Rituparna Chaki

Bio: Rituparna Chaki is an academic researcher from University of Calcutta. The author has contributed to research in topics: Routing protocol & Wireless sensor network. The author has an hindex of 16, co-authored 131 publications receiving 1514 citations. Previous affiliations of Rituparna Chaki include Calcutta Institute of Engineering and Management & Information Technology University.

Application of Data mining in Protein sequence Classification

Abstract: Protein sequence classification involves feature selection for accurate classification. Popular protein sequence classification techniques involve extraction of specific features from the sequences. Researchers apply some well-known classification techniques like neural networks, Genetic algorithm, Fuzzy ARTMAP, Rough Set Classifier etc for accurate classification. This paper presents a review is with three different classification models such as neural network model, fuzzy ARTMAP model and Rough set classifier model. This is followed by a new technique for classifying protein sequences. The proposed model is typically implemented with an own designed tool and tries to reduce the computational overheads encountered by earlier approaches and increase the accuracy of classification.

An efficient method for PET image denoising by combining multi-scale transform and non-local means

Abstract: The diagnosis of dementia, particularly in the early stages is very much helpful with Positron emission tomography (PET) image processing. The most important challenges in PET image processing are noise removal and region of interests (ROIs) segmentation. Although denoising and segmentation are performed independently, but the performance of the denoising process significantly affects the performance of the segmentation process. Due to the low signals to noise ratio and low contrast, PET image denoising is a challenging task. Individual wavelet, curvelet and non-local means (NLM) based methods are not well suited to handle both isotropic (smooth details) and anisotropic (edges and curves) features due to its restricted abilities. To address these issues, the present work proposes an efficient denoising framework for reducing the noise level of brain PET images based on the combination of multi-scale transform (wavelet and curvelet) and tree clustering non-local means (TNLM). The main objective of the proposed method is to extract the isotropic features from a noisy smooth PET image using tree clustering based non-local means (TNLM). Then curvelet-based denoising is applied to the residual image to extract the anisotropic features such as edges and curves. Finally, the extracted anisotropic features are inserted back into the isotropic features to obtain an estimated denoised image. Simulated phantom and clinical PET datasets have been used in this proposed work for testing and measuring the performance in the medical applications, such as gray matter segmentation and precise tumor region identification without any interaction with other structural images like MRI or CT. The results in the experimental section show that the proposed denoising method has obtained better performance than existing wavelet, curvelet, wavelet-curvelet, non-local means (NLM) and deep learning methods based on the preservation of the edges. Qualitatively, a notable gain is achieved in the proposed denoised PET images in terms of contrast enhancement than other existing denoising methods.

ORRP: Optimal Reactive Routing Protocol for Mobile Ad-Hoc Networks

Abstract: An ad-hoc network is a self-supporting collection of mobile nodes that happen to exist within a close proximity in an interval of time In this paper, a reactive routing protocol has been proposed that computes the shortest path in between any source-destination pair on demand The approach, unlike other reactive protocols, finds a loop-free, optimal path between the end nodes It does not use broadcasting like DSR or some other reactive protocols In fact ORRP exchanges only (n-1) control packets, in the best case A comparative performance evaluation establishes the advantages of ORRP The paper is a substantial extension of our earlier work reported in [S Dulta et al, 2006]

Secure energy efficient routing protocol for wireless sensor network

Abstract: Abstract The ease of deployment of economic sensor networks has always been a boon to disaster management applications. However, their vulnerability to a number of security threats makes communication a challenging task. This paper proposes a new routing technique to prevent from both external threats and internal threats like hello flooding, eavesdropping and wormhole attack. In this approach one way hash chain is used to reduce the energy drainage. Level based event driven clustering also helps to save energy. The simulation results show that the proposed scheme extends network lifetime even when the cluster based wireless sensor network is under attack.

BHAPSC: A New Black Hole Attack Prevention System in

Abstract: Black hole attack is one kind of routing disturbing attacks and can bring great damage to all clusters of a MANET. Security remains a major challenge for these networks due to their features of open medium, dynamically changing topologies, and infrastructure-less property. As a result, an efficient algorithm to detect black hole attack is important. This paper proposes and evaluates strategies to detecting black hole attacks and build reliable and secure inter cluster routing in wireless ad hoc networks. We choose AODV protocol to test our algorithm and ns-3 as our simulation tool.

PORs: Proofs of Retrievability for Large Files

Abstract: In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.