The study aims to investigate the effectiveness of cognitive-behavioral counseling in reducing symptoms of social anxiety and improving social skills among a sample of intermediate school students in the city of Souq Al-Shuyukh at Dhi Qar Governorate. The sample consisted of (40) female students, their ages ranged (14-15). They were selected based on their high scores on the social anxiety scale. The sample was divided into two groups: an experimental group, and a control group, equal in number (20) students in each group. The researcher used the social anxiety scale and the social skills scale. In addition, he used the cognitive-behavioral counseling program, consisting of (11) counseling sessions, with a rate of (45) minutes per sessio

Abstract
Objectives: this study aims to: (1). Assess self-esteem level and academic achievement for students of nursing colleges in southern Iraq. (2). Determine the relationship between levels of self-esteem and academic achievement of the student in the first semester. (3). Identify differences of self-esteem with gender and different age groups.
Methodology: a sample of (426 students) was purposively selected then collected by using a questionnaire which consisted of: I- Sociodemographic characteristics for assessing some important aspects of students, II- Rosenberg's Self-Esteem Scale (RSES) III- Iraq Grading Scale for assessing student achievement. Finally statistical analysis (SPSS) for data processing.
Results: study resu

Abstract

The current research aims to develop a guidance program suitable for high school students and apply it to them in order to ensure the reduction of addiction to the use of different means of communication. The researchers used the scale of addiction to the means of communication (SAS) to measure the level of addiction as well as to identify the impact of the proposed guidance program in reducing the degree of addiction to communication. It was applied to a sample of (20) female students divided equally into two groups: an experimental group of (10) female students and a control group of (10) female students from the secondary level in a school under the department of education in the education of the alma

Whereas conventional methods of teaching history do not meet the sometimes purpose has suggested some educators strategies modern help to improve the level of students in the education process through activating prior knowledge and employed in positions of current learning, and focus attention on the points and the outstanding elements of the content and practice techniques Calendar criticof ideas and meanings and mental and linguistic activities used to verify the extent of reaching understanding.                                       &n

Deep Learning Techniques For Skull Stripping of Brain MR Images

    In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p