يتطلب تحقيق تمايز الوحدة الاقتصادية في ظل استعمال تقنيات الأعمال الحديثة وازدياد المنافسة وعالمية الأعمال ضرورة الاهتمام بمستوى نوعية المنتجات وما تتطلبه هذه النوعية من كلف والتي تسمى بكلف النوعية، إذ ان العديد من الشركات العالمية قد قامت بدراسة وتحليل هذه الكلف ووضع برامج خاصة بها بهدف تخفيضها إلى أدنى حدٍ ممكن وبما يكفل تحقيق العديد من المنافع والتوفيرات في هذه الكلف وبما يرشد عملية اتخاذ القرارات

يعد موضوع البرنامج النووي الايراني من الموضوعات التي تحظى بأهمية كبيرة في مجال الدراسات الدولية بشكل عام والدراسات الاستراتيجية بشكل خاص، وذلك لكونه لا يتعلق بمستقبل الجمهورية الاسلامية الايرانية فحسب بل بمستقبل منطقة الشرق الاوسط ولاسيما منطقة الخليج العربي، وهو ذو صله وثيقة بمستقبل انتشار الاسلحة النووية بين دول المنطقة والذي قد يؤدي انتشارها الى حروب اقليمية وقد يوظف فيها هذا النوع من الاسلحة النووية،

When sites of new communication occurs which represents the merit of the development of communication technology which is characterized by the services of ( facebook-twiter-corapora-youtube-mass space-friendster-flicker-willnecked in addition to the direct services for viber-whatsup-telgram-and chat on) play important role in changing the infrastructure of Arabic societies which are consideredas closed and not changeable societies during near period and the significance of this study comes from the importounce of this subject which is considered as anew trend of the age on the field of media and public response and acceptance inspite of what is known about Arabic society-it doesn’t accept change-this occurance is associated with terms

This research reviews the aesthetic variables that were founded according to (theatrical rehearsal) as one of the most important pillars on which the theatrical process is based, because of its necessity in developing theatrical art on several levels that helped the theatrical director in organizing his work, and this became clear through the research chapters represented in the first chapter (methodological framework) and the second chapter, which consisted of the first topic (the duality of watching / rehearsal) and the second topic (the applications of theatrical rehearsal in theatrical experiences), all the way to the third chapter (research procedures), which included the analysis of theatrical rehearsals (sharing on life), and the

Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise

 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct