Computational Thinking (CT) is very useful in the process of solving everyday problems for undergraduates. In terms of content, computational thinking involves solving problems, studying data patterns, deconstructing problems using algorithms and procedures, doing simulations, computer modeling, and reasoning about abstract things. However, there is a lack of studies dealing with it and its skills that can be developed and utilized in the field of information and technology used in learning and teaching. The descriptive research method was used, and a test research tool was prepared to measure the level of (CT) consisting of (24) items of the type of multiple-choice to measure the level of "CT". The research study group consists of (100) third-year students studying at the University of Baghdad in Computer Science within the scope of (2020-2021). The results are detailed.
This research aims to examine the relationship between learning organization and behavior of work teams. The variable of the learning organization took four dimensions depending on the study (sudhartna & Li, 2004): Common cultural values , communication, knowledge transfer and the characteristics of workers. The behavior of teams was identified on the basis of realizing of the respondents of their organization to work as a team where the research relied concepts applied in the study (Hakim , 2005) , and chose to research the case of a service organization for the study and relied on four dimensions of coordination , cooperation , sharing of information , the performance of the team, and was a curriculum approach and des
... Show MoreFace Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d
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