The present study aimed at examining the factors that affect the choice of A major among a sample of BA fe(male) students at the levels 3-8 in King Abdulaziz University (KAU), in Jeddah, Saudi Arabia. To meet this objective, a descriptive survey method was used together with a questionnaire that consisted of 4 axes to answer the central question: What are the factors affecting the choice of a major at the university? Results have shown that the item that measured the students’ ability to choose the major ranked (First); it was concerned with the effect on the students' choice of his/her major in the university. On the last position and with respect to this effect came the professional tendencies and desires. Results have also shown tha

Uncompleted Personality and it’s relation with Some Variables of the University Students

الهدف من الدراسة هو معرفة أثر استراتيجيات التفكير البصري (VTS) على تحصيل طلاب الكلية في مهارات الاتصال باللغة الإنكليزية. ولتحقيق هدف الدراسة تم استخدام التصميم البعدي التجريبي. وتمثلت العينة بـ (70) من طلاب المرحلة الثانية بقسم اللغة الإنجليزية بكلية التربية. وقد تم اختيارهم عشوائياً وتقسيمهم إلى مجموعتين: المجموعة التجريبية والضابطة (35) طالباً في كل مجموعة. ولتحقيق هدف الدراسة تم بناء اختبار تحصيلي لمهارا

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

 This study investigated three aims for the extent of effectiveness of the two systems in educational development of educators. To achieve this, statistical analysis was performed between the two groups that consisted of (26) participants of the electronic teaching method and (38) participants who underwent teaching by the conventional electronic lecture. The results indicated the effectiveness of the “electronic teaching method” and the “electronic lecture method” for learning of the participants in educational development. Also, it indicated the level of equivalence from the aspect of effectiveness of the two methods and at a confidence level of (0.05). This study reached several conclusions, recommendations, and suggestio

    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

Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of

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

Deep Learning Techniques For Skull Stripping of Brain MR Images

research aim :
- The research aimed to investigate the effect of two treatment
methods in the gaining of fourth grade students in geography
object.
- Research hypothesis
 there are no statistically significant differences at the level of ( 0.05 )
in the average level of achievement in geography between the first
experimental group ( strengthening lessons ) and the second group
( re- teaching )
 no individual differences statically significant at the level of ( 0.05 )
in the average level achievement in geography object of the second
experimental group ( re- teaching ) and the first experimental group
( strengthening lesson )
 the research sample : the researcher selected randomly Baghdad