This research sheds light on the morphological construction of kitchen terms in Hebrew language, especially methods of derivation, by offering an extensive review of the most important methods of morphological derivation. The field of kitchen terms is a fertile and rich field, where we see daily production of new kitchen tools which require suitable names with specific meanings. Morphologically, the research interested in methods of morphological derivation. So as to know and identify the common and effective methods to derive the kitchen terms in Hebrew language, in addition to put a special glossary of these terms. The research found that most of the kitchen terms were derived according to the methods of derivation prevailing in Hebrew la

Abstract The relative pronoun in Hebrew language is an important pronoun use anciently and recently, it developed and it's usage and meanings differed so, it was not confined to the particle "אֲשֶׁר" as a relative pronoun, but beside it appeared other pronouns giving the relative meaning. Hence, the topic of this research was on this basis through studying the relative pronouns in old and modern Hebrew, the way of using them and their connection with preposition particles, as well as studying the relative clause.

Background: Nasopharyngeal carcinoma (NPC) is one of the most challenging tumors because of their relative inaccessibility and that their spread can occur without significant symptoms with few signs, but Radiotherapy (RT) has a role in treatment of it.
Objectives: To show that RT is still the modality of choice in the treatment of NPC, to study modes of presentations, commonest histopathological types and their percentages, to show differences in the sensitivities of these types to RT and to find out a 5 year survival rate(5YSR) and its relation with lymph node involvement.
Methods: This is a retrospective study of 44 patients with NPC who were treated with routine RT from 1988-2007 at the institute of radiology and nuclear medicin

Deep Learning Techniques For Skull Stripping of Brain MR Images

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

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

Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.