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EXTRAPOLATION OF THE MACHINE AND ITS EFFICIENCY IN DEVELOPING THE SKILL PERFORMANCE AND ACCURACY OF DRIBBLING IN YOUTH FOOTBALL
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Relying on modern work strategies, such as adopting scientific inductions, consolidates the information in the learner’s memory, develops the skill work of the football player, and raises the efficiency of their motor abilities. From this standpoint, the researcher, who is a teacher at the University of Baghdad, College of Physical Education and Sports Sciences, and follows most of the sports club teams in youth football, believes that there must be From extrapolations through the machine and employing it in the field to serve the skill aspect and benefit from scientific technology in development and making it a useful tool to serve the sports field in football, as the goal of the research was the efficiency of machine extrapolation in developing the dialogue and its accuracy in football for juniors, and the researcher assumed the existence of a statistically significant correlation between the extrapolation relationship The machine and its efficiency in developing the skill and accuracy of dribbling in football for juniors. The research sample was players from the Husseiniya Youth and Sports Forum, the Algeria Youth and Sports Forum, and the Challenge Youth and Sports Forum. The approach was experimental in application, adopting statistical methods of percentages, the Pearson correlation coefficient, and the arithmetic mean. And the standard deviation to arrive at the results. The results were presented and discussed, and the most important conclusions were reached. There is a significant correlation between machine extrapolation and its efficiency in developing dribbling and accuracy in youth football, and it is recommended to adopt machine extrapolation and its ability and efficiency in developing the technical skills of dribbling in youth football, which enhances accuracy in Perform that skill.

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Publication Date
Fri Nov 21 2025
Journal Name
Journal Of Advances In Information Technology
Towards Accurate SDG Research Categorization: A Hybrid Deep Learning Approach Using Scopus Metadata
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The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

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