Objective: Evaluation of women's knowledge about risk factors and early detection of breast cancer at
Ibn Rushd college of education in Baghdad University.
Methodology: The study sample included (184) women in the Ibn Rushd College / University of
Baghdad, whose age ranged between (17-58) years. Data were collected through a structured
questionnaire prepared by the National Cancer Research Center which were answered during a scientific
symposium about breast cancer. The score was calculated by correcting the results of the answer, giving
one score for each correct answer and then estimating the level of knowledge and inputting all data in a
statistical program.
Results: The results showed limited level of women's knowledge about risk factors. As good, medium
and weak answer ratios of (11%).(21%) and (69%) each, respectively. . The study revealed no significant
relationship between the level of knowledge with age, occupation and married status (p < 0.05)). there
was more than half of a correct answer to some risk factors such as, increase the probability of disease
with age (53%), obesity in postmenopausal (53%) or breast self-examine once a month after the
menstrual cycle (69 %,), and more than 60% consent increasing physical activity and good nutrition and
maintaining a healthy weight and stay away from abuse hormones, alcohol be able to prevent of breast
cancer
Recommendations: Promoting public education and awareness campaigns on the risk factors for breast
cancer among women in university is mandatory to control the disease
The research discusses the issue of attribution to the verb, because the Arab scholars are unanimous in preventing attribution of the verb, because it is always informed of it, and does not inform about it, but this consensus violates the linguistic use. The research discusses this matter.
Vision loss happens due to diabetic retinopathy (DR) in severe stages. Thus, an automatic detection method applied to diagnose DR in an earlier phase may help medical doctors to make better decisions. DR is considered one of the main risks, leading to blindness. Computer-Aided Diagnosis systems play an essential role in detecting features in fundus images. Fundus images may include blood vessels, exudates, micro-aneurysm, hemorrhages, and neovascularization. In this paper, our model combines automatic detection for the diabetic retinopathy classification with localization methods depending on weakly-supervised learning. The model has four stages; in stage one, various preprocessing techniques are app
Wind energy is one of the most common and natural resources that play a huge role in energy sector, and due to the increasing demand to improve the efficiency of wind turbines and the development of the energy field, improvements have been made to design a suitable wind turbine and obtain the most energy efficiency possible from wind. In this paper, a horizontal wind turbine blade operating under low wind speed was designed using the (BEM) theory, where the design of the turbine rotor blade is a difficult task due to the calculations involved in the design process. To understand the behavior of the turbine blade, the QBlade program was used to design and simulate the turbine rotor blade during working conditions. The design variables suc
... Show MoreThe aim of the study was to identify the nutritional awareness of middle school students and its relation to some variables of the sample of the research according to the gender variable (male and female), the variable type of family and the variable achievement of the parents.
The descriptive approach was adopted and the sample of the study consisted of (795) male and female students who were selected by the random stratified method. The research tool was prepared based on the literature and previous studies. The food awareness measure in its final form was (25) after the data collection was processed using the appropriate statistical met
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... Show MoreTraffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c
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