Software-Defined Networking (SDN) has evolved network management by detaching the control plane from the data forwarding plane, resulting in unparalleled flexibility and efficiency in network administration. However, the heterogeneity of traffic in SDN presents issues in achieving Quality of Service (QoS) demands and efficiently managing network resources. SDN traffic flows are often divided into elephant flows (EFs) and mice flows (MFs). EFs, which are distinguished by their huge packet sizes and long durations, account for a small amount of total traffic but require disproportionate network resources, thus causing congestion and delays for smaller MFs. MFs, on the other hand, have a short lifetime and are latency-sensitive, but they accou

       The study aimed to reach the best rating for the views and variables in the totals characterized by qualities and characteristics common within each group and distinguish them from aggregates other for the purpose of distinguishing between Iraqi provinces which suffer from deprivation, for the purpose of identifying the status of those provinces in the early allowing interested parties and regulators to intervene to take appropriate corrective action in a timely manner. Style has been used cluster analysis Cluster analysis to reach the best rating to those totals from the provinces that suffer from problems, where the provinces were classified, based on the variables (Edu

the political changeable is important reflection in areas of life and the designers tried to high light it in the cover of Journal promised time Journal a fertile square to show reflection of political changeable, and triad  to  search  the  current  path. The  first  chapter organized according to axes, problem of  research formulated according to question:- what are reflections of political changeable in magazine cover?

    The importance of research depth of specialized approach on level of technical and stylistic as ago al ago as in: the baring of political changeable in design of journal's cover. The research moves in cognitive border from 2013 to 2017 and selection of t

This study aims to assess the accuracy of digital elevation model (DEM) created with utilization of handheld Global Positioning System (GPS) and comparing with Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), version 2. It is known that the quality of the DEM is affected by both of accuracy of elevation at each pixel (absolute accuracy) and accuracy of presented morphology (relative accuracy). The University of Baghdad, Al Jadriya campus was selected as a study area to create and analysis the resulting DEM. Additionally, Geographic Information System (GIS) was used to visualize, analyses and interpolate GPS track points (elevation data) of the study area. In this

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

لمعرفة مدى تأثير تمرينات مهارية وفق تقنية تركيز للتفكير الجاني على الدقة الحركة وتعلم هجمة الإيقاف  بالغطس للطلاب في سلاح الشيش استخدمت الباحثتان المنهج التجريبي على عينة من طلاب المرحلة الثالثة بكلية التربية البدنية وعلوم الرياضة –جامعة ديالى والتي بلغت (30) طالباً موزعين على مجموعتين التجريبية والضابطة وبعد إكمال اجراءات البحث وتطبيق الاختبارات القبلية وتنفيذ التمرينات والاختبار البعدي ومعالجة الب

Early diagnosis and clinical decision-making depend on accurate brain tumor classification using magnetic resonance imaging (MRI). However, traditional deep learning methods usually rely on centralized medical data, which raises privacy concerns and limits the use of distributed clinical data. This research proposes a privacy-preserving federated learning framework for MRI image-based binary brain tumor classification using a decentralized ResNet-18 architecture that enables collaborative training without sharing raw patient data. To reflect realistic clinical conditions, the framework integrates heterogeneous multi-source datasets in different image formats (PNG and JPG) and evaluates performance under both IID and non-IID settings