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Networks Management using the Ubuntu Server system

‫‪إدارة الشبكات باستخدام نظام أبونتو سيرفر

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 Publication date 2016
and research's language is العربية
 Created by shadi yousef




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References used
Y. Video, "Postfix & Dovecot," [Online]. Available: https://www.youtube.com/watch?v=kdio_MMNqUE. [Accessed 13 5 2016].
U. Documentaion, "Samba," [Online]. Available: https://help.ubuntu.com/lts/serverguide/samba-fileserver.html. [Accessed 28 2 2016].
U. Documentaion, "Firewall," [Online]. Available: https://help.ubuntu.com/12.04/serverguide/Firewall.html. [Accessed 2 3 2016].
U. Documentaion, "IPtables," [Online]. Available: https://help.ubuntu.com/community/IptablesHowTo. [Accessed 2 4 2016].
U. Documentaion, "Network File System," [Online]. Available: https://help.ubuntu.com/lts/serverguide/network-file-system.html. [Accessed 16 3 2016].
Website, "HowToForge," [Online]. Available: https://www.howtoforge.com/tutorial/how-to-install-nagios-on-ubuntu-15-04/. [Accessed 2 3 2016].
Ubuntu Server Guide, 2014
Website, "AU.conf DHCP," [Online]. Available: http://askubuntu.com/questions/644191/configure-dhcp-on-ubuntu-12-04. [Accessed 2 3 2016].
WebSite, "Ubuntu D.File Server," [Online]. Available: https://help.ubuntu.com/lts/serverguide/file-servers.html. [Accessed 2 3 2016].
Website, "Server-Word-Email," [Online]. Available: http://www.server- world.info/en/note?os=Ubuntu_14.04&p=mail. [Accessed 3 5 2016].
Liquidweb, "Setup-Email," [Online]. Available: http://www.liquidweb.com/kb/how-to-setup-email-on-thunderbird/. [Accessed 15 5 2016].
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1317 - Ubuntu 2018 كتاب
Ubuntu Server Guide contains information on how to install and configure various server applications on your Ubuntu system to fit your needs.
الشبكات المعرفة برمجيا SDN هي عبارة عن بنية شبكية جديدة توفر التحكم المركزي بكامل الشبكة. يعمل هذا المتحكم كنظام تشغيل يقوم بإرسال التعليمات وتطبيق التغييرات من خلال الواجهات التخاطبية بينه وبين الأجهزة المسؤولة عنها ويدعى بالمتحكم.
This paper introduces a system to recognize labels of time plans, where labels are extracted from time plan. This labels are images, so spatial segmentation is used to extract images of labels only. Size of images of labels are made same using medi an's algorithm for two purposes. The first one is to create database training for used neural networks. The second is to recognizing's processing. Two methods of recognizing are dependent on using neural networks technic: classification using perceptron network and recognizing using back propagation network. Perceptron network is built to take image as input and to give classification index as output for label. Then label is recognize dependent on stored table of ASCII for label. Back propagation network is designed to recognize images for all letters of English alphabet that are used in time plan. Results of research appear efficiency of designed system to recognize labels of time plan from their images for both methods after system had been applied on three time plans.
إن الشبكات بأنواعها المختلفة بما فيها الشبكات المحلية الصغيرة يتوجب عليها تقديم الخدمة الجيدة للمستخدمين وهذا يحتاج إلى وجود نظام إدارة يتولى مهمة إدارة موارد الشبكة وخدماتها. الهدف من المشروع هو إعداد الخدمات بطريقة تسهل إدارة الشبكة على مدير النظ ام وتساعده على تنظيم العمل وتوفير الخدمات بأقل عبء وأقل اخطاء . حيث يساعدنا استخدام مخدم Zentyal في توفير الوقت والجهد لحل بعض الامور الروتينية وعبء الأخطاء الذي كان يظهر عند إعداد الخدمات بالطرق القديمة في نظام LINUX
In recent years, the problem of classifying objects in images has increased by using deep learning as a result of the industrial sector requirements. Despite of many algorithms used in this field, such as Deep Learning Neural Network DNN and Convolut ional Neural Network CNN, the proposed systems to address this problem Lack of comprehensive solution to the difficulties of long training time and floating memory during the training process, low rating classification. Convolutional Neural Networks (CNNs), which are the most used algorithms for this task, were a mathematical pattern for analyzing images data. A new deep-traversal network pattern was proposed to solve the above problems. The aim of the research is to demonstrate the performance of the recognition system using CNNs networks on the available memory and training time by adapting appropriate variables for the bypass network. The database used in this research is CIFAR10, which consists of 60000 colorful images belonging to ten categories, as every 6,000 images are for a class of these items. Where there are 50,000 training images and 10,000 test tubes. When tested on a sample of selected images from the CIFAR10 database, the model achieved a rating classification of 98.87%.

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