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The Effectiveness of Using Generative Learning Model in Learning Kinetic Series on Rings and Horizontal Bar In Artistic Gymnastics for men

The aim of this study was to identify the effectiveness of using generative learning model in learning kinetic series on rings and horizontal bar in artistic gymnastics for men ,Also, the two groups were better in learning the two series of movements on the rings and horizontal bar . The experimental method was used to design two parallel groups with pretested and posttest .The sample included third graders at the College of Physical Education and Sports Sciences - University of Baghdad ,The third class (d) was chosen to represent the control group that applied the curriculum in the college, with (12) students per group. After conducting the tribal tests, the main experiment was carried out for (8) weeks at the rate of two units per week divided into (4) weeks to learn the kinetic chain on the rings and (4) weeks learning the kinetic chain on the device of mind and after the completion of the experiment was carried out remote tests, used the statistical package of social sciences (spss) Results The research included (T. test) of the corresponding samples and (T. test) for asymmetric samples. A number of conclusions were reached, the most important of which were the following: - The generative learning model of the applied learning applied to the experimental group and the method applied to the control group had an effect Was effective in learning the kinetic chain under study, but at varying rates was in favor of the generative learning model.

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Publication Date
Wed Jun 26 2019
Journal Name
Iraqi Journal Of Science
Modeling and Forecasting Periodic Time Series data with Fourier Autoregressive Model

Most frequently used models for modeling and forecasting periodic climatic time series do not have the capability of handling periodic variability that characterizes it. In this paper, the Fourier Autoregressive model with abilities to analyze periodic variability is implemented. From the results, FAR(1), FAR(2) and FAR(2) models were chosen based on Periodic Autocorrelation function (PeACF) and Periodic Partial Autocorrelation function (PePACF). The coefficients of the tentative model were estimated using a Discrete Fourier transform estimation method. FAR(1) models were chosen as the optimal model based on the smallest values of Periodic Akaike (PAIC) and Bayesian Information criteria (PBIC). The residual of the fitted models was diagn

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Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review

Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise

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Publication Date
Sat Apr 15 2023
Journal Name
Journal Of Robotics
A New Proposed Hybrid Learning Approach with Features for Extraction of Image Classification

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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Publication Date
Mon Mar 15 2021
Journal Name
Al-academy
Aesthetics of the Artistic Construction in Educational TV Programs " progrm Hello Hi! A Model": محمد عبد الرضا حسين

  The TV has the ability to combine sound and picture, which makes it of a direct impact on the recipient as it is a rich communicative tool with various artistic forms that display through this small screen programs and films, so that the TV has become superior over many other means of communication.
This research is concerned with the technical and artistic ability in the production of programs owned by the TV as a means of attraction of educated children aged between 5-12 years old, which makes it an educational supplement for the school that helps them in providing knowledge, acquiring skills and being informed about the different sciences through educational programs. It is known that the child at this stage enjoys when wa

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Publication Date
Thu Mar 30 2017
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Isolation of Some Microorganisms from Bar Soaps and Liquid Soaps in Hospital Environments

This study was designed to determine the colonization of the in-use hand washing soaps in hospital settings. It is a comparative cross-sectional research in a surgical specialties and Baghdad teaching hospital in Baghdad, Iraq. Swabs from surfaces of bar soaps and from liquid soaps via their applicator tips; at the sinks of toilets of hospital staff and working rooms of the wards were taken in January 2008. Conventional microbiologic methods were used for culture of the swabs and identification of the isolates. Colonization was detected 60% and 15.9% in bars and liquid forms respectively. And this lead to the conclusion that bar soaps could be colonized with microorganisms excessively. Liquid hand washing soaps are more appropriate in ho

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Publication Date
Mon Apr 26 2021
Journal Name
Journal Of Electrical Engineering & Technology
ANFIS Based Reinforcement Learning Strategy for Control A Nonlinear Coupled Tanks System

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Publication Date
Wed Jun 01 2022
Journal Name
Baghdad Science Journal
Constructing a Software Tool for Detecting Face Mask-wearing by Machine Learning

       In the pandemic era of COVID19, software engineering and artificial intelligence tools played a major role in monitoring, managing, and predicting the spread of the virus. According to reports released by the World Health Organization, all attempts to prevent any form of infection are highly recommended among people. One side of avoiding infection is requiring people to wear face masks. The problem is that some people do not incline to wear a face mask, and guiding them manually by police is not easy especially in a large or public area to avoid this infection. The purpose of this paper is to construct a software tool called Face Mask Detection (FMD) to detect any face that does not wear a mask in a specific

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Publication Date
Mon Jul 01 2019
Journal Name
Journal Of Educational And Psychological Researches
Test Wisdom of Postgraduates at the University of Samarra and Its Relationship to Learning and Psychological Tension: Test Wisdom of Postgraduates at the University of Samarra and Its Relationship to Learning and Psychological Tension

Abstract

The aim of the present research is to identify the test wisdom and the preoccupation with learning and psychological tension among postgraduate students at the University of Samarra according to the variables of the department,  gender,  age,  and employee or non-employee, and revealing the relationship between the test wisdom and the preoccupation with learning and psychological tension. The research sample consisted of (75) students randomly selected from postgraduate students at the college of Education. The researcher applies test –wisdom of (Mellman & Ebel) and measurement of preoccupation with learning prepared by (Al-zaabi 2013) also, the researcher used the scale of t

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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Science
Machine Learning Prediction of Brain Stroke at an Early Stage

     The healthcare sector has traditionally been an early adopter of technological progress, gaining significant advantages, particularly in machine learning applications such as disease prediction. One of the most important diseases is stroke. Early detection of a brain stroke is exceptionally critical to saving human lives. A brain stroke is a condition that happens when the blood flow to the brain is disturbed or reduced, leading brain cells to die and resulting in impairment or death. Furthermore, the World Health Organization (WHO) classifies brain stroke as the world's second-deadliest disease. Brain stroke is still an essential factor in the healthcare sector. Controlling the risk of a brain stroke is important for the surviv

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Publication Date
Sun Mar 19 2023
Journal Name
Journal Of Educational And Psychological Researches
E-Learning and Its Relationship with Academic Passion among Middle School Students

Abstract

The current research aims to identify the level of E-learning among middle school students, the level of academic passion among middle school students, and the correlation between e-learning and academic passion among middle school students. In order to achieve the objectives of the research, the researcher developed two questionnaires to measure the variables of the study (e-learning and study passion) among students, these two tools were applied to the research sample, which was (380) male and female students in the first and second intermediate classes. The research concluded that there is a relationship between e-learning and academic passion among students.

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