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Abstract Kidney stones are one of the most common and most painful medical problems known (1). Nurses assess and monitor patients through diagnosis and treatment and teach patients how to avoid recurrence of stones (2). A descriptive study was conducted on 150 patients diagnosed with recurrent kidney stones, who were attending the out patients consultation urology disease clinics at surgical specialties, Al-Kadhimia, Al-Yarmook, and Al-Karama Teaching Hospital and Extracorporeal shock wave lithotripsy (ESWL) departments for the period from the 1st of Feb. 2002 through to the end of May 2004. The aim of
Objective(s): To determine the quality of life for adults with a chronic obstructive pulmonary disease.
Methodology: A descriptive study was carried out on (80) patients with a chronic obstructive pulmonary disease from
December 2008 through October 2009 with special inclusion criteria (adult paƟents from 18 years and above exclude
the patients who suffer complication related of disease and from psychological problems and other chronic illnesses.
The data were analyzed through the application of descriptive data analysis approach and inferential data approach.
Result: The study indicated that the determination of QoL for COPD depended on the level of effect .The grades
according to R.S are: "high" effect of disease in
Background: The liver is one of the most common organs
injured after blunt abdominal trauma. The control of severe
hemorrhage remains a problem.
Methods: One-hundred thirty-eight patients diagnosed as
liver injury between 09/2003 and 08/2006 had been evaluated
prospectively in Al- Kindy Teaching Hospital.
A distinction was made between hemodynamically stable and
unstable patients. Different modalities of surgical procedures
were done concentrating on perihepatic gauze packing.
Results: (60 out of 138) patients included in the study were
clinically evaluated as hemodynamically stable. The average
abbreviated injury severity score (ISS) was 25. Twenty
patients underwent abdominal surgery. In 12 of them
Because the Coronavirus epidemic spread in Iraq, the COVID-19 epidemic of people quarantined due to infection is our application in this work. The numerical simulation methods used in this research are more suitable than other analytical and numerical methods because they solve random systems. Since the Covid-19 epidemic system has random variables coefficients, these methods are used. Suitable numerical simulation methods have been applied to solve the COVID-19 epidemic model in Iraq. The analytical results of the Variation iteration method (VIM) are executed to compare the results. One numerical method which is the Finite difference method (FD) has been used to solve the Coronavirus model and for comparison purposes. The numerical simulat
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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Background: Sialosis described as a specific consequence of diabetes. In diabetic sialosis, the increased volume of the glands is due to the infiltration of adipose in the parenchyma. The B-scan ultrasonography is a generally accepted tool for determining parotid gland enlargement. Oral health is, to a greater extent, dependent on quality and quantity of saliva, both of which may be altered in diabetics. This study was established to detect the enlargement of parotid gland in diabetic patient and study the changes in physical properties of saliva and its relation with the salivary gland enlargement. Subjects, Materials and Methods: A cross-sectional study with highly specified criteria with ages ranged (20-65) years, male and female subject
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