Neural Networks, Computer systems and computational processes


Ram Chandra Barik received his M.Tech Degree in Computer Science and Engineering from Sambalpur University Institute of Information Technology (SUIIT), Sambalpur University. M.Tech research work is carried out in Indian Institute of Technology (IIT) Bhubaneswar. Received MCA Degree in Computer Science from Biju Patnaik University of Technology (BPUT) Previously he has worked as a Lecturer in the Dept. of Computer Science & Engineering in Veer Surendra Sai University of Technology, Burla and also worked as a Software Engineer at Accenture Services Pvt. Ltd. in Bangalore. Currently he is working as Asst. Professor in the department of Computer science & Engineering, Vikash Institute of technology, Bargarh, Odisha, India. His current research focuses on Bioinformatics, Image Processing, Information Security, Computer Graphics, Neural Networks and Signal Processing; recently he has developed interest in the research for localization in wireless sensor network, Pattern Recognition.

Author Articles
Automatic Dead Zone Detection in 2-D Leaf Image Using Clustering and Segmentation Technique

By Rajat Kumar Sahoo Ritu Panda Ram Chandra Barik Samrendra Nath Panda

DOI: https://doi.org/10.5815/ijigsp.2018.10.02, Pub. Date: 8 Oct. 2018

Plant is a gift of almighty to the living being in the earth. Leaf is an essential component for any types of plant including crops, fruit and vegetables. Before the scheduled decay of the leaf due to deficiency there are patches of dead zone spot or sections generally visible. This paper introduces a novel image based analysis to identify patches of dead zone spot or sections generally visible due to deficiency. Clustering, colour object based segmentation and colour transformation techniques using significant salient features identification are applied over 12 plant leaves collected naturally from gardens and crop fields. Hue, saturation and Value based and L*a*b* colour model based object analysis is being applied over diseased leaf and portion of leaf to identify the dead zone automatically. Derivative based edge analysis is being applied to identify the outline edge and dead zone segmentation in leaf image. K-means clustering has played an important role to cluster dead zone using colour based object area segmentation.

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Cascaded Factor Analysis and Wavelet Transform Method for Tumor Classification Using Gene Expression Data

By Jayakishan Meher Ram Chandra Barik Madhab Ranjan Panigrahi Saroj Kumar Pradhan Gananath Dash

DOI: https://doi.org/10.5815/ijitcs.2012.09.10, Pub. Date: 8 Aug. 2012

Correlation between gene expression profiles to disease or different developmental stages of a cell through microarray data and its analysis has been a great deal in molecular biology. As the microarray data have thousands of genes and very few sample, thus efficient feature extraction and computational method development is necessary for the analysis. In this paper we have proposed an effective feature extraction method based on factor analysis (FA) with discrete wavelet transform (DWT) to detect informative genes. Radial basis function neural network (RBFNN) classifier is used to efficiently predict the sample class which has a low complexity than other classifier. The potential of the proposed approach is evaluated through an exhaustive study by many benchmark datasets. The experimental results show that the proposed method can be a useful approach for cancer classification.

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