In this paper, a method is proposed to increase the compression ratio for the color images by
dividing the image into non-overlapping blocks and applying different compression ratio for these
blocks depending on the importance information of the block. In the region that contain important
information the compression ratio is reduced to prevent loss of the information, while in the
smoothness region which has not important information, high compression ratio is used .The
proposed method shows better results when compared with classical methods(wavelet and DCT).

Image pattern classification is considered a significant step for image and video processing.Although various image pattern algorithms have been proposed so far that achieved adequate classification,achieving higher accuracy while reducing the computation time remains challenging to date. A robust imagepattern classification method is essential to obtain the desired accuracy. This method can be accuratelyclassify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism.Moreover, to date, most of the existing studies are focused on evaluating their methods based on specificorthogonal moments, which limits the understanding of their potential application to various DiscreteOrthogonal Moments (DOMs). The

Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOM

In this study, iron was coupled with copper to form a bimetallic compound through a biosynthetic method, which was then used as a catalyst in the Fenton-like processes for removing direct Blue 15 dye (DB15) from aqueous solution. Characterization techniques were applied on the resultant nanoparticles such as SEM, BET, EDAX, FT-IR, XRD, and zeta potential. Specifically, the rounded and shaped as spherical nanoparticles were found for green synthesized iron/copper nanoparticles (G-Fe/Cu NPs) with the size ranging from 32-59 nm, and the surface area was 4.452 m2/g. The effect of different experimental factors was studied in both batch and continuous experiments. These factors were H2O2 concentration, G-Fe/CuNPs amount, pH, initial DB15

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

Objective: To assess knowledge of pregnant women concerning prenatal care who attend primary health care
center in Baghdad city.
Methodology: A descriptive analytic study carried on (100) pregnant women who attend primary health care
centers in Baghdad city (50) of them from Al- Sheik Omer primary health care center \Resafa sector .and 50 from
Belat Al-Shuhadaa/ Al Karch sector, during the period from April to November 2011. The data were collected
through interview and use questionnaire format. Validity and Reliability of the questionnaire were determined
through panel of experts and pilot study, data were analysed through the application of descriptive statistical
analysis and inferential statistical analysis.
R

Background: Breast lump is one of the most common prevalent complaint of patients attending breast clinics.

Objective: To determine if there is any change in the pattern of common breast, diseases presenting as breast lumps between pregnant and non-pregnant women among patients attending Al-Elwiya Breast Clinic.

Methods: This is a cross – sectional study, with convent's patient sampling setting in AL-Elwiya Breast Cancer Early Detection Clinic from 1^st Feb. to 1^st May 2018, we collected data from patients with breast lumps including the age groups, pregnancy status, parity status, previous breast diseases, hormonal drugs, menstrual cycle, breast fe