IRAOI JOURNAL OF STATISTICAL SCIENCES

This paper deals with a new proposed method to measure the activity of
internet sites linked to a certain network. This method is then applied on the
Web sites associated with the University of Mosul. The proposed method
depends mainly on the principal components technique and from which is
extracted the most effect factors on the activity of Web sites. It transforms the
principal components to a classification equation taking into account the
importance of each influential principal component. It has been used to classify
websites of colleges associated with the University of Mosul, according to the
proposed method, and it gives encouraging results.

This research includes the application of some statistical techniques for
studying the time series of the average monthly rainfall as an output series with
two of the variables which affect on to, which are the series of the average
monthly relative temperature and humidity as an input which is measured at the
meteorological station of Ninavah. The techniques used are the modeling by
an(ARIMA) model as well as the dynamic regression model. So that the perfect
dynamic regression model selected was suitable for determining the future
forecasting values

In this paper, a new nonlinear conjugate gradient methods
based on the modified secant condition is derived which are
given by Li and Fukushima (Li and Fukushima, 2001). These
methods showed global convergent under some assumptions.
Numerical results indicate the efficiency of these methods to
solve the given test problems.

Regression splines is one of the methods that are used to estimate the
regression curve non parametrically. One of the most important elements that
contribute to the application of the method is to determine the degree of the
spline function and the number of knots and their locations Choosing the
number of knots and their locations is the main problem in estimating the non
parametric regression using regression splines and, when carefully selected, the
amount of smoothing in the fitted curve will be in optimal conditions. The
research is to shed light on two methods of knots locations; the first method
includes place of the knots which represents the data quintile and use one of
models selection criteria such as GCV ( Generalized Cross Validation)
criterion to select the number of these knots. The other method depends on
placing the knots on equal spaces and the use of GCV criterion to select the
final locations of knots and its number , A comparison is made between the two
methods using one of the errors criteria which is MAAE ( Mean Average
Absolute Error),depending on the experimental data using the simulation
method . Through simulation experiments the method of place knots in equal
spaces , is better than the method of placing knots in the form of quintile of data

In this paper an image is hidden in another image by applying logical
XOR operation to the two least significant bit of each byte of the cover and
two bits of the secret message to produce the stego-cover image which is
used as an input with the cover to Radial Basis Function Network (RBFN) or
General Regression Neural Network (GRNN) to produce the weights.
Cover is delivered once to the recipient who can use it for unlimited
number of messages. The weights are delivered to the recipient for each
hidden message as a key. The recipient uses the cover with the weights to
unhide the message, so that this method includes two levels of security. The
first one is hiding the message in the cover to produce stego-cover image.
The second one is ciphering the embedded image using RBF Neural Network
or GRNN. The stego-cover is considered as a target and the input to the
neural network is the cover image. Then the weights, which represent the
encrypted information are reconstructed by training the neural network. The
recipient can use RBF Network or GRNN to unhide the message by having
the stego-cover image then the message.
In this paper the performance of RBF Neural Network and GRNN are
compared. Noted that the GRNN is better than RBF for this work. Matlab
R2008a was used in this paper.

This paper deals with the two-stage hybrid flow shop problem , in which the first stage
consists of three machines , the second stage consists of two machines . The aim is to find
out the optimal scheduling for n jobs when processing in this environment when the
makespan is minimum. Therefore we propose a particle swarm algorithm which consists of a
new procedure to calculate the makespan and a new stopping criteria .Also, we added
improvement to the proposed algorithm , by using one of the components of the genetic
algorithm (crossover operation) in order to obtain initial swarm particles instead of random
obtaining. After applying the two algorithms on several problems which were generated
randomly by uniform distribution , the results showed that the improved proposed particle
swarm algorithm was the best in finding out the optimal scheduling for jobs and in cpu time
to reach the solution

In this paper we determine the optimal Bayesian sample size to estimate
location parameter of normal distribution when the shape parameter is unknown
under squared loss function and linear exponential loss function (LIENX) .In
application we explain the efficient estimator under two loss functions and
obtain the relationship between the sampling cost per unit and the sample size
which we determine using Matlab language.

This research deals with the most important methods of estimating
parameters of the Geometric Process(GP). We use the least squares and the
modified moment method, as nonparametric methods, and the maximum
likelihood method as a parametric method with the lognormal distribution as
the probability distribution for the first stop. To compare these methods we use
a criteria proposed in this research, as well as comparing(GP) with the renewal
process(RP). As a part of applied research we test whether the data which
represent the occurring stops in the oven of the General Company of Northern Cement follow the(GP)or not, as
well as estimating the parameters of the (GP) of stops and comparing them.

The research deal with the Į-series stochastic process. It’s proposed by the
researchers [Braun et a., 2005] as considered a very useful complement to the
increasing geometric stochastic process. The parameters of Į-series process are
estimated using some nonparametric methods, as well as Mann test for trend
analysis monotonous stochastic process, and technical graphic that tests the ownership of the process to the Į-series process. The research includes an
application addresses the stops of Cement production factories in Iraq. The
statistical analysis is performed for the factory stops as Į-series process, and tests
the appropriateness of the data for the process. The study also estimates the
expected number of factory stops cases, and compares between the suggested
estimation methods.

The idea of this research is based on adopting Support Vector Machine
for Regression (SVR) in estimating and predicting regression models and
comparing it with one of the multi layer neural network that has back
propagation((MLP). Therefore ,this will lead to demonstrate the advantages
and possibility to use it in scientific application by illustrating how it is
accurate in comparison with the ((MLP) by using Mean Square Errors(MSe)
.
The time sequence data are that used for month flow for the water from
Tigris that incomes in to Mosul city for the period between (1950-1995) to
accomplish the comparison process by using the employment of two
computers softwares.

In this paper GRNN is used to hide an image into a cover file, Audio
and video files are used as cover files in this work. The hiding was applied
using one of concealment algorithm that is (Least Significant Bit (LSB)) by
applying (XOR) operation between the last (2bits) of each byte of the image
(cover) and 2bits of the message to produce the (stego_cover), which feed with the cover to General Regression Neural
Network (GRNN) to produce the weights.
Cover is delivered once to the recipient who can use it for unlimited
number of messages. The weights are delivered to the recipient for each
hidden message as a key. The recipient uses the cover with the weights to
unhide the message. So, this method includes two levels of security. The first
one is hiding the message in the cover to produce embedded cover (stegocover). The second one is ciphering the embedded cover using GRNN Neural
Network. This Network is considered as a target and the input to the Neural
Network is the cover. Then the weights, which represent the encrypted
information, are reconstructed. The recipient can use GRNN Network to
unhide the message by having the embedded cover (stego-cover) then the
message. Matlab R2008a was used in this paper.