Background: Obesity represents a clear and
present danger to the health of children and
adolescents. Its prevalence among American
youth has doubled in the past 3 decades, and
there are now more overweight and obese
adults in the United States than adults of
normal weight.
Objectives of the study:
1- Finding whether screen watching among
adolescents has an effect on increasing
prevalence overweight and obesity.
2- The effect of other variables like physical
activity, eating in front of screen, eating under
stress on obesity and overweight among the
subjects sample.
Patients &Methods: During 3 months period a
cross sectional survey was conducted on 4
high schools at Baghdad with total sample of
500 subjects using time table to assess screen
watching hours per week,
Results:Higher percentagesof overweight &
obesity were found among female adolescents
than males, significant associations was
obvious among total sample & male sample
betweenthe level of physical activity &
BMI,while there was no significant
association among female sample for the same
variables , There was obvious significant
association between BMI a time spending in
screen watching, eatingin front of screen, ,
eating under stressin the total & male samples.
Conclusion: Increased levels of physical
activity are associated with a lower BMI and
less time spent on screen watching .in addition
.stress-induced eating may be one factor
contributing to the development of obesity;
furthermore habits like eating in front of
screen could increase BMI of an adolescent.
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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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