In this paper we proposed a new method for selecting a smoothing parameter in kernel estimator to estimate a nonparametric regression function in the presence of missing values. The proposed method is based on work on the golden ratio and Surah AL-E-Imran in the Qur'an. Simulation experiments were conducted to study a small sample behavior. The results proved the superiority the proposed on the competition method for selecting smoothing parameter.

A loS.sless (reversible) data hiding (embedding) method  inside  an image  (translating medium)  - presented   in  the  present  work  using  L_SB (least  significant  bit). technique  which  enables  us to translate   data  using an  image  (host  image),  using  a  secret  key, to  be  undetectable  without losing  any  data  or  without   changing   the  size  and  the  external   scene (visible  properties) of the image, the hid-ing data is then can  be extracted (without  losing)   by reversing &n

   In this paper , two method which deal with finding the optimal value for adaptive smoothing constant, are compared .This constant is used in adaptive Single Exponential Smoothing (ASES).

The comparing is between a method uses time domain and another uses frequency domain when the data contain outlier value for autoregressive model of order one AR(1) , or Markov Model, when the time series are stationary and non stationary with deferent samples .    

This study presents the execution of an iterative technique suggested by Temimi and Ansari (TA) method to approximate solutions to a boundary value problem of a 4th-order nonlinear integro-differential equation (4th-ONIDE) of the type Kirchhoff which appears in the study of transverse vibration of hinged shafts. This problem is difficult to solve because there is a non-linear term under the integral sign, however, a number of authors have suggested iterative methods for solving this type of equation. The solution is obtained as a series that merges with the exact solution. Two examples are solved by TA method, the results showed that the proposed technique was effective, accurate, and reliable. Also, for greater reliability, the approxim

TI1e Web service securi ty challenge is to understand  and  assess the risk  involved  in securing  a web-based  service  today, based on our existing security technology, and at the same time tmck emerging standards and  understand  how they will be used  to offset the risk in

new web services. Any  security model must  i llustrate  how data  can

now  through   an  application   and   network   topology  to  meet  the

requirements  defined  by the busi ness  wi thout exposing  the data  to undue  risk.  In this paper  we propose &n

     In this article, we aim to define a universal set consisting of the subscripts of the fuzzy differential equation (5) except the two elements  and , subsets of that universal set are defined according to certain conditions. Then, we use the constructed universal set with its subsets for suggesting an analytical method which facilitates solving fuzzy initial value problems of any order by using the strongly generalized H-differentiability. Also, valid sets with graphs for solutions of fuzzy initial value problems of higher orders are found.

          In this work, the modified Lyapunov-Schmidt reduction is used to find a nonlinear Ritz approximation of Fredholm functional defined by the nonhomogeneous Camassa-Holm equation and Benjamin-Bona-Mahony. We introduced the modified Lyapunov-Schmidt reduction for nonhomogeneous problems when the dimension of the null space is equal to two.  The nonlinear Ritz approximation for the nonhomogeneous Camassa-Holm equation has been found as a function of codimension twenty-four.

In this work, satellite images for Razaza Lake and the surrounding area
district in Karbala province are classified for years 1990,1999 and
2014 using two software programming (MATLAB 7.12 and ERDAS
imagine 2014). Proposed unsupervised and supervised method of
classification using MATLAB software have been used; these are
mean value and Singular Value Decomposition respectively. While
unsupervised (K-Means) and supervised (Maximum likelihood
Classifier) method are utilized using ERDAS imagine, in order to get
most accurate results and then compare these results of each method
and calculate the changes that taken place in years 1999 and 2014;
comparing with 1990. The results from classification indicated that