Objectives: To assess the knowledge and practice of thalassemic patients about desferal administration and
complications of iron overload.
Methodology: The present study composed of (50) thalssemic patient who are registered in center and was
performed in Ibn Al-Atheer center for congenital anemia for the period from 15/12/2006 to 1/4/2007.
Results: The result of the study showed highly significant difference at (160.05) for knowledge of thalassemic
patients and also appear highly significant difference at (P<O.O5) for practice of thalassemic patients.
Recommendations: The study recommends that there is necessity to increase the knowledge and practice of
thalassemic patient about desferal administration to minimiz

The present study aims at knowing the effect of discussion method for students of fifth grade in preparatory school.

Methodology of the Study:

          In order to achieve the objective of the study, the researcher chooses non-randomly the preparatory school affiliated to the District Chamchamal \ Suliemnaniya. The sample attained 64 students in 32 per group (control and experimental) groups. The researcher used the discussion method which was applied on experimental group. She uses the traditional method on the control group.

The researcher matched the two group in ago, intelligence, marks at the Kurdish Language in the previous year , pretest and posts for the indepe

The present research aims at the following:
- Measure Classroom environment of university Student (research sample)- Measure
Cognitive motive and Reflective Thinking of university Student (research Sample) .
- In order to Know the relationship between the Scores of Classroom environment and
Scores of cognitive motive and Scores of Reflective Thinking of university Student
(research Sample) .
Sample of research consisted of (500) Students for both genders distrivbted upon ten
colleges in Baghdad University , (5) Scientific Colleges , and (5) human Colleges , Sample
was chosen by using radom method  Research instruments were three , Prepared by the researcher ,The first is to measure
classroom environme

Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv