The research falls in to three sections: the first section has two parts; the first one includes a general description of the research: its   problem, aim, limitations, methodology, and the terminological definition. The second part presents historical background of the weekly school speeches in Iraq and their types. Section Two has two parts: the first is about the principles of writing weekly speeches, their elements, and principals, their conditions. As for the second part, it is about introducing four samples of weekly school speeches distributed as two taken from Al-karkh sector and two taken from Al-rusafa'a sector. The third section: analyzing the content of the sample and giving a short historical background abo

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

The process of cognitive representation includes mental activities such as perception, concepts formation and decision making leading to formation of Cognitive representation where the need for Cognition is one of basic humane needs promoting individuals to have more information.
This Study aims to measure the level of Cognitive representation among gifted Schools, the level of need for Cognition among them, recognize statistical Significant differences with Cognitive representation according to gender Variable and recognize the Correlation between Cognitive representation and the need for Cognition among giftel schools . The sample Consists of subsample of mair application one Consisting of( 400) students, noting that the first sampl

The main reason for the emergence of a deepfake (deep learning and fake) term is the evolution in artificial intelligence techniques, especially deep learning. Deep learning algorithms, which auto-solve problems when giving large sets of data, are used to swap faces in digital media to create fake media with a realistic appearance. To increase the accuracy of distinguishing a real video from fake one, a new model has been developed based on deep learning and noise residuals. By using Steganalysis Rich Model (SRM) filters, we can gather a low-level noise map that is used as input to a light Convolution neural network (CNN) to classify a real face from fake one. The results of our work show that the training accuracy of the CNN model

importumt educational institution as (kindergartens) need teachers which qualified ownes modalities in their education for children , as Marzanu method in a way of learning and own methods of crisis management, because the teachers that own those styles of learning ginekindergarten children knowledge and the childrenIeaving based on theMeaing and knowledge and integration of their information, And teachers that earn methods of crisis management provide for the children of the kindergarten security within the educational institution which in turn affect the growth and development of the Child and then abilities, health physical, mental, psychological …etc.., The aims of the current research have identified to recognize: 1- the dimension

تهدف الدراسة إلى التعرف على مستوى الاتفاق في استخدام اللغة الإنجليزية كوسيلة للتعليم وفقًا لاستجابات عينة الدراسة لاستبيان نموذج السياق والمدخلات والعمليات والمنتج وتقييم برنامج اللغة الإنجليزية كوسيلة للتعليم في المدارس الثانوية للطلاب المتميزين من قبل عينة الدراسة وفقًا لبنود استبيان CIPP. شملت الدراسة الكمية الوصفية 109 معلمًا حضروا البرنامج بنشاط في العراق خلال العام الدراسي 2023-2024. لتحقيق أهداف هذه

Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatio-temporal inputs. This article presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm. There are several unique features in the proposed architecture that tightly link with the HTM algorithm. A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed. The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM. The crossbar for the SP avoids dark spots caused by unutil

The increasing demand for continual learning in sequential data processing has led to progressively complex training methodologies and larger recurrent network architectures. Consequently, this has widened the knowledge gap between continual learning with recurrent neural networks (RNNs) and their ability to operate on devices with limited memory and compute. To address this challenge, we investigate the effectiveness of simplifying RNN architectures, particularly gated recurrent unit (GRU), and its impact on both single-task and multitask sequential learning. We propose a new variant of GRU, namely the minion recurrent unit (MiRU). MiRU replaces conventional gating mechanisms with scaling coefficients to regulate dynamic updates of hidden