Sunil Karforma

Work place: Department of Computer Science, The University of Burdwan, Burdwan, WB, India



Research Interests: Network Security


Prof. Sunil Karforma completed his Bachelors in Computer Science & Engineering, and his Masters in Computer Science & Engineering, from Jadavpur University. He received his Ph. D. in Computer Science, and is presently Professor & Head of the Dept. of Computer Science at the University of Burdwan. His research interests include Network Security, E-Commerce, and Bioinformatics. He has published numerous papers in both national as well as international journals and conferences.

Author Articles
A Blockchain based Framework for Property Registration System in E-Governance

By Siddhartha Sen Sripati Mukhopadhyay Sunil Karforma

DOI:, Pub. Date: 8 Aug. 2021

In recent years, the most cutting edges and promising technology emerged is Blockchain. It has huge potential to impact various industries. The append-only distributed ledger technology (DLT) and the consensus mechanism of Blockchain can also change the dimension of E-Governance. The Electronic Property Record (EPR) systems of government have challenges like data security, integrity, secure storage of data and automated service delivery. In this paper, we discuss how Smart Contract based Blockchain technology can effectively be used to address the challenges of EPR System over the existing available systems. We propose a Smart Contract based permissioned blockchain framework which is an innovative approach, especially in the Electronic Property Registration domain of E-Governance in India. The objective of our proposed framework is firstly to implement Smart Contract solving the security problems like confidentiality, integrity, authentication, and secondly to ensure secure storage of electronic records by defining access rules for the stakeholders of the proposed framework. Moreover, we address the issues of single-point-of-failure, data inter-operability between the organizations involved for sharing and verification of property information among various stakeholders.

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An Efficient Clustering Algorithm for Spatial Datasets with Noise

By Akash Nag Sunil Karforma

DOI:, Pub. Date: 8 Jul. 2018

Clustering is the technique of finding useful patterns in a dataset by effectively grouping similar data items. It is an intense research area with many algorithms currently available, but practically most algorithms do not deal very efficiently with noise. Most real-world data are prone to containing noise due to many factors, and most algorithms, even those which claim to deal with noise, are able to detect only large deviations as noise. In this paper, we present a data-clustering method named SIDNAC, which can efficiently detect clusters of arbitrary shapes, and is almost immune to noise – a much desired feature in clustering applications. Another important feature of this algorithm is that it does not require apriori knowledge of the number of clusters – something which is seldom available.

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Improving the Performance of Fuzzy Minimum Spanning Tree based Routing Process through P-Node Fuzzy Multicasting Approach in MANET

By Soham Bandyopadhyay Sunil Karforma

DOI:, Pub. Date: 8 Jun. 2018

Mobile Ad-hoc Network (MANET) is mostly decentralized and self-adjustable network system. It is significant to optimize the overall network energy utilization and improve packet sending performance by reducing the errors, generated due to different real-life environmental effects. In this paper, considering atmospheric, environmental change and varying distance for topological change, we try to generate the routing cost. By introducing m-minimum (membership value as m) triangular fuzzy number at interval based cost and energy of the network, we try to handle the uncertain environment. Here we generate both fuzzy minimum spanning tree (FMST) for a given n- nodes network and p-node fuzzy multicast minimum spanning tree (pFMMST), in fuzzy interval based format. Applying the fuzzy credibility distribution we modify the network routing cost and energy utilization for both FMST and pFMMST. Comparing the routing cost and residual energy for FMST and pFMMST of MANET, it is concluded that, pFMMST is better FMST based packet routing approach, with minimum routing cost, optimized total energy utilization and best possible technique to reduce the error which is generated due to the deviation of interval of upper and lower limit data in route cost and residual energy.

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Adaptive Dictionary-based Compression of Protein Sequences

By Akash Nag Sunil Karforma

DOI:‚Äč, Pub. Date: 8 Sep. 2017

This paper introduces a simple and fast lossless compression algorithm, called CAD, for the compression of protein sequences. The proposed algorithm is specially suited for compressing proteomes, which are the collection of all proteins expressed by an organism. Maintaining a changing dictionary of actively used amino-acid residues, the algorithm uses the adaptive dictionary together with Huffman coding to achieve an average compression rate of 3.25 bits per symbol, better than most other existing protein-compression and general-purpose compression algorithms known to us. With an average compression ratio of 2.46:1 and an average compression rate of 1.32M residues/sec, our algorithm outperforms every other compression algorithm for compressing protein sequences in terms of the balance in compression-time and compression rate.

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