The haplotype association analysis has been proposed to capture the collective behavior of sets of variants by testing the association of each set instead of individual variants with the disease.Such an analysis typically involves a list of unphased multiple-locus genotypes with potentially sparse frequencies in cases and controls.It starts with inferring haplotypes from genotypes followed by a haplotype co-classification and marginal screening for disease-associated haplotypes.Unfortunately,phasing uncertainty may have a strong effects on the haplotype co-classification and therefore on the accuracy of predicting risk haplotypes.Here,to address the issue,we propose an alternative approach:In Stage 1,we select potential risk genotypes inste

We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). St

In this paper, we develop the work of Ghawi on close dual Rickart modules and discuss y-closed dual Rickart modules with some properties. Then, we prove that, if are y-closed simple -modues and if -y-closed is a dual Rickart module, then either Hom ( ) =0 or . Also, we study the direct sum of y-closed dual Rickart modules.

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

In folk medicine there are various medicinal amalgamation possessing hepatoprotective activity. This activity is of significance because several toxins cause liver injury. Hence, many pharmaceutical companies are targeting herbal medicines for the treatment of liver abnormalities and towards evolving a safe and effective formulation with desired route of administration. In current review we have focused on the studies showing hepatoprotective effect using marine compounds and plant derived compounds. Liver disorder, a global health problem, usually include acute or chronic hepatitis, heptoses, and cirrhosis. It may be due to toxic chemicals and certain antibiotics. Uncontrolled consumption of alcohol also affects liver in an unhealthy wa

Multi-document summarization is an optimization problem demanding optimization of more than one objective function simultaneously. The proposed work regards balancing of the two significant objectives: content coverage and diversity when generating summaries from a collection of text documents.

     Any automatic text summarization system has the challenge of producing high quality summary. Despite the existing efforts on designing and evaluating the performance of many text summarization techniques, their formulations lack the introduction of any model that can give an explicit representation of – coverage and diversity – the two contradictory semantics of any summary. In this work, the design of

    In this present paper,  an experimental study of some plasma characteristics in dielectric barrier discharge (DBD) system using several variables, such as different frequencies and using two different electrodes metals(aluminium (Al) and copper (Cu)), is represented. The discharge plasma was produced by an AC power supply source of 6 and 7 kHz frequencies for the nitrogen gas spectrum and for two different electrodes metals(Al and Cu). Optical emission spectrometer was used to study plasma properties (such as electron temperature ( ), electron number density ( ), Debye length ( ), and plasma frequency ( )). In addition, images were analysed for the plasma emission intensity at atmospheric air pressure.

In this research, the methods of Kernel estimator (nonparametric density estimator) were relied upon in estimating the two-response logistic regression, where the comparison was used between the method of Nadaraya-Watson and the method of Local Scoring algorithm, and optimal Smoothing parameter λ was estimated by the methods of Cross-validation and generalized Cross-validation, bandwidth optimal λ has a clear effect in the estimation process. It also has a key role in smoothing the curve as it approaches the real curve, and the goal of using the Kernel estimator is to modify the observations so that we can obtain estimators with characteristics close to the properties of real parameters, and based on medical data for patients with chro