Jaccard Coefficients based Clustering of XML Web Messages for Network Traffic Aggregation

This paper provides static efficient clustering model based simple Jaccard coefficients that supports XML messages aggregator in order to potentially reduce network traffic. The proposed model works by grouping only highly similar messages with the aim to provide messages with high redundancy for web aggregators. Web messages aggregation has become a significant solution to overcome network bottlenecks and congestions by efficiently reducing network volume by aggregating messages together removing their redundant information. The proposed model performance is compared to both K-Means and Principle Component Analysis (PCA) combined with K-Means. Jaccard based clustering model has shown potential performance as it only consumes around %32 and %25 processing time in comparison with K-Means and PCA combined with K-Means respectively. Quality measure (Aggregator Compression Ratio) has overcome both benchmark models

Traffic signs recognitionusing cuckoo search algorithm and Curvelettransform with image processing methods

Compliance with traffic signs is one of the most important things to follow to avoid traffic accidents as well as compliance with traffic rules in terms of parking, speed control, and other traffic sings. Progress in different areas, such as self-propelled car manufacturing or the production of devices that help the visually impaired, require values to find a way to determine traffic signals with high precision in this research, The first step is to take a picture of the traffic sign and apply some digital image processing techniques to increase image contrast and eliminate noise in the image, the second step resize of origin image , the third step convert color to(YCbCr, HSB) or stay on RGB, the fourth step image is disassembled using curvelet transform and get coefficients , and the last step using cuckoo search algorithm to recognition sings traffics ,the MATLAB (2011b) program was used to implement the proposed algorithm . After applying this method to a set of traffic the percentage of discrimination of traffic signs was yellow 93%, green 94%, blue 94.5%, red 96%.

CHOOSING APPROPRIATE IMPUTATION METHODS FOR MISSING DATA: A DECISION ALGORITHM ON METHODS FOR MISSING DATA

Missing values commonly happen in the realm of medical research, which is regarded creating a lot of bias in case it is neglected with poor handling. However, while dealing with such challenges, some standard statistical methods have been already developed and available, yet no credible method is available so far to infer credible estimates. The existing data size gets lowered, apart from a decrease in efficiency happens when missing values is found in a dataset. A number of imputation methods have addressed such challenges in early scholarly works for handling missing values. Some of the regular methods include complete case method, mean imputation method, Last Observation Carried Forward (LOCF) method, Expectation-Maximization (EM) algorithm, and Markov Chain Monte Carlo (MCMC), Mean Imputation (Mean), Hot Deck (HOT), Regression Imputation (Regress), K-nearest neighbor (KNN),K-Mean Clustering, Fuzzy K-Mean Clustering, Support Vector Machine, and Multiple Imputation (MI) method. In the present paper, a simulation study is attempted for carrying out an investigative exploration into the efficacy of the above mentioned archetypal imputation methods along with longitudinal data setting under missing completely at random (MCAR). We took out missingness from three cases in a block having low missingness of 5% as well as higher levels at 30% and 50%. With this simulation study, we concluded LOCF method having more bias than the other methods in most of the situations after carrying out a comparison through simulation study.

Strongly b star(Sb*) – cleavability(splitability)

Poongothai, R. Parimelazhagan[5] introduced some new type of seperation axioms and study some of their basic properties. Some implications between , and axioms are also obtained. In this paper we studied the concept of cleavability over these spaces: ( sb*- sb*- , sb*- ) as following: 1- If is a class of topological spaces with certain properties and if X is cleavable over then X 2- If is a class of topological spaces with certain properties and if Y is cleavable over then Y

Influence of Varying Temperature and Concentration on MHD Peristaltic Transport for Jeffrey Fluid with Variable Viscosity through Porous Channel

The present paper deals with the peristaltic motion of Jeffrey fluid with varying temperature and concentration through a porous medium in a coaxial uniform circular tube. The fluid is assumed to be non-Newtonian, namely Jeffrey fluid. The inner tube is uniform, while the outer flexible tube has a sinusoidal wave traveling down its wall. The analytical formulas of the velocity and temperature have been obtained in terms of the Bessel function of first and second kinds. The numerical formula of the axial velocity, temperature and concentration are obtained as functions of the physical parameters of the problem (Darcy number, magnetic parameter, thermal Grashof number, Reynolds number, Prandtl number, and Schmidt number) with other physical parameters are obtained. The Influence of physical parameters of the problem on this formula are discussed numerically and illustrated graphically through a set of figures.

Some Results on Symmetric Reverse ∗-n-Derivations

In this paper, the commuting and centralizing of symmetric reverse ∗- -derivation on Lie ideal are studied and the commutativity of prime ∗-ring with the concept of symmetric reverse ∗- -derivations are proved under certain conditions.

Using inverse triangular and hyperbolic functions of Al-Tememe acceleration methods of first kind for improving the numerical integration results

The main aim of this work is to introduce the acceleration methods which are called the inverse triangular acceleration methods and inverse hyperbolic acceleration methods, which are considered a series of numerated methods. In general, these methods are named as AL-Tememe’s acceleration methods of first kind discovered by (Ali Hassan Mohammed). They are very beneficial to acceleration the numerical results for definite integrations with continuous integrands which are of 2nd order main error, with respect to the accuracy and the number of the used subintervals and the speed of obtaining results. Especially, for accelerating the results which are obviously obtained by trapezoidal and midpoint methods. Moreover, these methods could be enhancing the results of numerical of the ordinary differential equations, where the main errors are of 2nd order.

Robust logistic regression in the presence of high leverage points

In this article we conceder the logistic regression model with high leverage points. For the logistic regression model with a binary response, we suggested a new robust approach called robust logistic regression (RLR) based on the robust mahalanobis distance (RMD) which depends on the minimum volume ellipsoid (MVE) estimators. The RMD is computed by using the algorithm of stochastic gradient descent (SGD). In order to assist the new suggested approach we compare it with some existing method such as maximum likelihood estimator and robust M-estimator in logistic regression model. The simulation study points that the RLR has supreme performances throw some measurement comparison.

On Quadratic Unbiased Estimator for Variance Components of One-Way Repeated Measurements Model

In this paper, we investigate the estimator of variance components of one-way repeated measurements model (RMM) using MINQUE-principle (Rao 1971a and Rao 1971b) and method of MINQUE (1) which using priori values for components of variance.

Convolution Neural Networks for Blind Image Steganalysis: A Comprehensive Study

Recently, Convolution Neural Network is widely applied in Image Classification, Object Detection, Scene labeling, Speech, Natural Language Processing and other fields. In this comprehensive study a variety of scenarios and efforts are surveyed since 2014 at yet, in order to provide a guide to further improve future researchers what CNN-based blind image steganalysis are presented its architecture, performance and limitations. Long-standing and important problem in image steganalysis difficulties mainly lie in how to give high accuracy and low payload in stego or cover images for improving performance of the network.