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EN
The curriculum of physical education and sports through sports activities programmed in the secondary stage seeks to achieve a set of goals of mobility, cognitive and socio-emotional to serve the goals of physical education and sports at this stage. We find a basketball game one of these activities which need a degree of intelligence and knowledge that helps in developing the skill level of the students and selecting the teacher for the appropriate exercises to teach different basic skills. In this context, our study aims to measure the cognitive level of the students of the third secondary level, as well as their level in some basic skills. The study was conducted using a cognitive test in basketball and the selection of some technical tests according to the objectives of the curriculum, then comparing the level according to the sex variables and the level of intelligence and interaction between them and identifying the relationship between these variables. One of the students of the third year secondary school in the city of Mostaganem (Algeria), and after the statistical treatment of the results was reached the results of the most important: 1. The absence of a gender effect and the level of logical and physical intelligence and interaction between them at the cognitive level while being influenced by the level of space intelligence in favor of those with high intelligence. 2. There is a difference in the level of skill of pointing, scrolling and dialogue for the benefit of males, and there is no impact on the level of intelligence, whether logical or physical or space. 3. There is an impact of the interaction between sex and the level of spatial intelligence at the cognitive level, where males and females with high intelligence showed more response. 4. The interaction of sex and the level of intelligence, whether logical, physical or spatial, has no impact on the skills of correction, scrolling and dialogue. 5. There is a direct correlation between IQ and cognitive level as well as basic skills in basketball.
EN
Early identification can significantly improve the prognosis of children with autism spectrum disorder (ASD). Yet existing identification methods are costly, time consuming, and dependent on the manual judgment of specialists. In this study, we present a multimodal framework that fuses data on a child’s eye fixation, facial expression, and cognitive level to automatically identify children with ASD, to improve the identification efficiency and reduce costs. The proposed methodology uses an optimized random forest (RF) algorithm to improve classification accuracy and then applies a hybrid fusion method based on the data source and time synchronization to ensure the reliability of the classification results. The classification accuracy of the framework was 91%, which is higher than that of the RF, support vector machine, and discriminant analysis methods. The results suggest that data on a child’s eye fixation, facial expression, and cognitive level are useful for identifying children with ASD. Because the proposed framework can separate ASD children from typically developing (TD) children, it can facilitate the early identification of ASD and may improve intervention programs for children with ASD.
EN
Various understanding of human wisdom is discussed, from common-sense approaches through cognitive considerations to scientific analysis. The main cognitive levels are considered such as: filtering the available information about the appearing situation, comprehensive assessment of the situation, awareness of the consequences about the existing situation and reaction to the existing situation. The basic set of essential a4ributes like knowledge, skills, and inspirations are also analyzed. Based on these considerations some relations between cognitive levels and wisdom atributes are presented. It leads to a definition of the wisdom quotient which is a representative measure of human wisdom behaviors. Some representative cases of such behavior are specified and discussed as human attitudes. It is also shown how computer science approaches can support calculation of some wisdom indexes and, in consequence, allow understanding human wisdom.
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