Abstract
The research aims to build a training program to develop some executive functions for kindergarten children. To achieve this goal, the two researchers built the program according to the following steps:
1. Determining the general objective of the program.
2. Determining the behavioral objectives of the program.
3. Determining the included content in the program.
4. Implementing the content of the activities of the program.
5. Evaluating the Program.
The program included (12) training activities, the training activities included several items: the title of the activity, the time of implementation of the activity, the general objective of the activity, the procedural behavioral objective, the means and tools u

Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration

The aim of the current research is to verify the effect of the cognitive modeling strategy on the achievement of the chemistry course for the students of the first intermediate grade. To achieve the objective of the research, the null hypothesis was formulated via cognitive modeling strategy. The results showed that the experimental group's students performed better than the students in the control group. In the light of the results, the researchers concluded: The impact of the cognitive modeling strategy in the achievement of students of first intermediate grade in chemistry.

Self-driving automobiles are prominent in science and technology, which affect social and economic development. Deep learning (DL) is the most common area of study in artificial intelligence (AI). In recent years, deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. Different studies investigated a variety of significant technologies for autonomous vehicles, including car navigation systems, path planning, environmental perception, as well as car control. End-to-end learning control directly converts sensory data into control commands in autonomous driving. This research aims to identify the most accurate pre-trained Deep Neural Network (DNN) for predicting the steerin

Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the s

         This study attempts to determine the necessary teaching competencies for teachers and the level of their importance in the development of their performance professionally and scientifically, and the progress towards a better future to achieve an effective level. Accordingly, the research community consists of the morning-preparatory schools for boys at the General Directorate of Education of Baghdad \ Rusafa². The study sample included (68) teachers constituted 88.31% of the total community of teachers. As for the research tools, the researcher adopted the descriptive method by using the observation method in which the researcher prepared a list of teaching. For verifying the re

    The current research discusses "The Relationship critical factors for knowledge transfer in strategic success opportunities", the attention have been increased on knowledge transfer and strategic success subjects because on being one of the important and contemporary issues, which have a significant impact on the existence of organizations and its future. The research aims to identify the critical factors for knowledge transfer in private high education environment which enables (the college community surveyed) to achieve strategic success, also the research sought to answer questions related to research problem by testing a number of major and minor hypothes in correlation, in order to test the hypotheses I us

In networking communication systems like vehicular ad hoc networks, the high vehicular mobility leads to rapid shifts in vehicle densities, incoherence in inter-vehicle communications, and challenges for routing algorithms. It is necessary that the routing algorithm avoids transmitting the pockets via segments where the network density is low and the scale of network disconnections is high as this could lead to packet loss, interruptions and increased communication overhead in route recovery. Hence, attention needs to be paid to both segment status and traffic. The aim of this paper is to present an intersection-based segment aware algorithm for geographic routing in vehicular ad hoc networks. This algorithm makes available the best route f

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an