The aim of this study to identify the effect of using two strategies for active learning ( Jigsaw Strategy & Problems Solving) in learning some balanced beam's skills in artistic gymnastics for women , as well as to identify the best of the three methods (jigsaw strategy , problems solving and the traditional method) in learning some skills balance beam , the research has used the experimental methodology, and the subject included the students of the college of Physical Education and Sports Sciences / University of Baghdad / third grade and by the lot was selected (10) students for each group of groups Search three and The statistical package for social sciences (SPSS) was used means, the standard deviation and the (T.test), the one way a nova and the LSD test. A number of conclusions were reached, the researcher has concluded that using jigsaw strategy, problem solving and the traditional method has a positive effect on learning some balance beam's skills under study. However, his effect varies among the research groups. The experimental group that applied the jigsaw strategy has surpassed the groups , The second was the problem solving strategy and finally the traditional method.
Sports management is a fundamental pillar that supports sports institutions and plays a pivotal role in achieving advanced levels of success in talent development. The Talent Development Project is one of the key strategic initiatives of the Ministry of Youth and Sports. This study compares department heads with effective managerial competence to those with ineffective competence to highlight differences in performance quality. Through this comparison, the urgent need to assess the administrative performance skills of the heads of sports talent departments becomes evident, particularly their ability to lead and manage the Sports Talent Development Project. The objective is to identify strengths and weaknesses, establish a clear framework fo
... Show MoreOne of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p
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Deep Learning Techniques For Skull Stripping of Brain MR Images
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Abstract  
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Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated
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Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet
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General Background: Deep image matting is a fundamental task in computer vision, enabling precise foreground extraction from complex backgrounds, with applications in augmented reality, computer graphics, and video processing. Specific Background: Despite advancements in deep learning-based methods, preserving fine details such as hair and transparency remains a challenge. Knowledge Gap: Existing approaches struggle with accuracy and efficiency, necessitating novel techniques to enhance matting precision. Aims: This study integrates deep learning with fusion techniques to improve alpha matte estimation, proposing a lightweight U-Net model incorporating color-space fusion and preprocessing. Results: Experiments using the AdobeComposition-1k
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The research aims to examine the effect of Hawkins' strategy on students in the fourth grade of primary school in the General Directorate of Education in Baghdad / Karkh 3 for the academic year (2020-2021). The research was limited to the topics of the Arabic language grammar book for the fourth grade of primary school. The researcher developed the research hypothesis, which is: that there is no statistically significant difference at the significance level (0.05) between the average scores of the experimental group students who study using the Hawkins strategy and the average scores of the control group students who study in the traditional method in the achievement test. The researcher set a number of
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