Objective(s): To Evaluate Diabetes self –management among patients in Baghdad City and to compare
between these patients self-management relative to the type of the disease.
Methodology: A descriptive design was conducted in Baghdad city, started from November 16th 2017 to the
end of May 17 th 2018 in order to evaluate Diabetes self-management. Purposive (non-probability) sample,
which was consisted of (120) patients who were diagnosed with D.M. The sample is comprised of (60) patient
with diabetes type I and (60) patient with diabetes type II. It is consisted of (60) male and (60) female. A
questionnaire is constructed for the purpose of the study. It is composed of (42) items. Reliability and validity of
the questionnaire is determined through pilot study. Data are collected through the use of the questionnaire and
the structured interview as means of data collection. Data are analyzed through the application of descriptive
statistical data analysis approach of frequency, percentage, mean, range and total scores and inferential
statistical data analysis approach of t-test.
Results: The results show that more than a half have a poor level of diabetes self-management (n = 64; 53.3%)
and a lesser proportion who have a good level of such management.
Recommendations: Further study can be conducted on subjects with a large sample size. An education
program can be done to improve patient knowledge about the diabetes self-management. Better attention can be
directed for patients who were students, married and low income ones
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So, this study aimed at testing the system performance at poor s
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In this research, a study is introduced on the effect of several environmental factors on the performance of an already constructed quality inspection system, which was designed using a transfer learning approach based on convolutional neural networks. The system comprised two sets of layers, transferred layers set from an already trained model (DenseNet121) and a custom classification layers set. It was designed to discriminate between damaged and undamaged helical gears according to the configuration of the gear regardless to its dimensions, and the model showed good performance discriminating between the two products at ideal conditions of high-resolution images. So, this study aimed at testing the system performance at poo
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