IRAOI JOURNAL OF STATISTICAL SCIENCES

In this paper,the reliability function was estimation by the robust bayes approach using the initial distribution of the parameter ???? with class (ML-II-????contaminated) when the shape parameter β is known and under the quadratic loss function for the duration of gastric ulcer patients since diagnosis and treatment until recovery and hospital discharge or death. The time of patient's admission to diagnosis and treatment was truncated. It was found that the data follow the two-parameter Weibul distribution (????,β) where the prior distribution of the scale parameter was two-parameter Fréchet distribution
(α0=1,????0) according to prior information from previous years about the length
of hospital stay and the opinions doctors and specialists.

This paper tackles with two methods to dimensionality reduction, namely
principal component analysis (PCA ) in the case of linear combinations
and kernel principal component analysis method in the case of nonlinear
combinations to digital image processing and analysis for useful
information .And then compare the two methods and know which methods
are appropriate to imaging dimensionality reduction.
The methods were applied to a group of satellite images of an area in
the province of Basra, which represents the mouth of the Tigris and
Euphrates in the Shatt al-Arab, as well as the water channels
permeating Basra Governorate and the water bodies scattered around these
channels.In this research, it is shown that the fourth image band is best
when using the PCA method the value of it is eigen value was the biggest
,while the KPCA method showed that the third image band was the best,
giving the highest latent value. Comparing the two methods using the mean
error error (MSE) method, the results showed that the main KPCA method
was the best.

Air Quality Modeling gained great
importance
in
atmospheric pollution because of its negative effects on the
environment and human health. In our study, the relationship
between (Particulate Matter PM10) and other nine variables over
three years is studied to applied the multiple linear regression
models. The seasonal influences for seasonally periods lead to
difficult analyzing and forecasting. Therefore, Time-stratified (TS)
approach is used into seasonally. The multiple linear regression

(MLR) model is the most common for studying like this
number of variables. Genetic algorithm (GA) as well as their hybrid
method such as MLR–GA, is proposed to reduce the number of
studied variables. Reducing the number of variables may also lead
to more accurate results. The genetic algorithm has improved the
performance of MLR method separately. GA also improved MLR
performance by using hybrid method MLR-GA.

Air Quality Modeling gained great
importance
in
atmospheric pollution because of its negative effects on the
environment and human health. In our study, the relationship
between (Particulate Matter PM10) and other nine variables over
three years is studied to applied the multiple linear regression
models (MLR). The MLR model is the most common for studying
like this multivariate case. The main problem for this type of data is
the non linear style that has been referred by many researchers
before. The recurrent neural network (RNN) is nonlinear method

As a result of the development of software engineering , especially with
regard to improving the software systems within a short time to enhance the
development process and reduce cost as much as possible . Software
reusability is one of the most important software engineering metrics which
aim to reduce the effort and time in the development process . It is the process
of using pre-existing software components for implementing and updating
new software systems , thereby reducing effort , time and cost in addition to
increasing the quality and productivity of software systems . Many factors
affect the reusability of a software , of these factors , coupling and cohesion
are used in this search for the aforementioned purpose .
Reusability of classes is determined based on coupling and cohesion
results . Cohesion measures the intra-dependability of functions and variables
inside the class , while coupling measures the inter-dependability among
classes in the same package . The proposed expert system also used for
computing coupling , cohesion and reusability status , and produce
suggestions to enhance the reusability status of unreusable classes by offering
intelligence advices which depending on the weakness of the tested codes .
Proposed knowledge Base System (KBOSR) contains (61) rules to provide
and demonstrate why the items are not reusable by displaying code
weaknesses , and to provide advice, guidance and modifications to the code
for making the product reusable and displaying these guidelines and
instructions on user interfaces in a clear and easy-to-understand way, KBOSR
is the first knowledge base system to determine the status of reuse and
improve it .
The knowledge base was tested using 13 classes were distributed in
two packages , first package contains 5 classes , second package contains 8
classes , coupling and cohesion values of classes are computed . Coupling ,
cohesion and reusability status of these classes are determined , then , by
proposed expert system . Finally , suggestions of improvement of unresable
classes are displayed . The results of testing process have proved the strength
and validity of the proposed knowledge base in determining reusability status
of the software components , and the accuracy of the advices that were
suggested , these advices are practically applied on the codes of interest and
the modified codes proved to be highly reusable .

The spiral model is one of the models used in software
development. This model was used in the analysis, design and construction
of the tool (JCOT), where the tool replaces some of the sentences written in
JAVA in other sentences that match the work but at the time of
implementation less. The tool also displays the shortness of the time in a
longitudinal and chromatic strip for ease of user recognition. The spiral
model proved its effectiveness. Each case of optimization was treated as a
link and then the other state was transferred to the end of the tool. This
enabled the researcher to easily add new cases of the tool (JCOT).

It is no secret that the software industry in the present and future time is
the leading industry and the main element in any new technology and is
involved in most modern industries, from the smallest ones such as mobile
phones and smart watches to the largest ones such as airplanes, space stations
and nuclear reactors, and the relation of this software to preserving human life
or saving in sometimes It was important to make sure that it was quality and
error-free. For this reason was must be to develop metrics to measure this
software. This research will provide a comprehensive review of most of the
standards used in software quality measurement and emphasis on software

The Symmetric Block cipher is a considerable encryption algorithm
because of its straightforwardness, rapidity and strength and this
cryptographic algorithm is employed in carrying out the encryption and
decryption for most current security applications. The confusion properties
are attained using the substitution-Box (S-Box). Substitution and
permutation functions are normally used in block ciphers to make them
much firmer and more effectual ciphers. The Security of S-Box is checked
using S-Box test criteria and the randomness test. The objective of this
paper is to give the researchers a specific knowledge (standards) for testing
the ciphers’ S-Boxes. This paper includes survey or guide for the S-box
test criteria.

Abstract
The wind speed forecasting is important to observe the wind behaviour
and control the harms caused by extreme speeds. A linear ARIMA model is
unable to identify the nonlinear pattern of wind speed data. ARIMA
modelling process causes the stochastic uncertainty as a second reason of
inaccurate forecasting results. In this study, a review of an ARIMAartificial
neural
network
(ANN)
and
ARIMA-Kalman
filter
(KF)
methods

is

presented. ANN and KF based on ARIMA are used to improve wind
speed forecasting by handling the nonlinearity and the uncertainty
respectively. This hybrid method will improve the accuracy of wind speed
forecasting. Usually, the forecasting results of the hybrid methods will be
in better forecasting than other compared methods. In conclusion, the
proposed hybrid methods especially the hybrid ANN-KF based on ARIMA
method can be used to forecast wind speed data with nonlinearity and
uncertainty characteristics more accurately.