Kashi Nath Dey

Secure energy efficient routing protocol for wireless sensor network

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.

Automatic contrast enhancement of low-light images based on Naka-Rushton visual adaptation in non-sub-sampled shearlet domain

In this paper a non-sub-sampled shearlet transform model for image enhancement to improve image quality by maximizing the information content in the input image is proposed and implemented. This is inspired by two basic human visual features: i) visual adaptation, ii) local contrast enhancement of color image. We achieve the goal in different stages: In initial stage evolved upon the shearlet transformation of an image from image space to a low-pass and high-pass images in shearlet space and then to the visual response space. The visual adaptation is generally implemented with the Naka-Rushton equation, a rational transformation which allows to accurately compress the range of the input image. The later stage consists in a variational method able to perform local contrast enhancement with Weber-Fechners law. After modifying the response values, the transformation can be reversed to produce the resulting image. The proposed method Naka-Rushton shearlet Image Enhancement (NRSIE) has been implemented on several color images. The results are compared with two other popular color image enhancement techniques along with visual analysis, detail and background variance. It has been found that NRSIE scales better results compared to two other popular methods.

Traffic organization by utilization of resources through grid computing concept

Recent progress in grid computing concept and its allied application technologies has helped us to device an efficient tool for heavy workload management. Concept of this middleware technology supports to build more and more complex application which can process large real time data set. Mobilization of under utilized resources among the needy processes can easily be carried out with distributed grid computing concept. In a real life scenario of a city, we have experienced traffic hazards as a daily phenomenon. There we often trail through to a well known regular path to avail our destination. This leads to an over utilization of the path, which can cause in serious congestion and delay. In our work we have considered the crisscross roads as resources, those cross sections as junctions and different kind of vehicles as processes. We have designed and simulated a method which has shown effective prospect in computing the real time congestion scenario between a source and destination pair. This mode of computing will help in efficient traffic management by directing the vehicles for some alternative well suited options of path to reach its destination more conveniently. Some effective decision makings in this regard have been performed depending upon the calculated congestion.

On degree sequence

I ndi got her BS in Computer Science from Cal Poly and began her masters at Colorado State. She then worked as a software engineer, later managing Web applications that focused on the user. Her concepts in mental models derive from attempting to bridge the developer-user gap. Her expertise ranges from structuring crossfunctional teams, to managing participant recruiting, and conducting user interviews, thereby creating effective tools for exchanging results.

Gene ranking: An entropy & decision tree based approach

The Cancer disease involves abnormal cell growth and has the potential to spread to other parts of the body. Today, technology has provided us with many methods to study the pattern of thousands of cancer gene expressions simultaneously. Often microarray gene expression data comprises of a huge number of genes and a very small number of samples or observations. Our task is to identify those genes that are most significant in the expression of a particular disease, in this case, cancer. In order to achieve that, it is useful to rank the genes. In this article, we propose a novel method for ranking genes using Relative Entropy and Decision Trees. Relative Entropy has been used to reduce the dimensionality of the microarray dataset and rank the genes. The final reduced set of genes is then used for classification using decision trees with 10 folds cross-validation. The proposed method has been applied on eight benchmark datasets, and results show that it can reach 70-100 % classification accuracy with a very few dominant genes.

An improved fuzzy based approach to impute missing values in DNA microarray gene expression data with collaborative filtering

DNA microarray experiments normally generate gene expression profiles in the form of high dimensional matrices. It may happen that DNA microarray gene expression values contain many missing values within its data due to several reasons like image disruption, hybridization error, dust, moderate resolution etc. It will be very unfortunate if these missing values affect the performance of subsequent statistical and machine learning experiments significantly. There exist various missing value estimation algorithms. In this work we have proposed a modification to the existing imputation approach named as Collaborative Filtering Based on Rough-Set Theory (CFBRST) [10]. This proposed approach (CFBRSTFDV) uses Fuzzy Difference Vector (FDV) along with Rough Set based Collaborative Filtering that analyzes historical interactions and helps to estimate the missing values. This is a suggestion based system that works on the principle of how suggestion of items or products arrive to an individual while using FB, Twitter or looking for books in Amazon. We have applied our proposed algorithm on two benchmark dataset SPELLMAN & Tumor Cell (GDS2932) and the experiments show that the modified approach, CFBRSTFDV, outperforms the other existing state-of-the art methods as far as RMSE measures are concerned, particularly when we increase the number of missing values.

A novel gene ranking method using Wilcoxon rank sum test and genetic algorithm

Over the last few decades, a large amount of research work has been carried on genomic data. The cancer disease make cells in specific tissues in the body undergo uncontrolled division which results in the malignant growth or tumour. Today, DNA microarray technologies allow us to simultaneously monitor the expression pattern of thousands of genes. Microarray gene expression data are characterised by a very high dimensionality genes, and a relatively small number of samples observations. If one wants to identify all those genes from these thousands of gene expressions which are responsible for the disease like cancer, then it is useful to rank the genes. In this paper, we have proposed a novel gene ranking method based on Wilcoxon Rank Sum Test and genetic algorithm. WRST has been used for reducing dimensionality and genetic algorithm for finding out those differentially expressed genes. The final subset of genes has been cross-validated using k fold LOOCV k varied for different dataset method and thereafter used for classification of data using SVM with linear kernel. At first the proposed method has been applied on two relatively new benchmark datasets, like GDS4382 colorectal cancer dataset and GDS4794 small cell lung cancer dataset and the results show that the proposed method can reach up to 100% classification accuracy with very few dominant genes, which indirectly validates the biological and statistical significance of the proposed method. After that it is also applied on five real-life datasets and the results are compared with one of the recent state of the art approach on the basis of % of Accuracy, Sensitivity, and Specificity etc.

An Improved Fuzzy Based Missing Value Estimation in DNA Microarray Validated by Gene Ranking

Most of the gene expression data analysis algorithms require the entire gene expression matrix without any missing values. Hence, it is necessary to devise methods which would impute missing data values accurately. There exist a number of imputation algorithms to estimate those missing values. This work starts with a microarray dataset containing multiple missing values. We first apply the modified version of the fuzzy theory based existing method LRFDVImpute to impute multiple missing values of time series gene expression data and then validate the result of imputation by genetic algorithm GA based gene ranking methodology along with some regular statistical validation techniques, like RMSE method. Gene ranking, as far as our knowledge, has not been used yet to validate the result of missing value estimation. Firstly, the proposed method has been tested on the very popular Spellman dataset and results show that error margins have been drastically reduced compared to some previous works, which indirectly validates the statistical significance of the proposed method. Then it has been applied on four other 2-class benchmark datasets, like Colorectal Cancer tumours dataset GDS4382, Breast Cancer dataset GSE349-350, Prostate Cancer dataset, and DLBCL-FL Leukaemia for both missing value estimation and ranking the genes, and the results show that the proposed method can reach 100% classification accuracy with very few dominant genes, which indirectly validates the biological significance of the proposed method.

An ensemble based missing value estimation in DNA microarray using artificial neural network

DNA microarrays are normally used to measure the expression values of thousands of several genes simultaneously in the form of large matrices. This raw gene expression data may contain some missing cells. These missing values may affect the analysis performed subsequently on these gene expression data. Several imputation methods, like K-Nearest Neighbor Imputation (KNNImpute), Singular Value Decomposition Imputation (SVDImpute), Local Least Square Imputation (LLSImpute), Bayesian Principal Component Analysis (BPCAImpute) etc. have already been proposed to impute those missing values. In this work we have proposed an ensemble classifier based Artificial Neural Network implementation, ANNImpute, to enhance the accuracy of the missing value imputation technique by applying Two Layer Perceptron Learning algorithm. Ensemble classification is done on the parameters such as learning rate a, weight vector & bias. We have applied our algorithm on two benchmark datasets like SPELLMAN and Tumour (GDS2932) and the results show that this approach performs well compared to the other existing methods as far as RMSE measures are concerned.

Medical Image Fusion Using Daubechies Complex Wavelet and Near Set

Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities. It helps to improve the imaging quality and reduces the redundancy, which improves the clinical applicability of medical images for diagnosis. The idea is to improve the content of an image by fusing images of multiple modalities viz. positron emission tomography (PET), computerized tomography (CT), single-photon emission computerized tomography (SPECT), magnetic resonance imaging (MRI) etc. Registration is an important step before fusion. In general, the problem of image registration can be identified as the determination of geometric transformations between the respective source image and target image.