Tanmoy Halder ¹, Sunil Karforma ¹, Rupali Mandal ¹

Affiliations
1 Department of Computer Application, Dr. B.C. Roy Engineering College, Durgapur - 713206, West Bengal, IN

Abstract

In this paper, we have proposed a new data hiding approach using Pixel Value Difference (PVD) seganography for digital image. PVD steganography, proposed by Wu and Tsai, use non-overlapping block of two pixels to find the edge areas to hide secret message by adjusting the pixel pairs. In PVD method only the edge between two pixels within a block is detected. In order to find out more edges and to increase the capacity of PVD method we have used non-overlapping blocks of three pixels and an optimal adjustment process. The proposed method compared with different PVD approaches with respect to capacity and PSNR. This novel method could be applied to hide E-Governance related data for secure transfer.

Keywords

E-Governance, Optimal-Adjustment, Pixel-Value-Difference, Steganography

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Akash Nag ¹, Sunil Karforma ¹

Affiliations
1 Department of Computer Science, The University of Burdwan, IN

Abstract

In this paper, we present an algorithm to quickly identify conserved patterns from a set of aligned protein sequences. Using contribution statistics, the proposed method identifies a motif describing the given set of sequences, and it is flexible enough to identify variable-length wildcard regions and also identifying motif elements based on regions containing amino-acids having similar physiochemical properties. In this paper, we compare its performance against other well known motif-discovery algorithms, on three datasets: snake-toxins, insulin proteins, and methylated-DNA protein-cysteine methyltransferase active-site enzymes. When tested with 91 neurotoxin protein sequences from 45 species of Elapid snakes, the algorithm successfully generated a motif which had a 97% precision. The motif generated by our algorithm had a 92% precision on the Insulin family, and 96.5% on the MGMT family of proteins. Our algorithm is fast, efficient, outperforms on average the commonly used motif generation algorithms in terms of accuracy, and never fails to report any motifs unlike some other algorithms.

Keywords

motif generation; PROSITE; protein families; patterns; motifs; flexible wildcard regions; snake toxins; insulin

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