Journal of Kufa for Mathematics and Computer

Using the concept of T*-open set, we introduce
and study Almost contra –T* -continuous
functions
Several properties and characterizations of these
function are given.

The Kaplan–Meier estimator is a very popular
that provides better estimates to determine the
median when the sample size is reasonably
large. The aim of this research was mainly
concerned with a study and analysis an
estimation of the survivorship time of real
data of breast cancer patients in Iraq.

In this work, we present a new
approach for solving Elliptic Curve
Discrete Logarithm Problem. This method
provides a new access to the field of
attacking methods the Elliptic Curve
Cryptosystems. Beside this paper gives a
program for executing the proposed method
by using MATLAP.

In this paper an optimal (Bayesian) fixed
sample size procedure for selecting the better of
two Poisson populations is proposed and studied
. Bayesian decision-theoretic approach with
general loss function and Gamma priors are used
to construct this procedure .
The numerical result of this procedure are
given with different loss functions constant ,
linear and quadratic , in one equation we can
obtain the Bayes risk for the three types of the
loss functions : constant , linear and quadratic .
in this paper the numerical results are given by
using Math Works Matlab ver. 7.10.0 .

This paper is concerned with Modified
Double Stage Shrinkage Bayesian (DSSB)
Estimator for lowering the mean squared error of
classical estimator ˆθ for the scale parameter (θ)
of an Exponential Distribution in suitable region
(R) around available prior knowledge (θ0) about
the actual value (θ) as initial estimate as well as
to reduce the cost of observation. In situation
where the observations are time consuming or
very costly, a “Double Stage procedure “can be
used to reduce the Expected Sample Size needed
to obtain the estimator. This estimator has been
showing a smaller Mean Squared Error for
certain choice of the shrinkage weight factor ψ(⋅)
and for acceptance region R.
Expressions for Bias, Mean Square Error
(MSE), Expected sample size [E(n/θ,R)],

Using the concept of T- open set we introduce
and study the concept of Almost T-
continuous multifunctions which is stronger
than the concept of Almost - continuous
multifunctions. Several properties and
characterizations of this new concept are
proved .

Techniques and applications for
information hiding have become
increasingly more sophisticated and
widespread. With high-resolution digital
images as carriers, detecting the presence
of hidden messages has also become
considerably more difficult. It is sometimes
possible, nevertheless, to detect (but not
necessarily decipher) the presence of
embedded messages. The basic approach
taken here works by finding predictable
higher-order statistics of "natural" images
within a multi-scale decomposition, and
then showing that embedded messages
alter these statistics.
The decomposition of images using
basis functions that are localized in spatial
position, orientation, and scale (such as
wavelets) has proven extremely useful in a
range of applications. One reason for this is
that such decompositions exhibit statistical
regularities that can be exploited.
The proposed algorithm consist of
three stages: Image feature extraction (IFE)
stage, training stage, and testing stage. In
IFE the image decomposes to four level
wavelet. Set of statistics (mean, skewness,
and kurtosis for each subband) is collected
from this decomposition. The
second set of statistics collected is
based on the errors in an optimal linear
predictor of coefficient magnitude. In this
predictor, the subband coefficients are
correlated to their spatial, orientation and
scale neighbors.
The steganalysis technique was tested
on samples of images processed with most
commercial steganographic software
products.

ct
The Monte Carlo simulation technique
is one of the common computational tools used
to imitate and follow up complex real life
systems and their development with time.
Variables of a disease problem were defined
and the mathematical model for this problem
was constructed. The numerical solution of this
model was compared with the computational
simulation of Markov Renewal Process of the
type '' Birth and Death ''. We obvious from the
results we obtained the efficiency of the Monte
Carlo simulation technique and through out
extended time periods episodes.

The main aim of this search to study
the composition operator φλ
C induced by
the automorphism φ λ
and discuss the
adjoint of the composition operator, also we
discuss some properties of composition
operator and tried to see the analogue
properties in order to show how the results
are changed by changing the function ψ of
U. In order to make the work accessible to
the reader, we have included some known
results with the details of the proofs for
some cases and proofs for the properties.

In this paper we shall discuss when an
invertible matrix and its inverse are
similar.We shall give theorems for such
case.

In the present paper, a comparison
between classical masks and (odd and even)
masks groups for Mycosis Fungoides disease
Skin image edges detection is performed.
The goal is to extract the information known in
the image because it is vital to understand the
image content as the proposed approach is the
comparative edge by masks classical and a new
set of Groups masks (odd and even ) which
consist of 10 masks. The database consists of
40 images reprints different stage of the
Mycosis Fungoides disease Skin images 10
images for each stage. The experimental results
confirm the effectiveness of the proposed
system. and confirm the effectiveness of the
proposed(odd and even) Groups masks.

The aim of this research is to predict membership in
two mutually exclusive groups of bronchitis cancer
patients, and allocating new patients using
Discriminant Analysis.
Discrimination is a multivariate technique
concerned with separating distinct sets of objects
(or observations) and with allocating new objects
(observation) to previously defined groups. The
results showed 90% , and 98% of dead and alive
patients were classified correctly. Only 2% and
10% of dead and alive patients were misclassified

In the present paper, an important
mathematical transform which is called
Gabor transform be used to develop a
method for image compression. Gabor
transform is a type of wavelet-based
transform. It is embedded in the standard
compression algorithm (JPEG2000) as a
mother wavelet. Based on the obtained
results we believe that Gabor wavelet
transform can provide the optimal resolution
in both the time and frequency domains, as
well as it seems to be the optimal basis to
extract local features such as discontinuities.
These advantages of Gabor wavelet
transform make it an efficient transform for
compression. The proposed compression
system is a new approach that achieves a
high compression ratio with acceptable
visual quality by exploiting the advantages
of Gabor wavelets .The simulation results
show that the proposed model gives a good
compression images that could be
competitive to JPEG2000 using Haar
wavelet.