مدة تولي رئاسة السلطة في الفكر السياسي الاسلامي المعاصر

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca

There is various human biometrics used nowadays, one of the most important of these biometrics is the face. Many techniques have been suggested for face recognition, but they still face a variety of challenges for recognizing faces in images captured in the uncontrolled environment, and for real-life applications. Some of these challenges are pose variation, occlusion, facial expression, illumination, bad lighting, and image quality. New techniques are updating continuously. In this paper, the singular value decomposition is used to extract the features matrix for face recognition and classification. The input color image is converted into a grayscale image and then transformed into a local ternary pattern before splitting the image into

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

بناء على ما تقدم يتضح ان القطاع العام والقطاع الخاص ليسا موقفا ايدولوجيا انتقائيا، وانما هو انبثاق من العمود الفقري للصيرورة الرأسمالية القائمة على تقديس الملكية الخاصة، وان الادوار التي يمكن ان يلعباها ليسا منعزلين عن الحركة العامة للاقتصاد في كليته، وليس هما ابنا السياسات الاقتصادية، والبناء الفوقي بقدر ماهما نتاج الحركة الموضوعية وصيرورة التطور الاجتماعي معبرا عنها في تطور علاقات الانتاج وقوى الانتا

This paper discuses the mindset contribution of the organization Science in organizational effectiveness (OE), and the most valuable thoughts they introduced in this subject, starting from the pre- classical era, through classical era to present time. This paper has identified the main contribution in OE. The concept of OE didn’t arise until what had been introduced by Chester Barnard, although Max Weber mentioned this concept in his bureaucratic theory, but he didn’t explain it as Barnard does. After that no clear focus was pointed to this subject until Peter Drucker did so, which considered as the main participant in OE after Barnard. After that many researchers be interested in OE and they start studying this subject like

The removal of direct blue 71 dye from a prepared wastewater was studied employing batch electrocoagulation (EC) cell. The electrodes of aluminum were used. The influence of process variables which include initial pH (2.0-12.0), wastewater conductivity (0.8 -12.57) mS/cm , initial dye concentration (30 -210) mg/L, electrolysis time  (3-12) min, current density (10-50) mA/cm2   were studied in order to maximize the color removal from wastewater. Experimental results showed that the color removal yield increases with increasing pH until pH 6.0 after that it decreased with increasing pH. The color removal increased with increasing current density, wastewater conductivity, electrolysis time, and decreased with increasing the concen

This work deals with the effect of adding aluminum nanoparticles on the mechanical properties, micro-hardness and porosity of memory-shape alloys (Cu-Al-Ni). These alloys have wide applications in various industrial fields such as (high damping compounds and self-lubricating applications). The samples are manufactured using the powder metallurgy method, which involved pressing in only one direction and sintered in a furnace surrounded by an inert gas. Four percentages (0%, 5%, 10%, and 15%) of aluminum nanoparticles were fabricated, which depended on the weight of aluminum powder (13%) in the sample under study. To find out which phase is responsible for the reliability of the formation of this type of alloy and its porosity, X-ray diffr