The estimation of the regular regression model requires several assumptions to be satisfied such as "linearity". One problem occurs by partitioning the regression curve into two (or more) parts and then joining them by threshold point(s). This situation is regarded as a linearity violation of regression. Therefore, the multiphase regression model is received increasing attention as an alternative approach which describes the changing of the behavior of the phenomenon through threshold point estimation. Maximum likelihood estimator "MLE" has been used in both model and threshold point estimations. However, MLE is not resistant against violations such as outliers' existence or in case of the heavy-tailed error distribution. The main goal of t

تكمن مشكلة البحث في أن كليات التربية عامةً وكلية التربية /ابن الهيثم خاصة ما زالت تسير وفق أنماط إدارية تقليدية وأن هناك شكوى تلهج بها الألسن في المجتمع المحيط بأن المخرجات التعليمية لا توازي تطلعات المجتمع وتوجهاته المستقبلية، كذلك الأهمية لتطبيق معايير ضمان جودة تحقق المستوى المطلوب لهذه الكلية أسوة بالمعايير العالمية، إذ هدف البحث في معرفة واقع معايير ضمان الجودة في الكلية والانتقال إلى رؤية مستقبلية ل

The research endeavored to single out the problems, difficulties and obstacles faced by the members of the staff of Baghdad University. The importance of research, then, depends upon the importance of the university lecturers and their vital role in developing the society.  The goals of the research are summarized in knowing the main problems facing those university lectures and relative weight  of these problems in addition to identifying the differences in their opinions to these problems according to the specializations variables (scientific, humanistic), the gender (males, females) and the Scientific grade (professor, assistant professor, lecturer, assistant lecturer.

The research is limited

تشهد بیئة الأعمال المعاصرة جملة من التغیرات والتطورات التكنولوجیة وتزایداً في حدةالمنافسة، وأتساع الفجوة بین العاملین والإدارة ، وقدرة الإدارة على التمییز بین الموظفین، والمشاركة فيإتخاذ القرارات، الامر الذي یلقي على عاتق شركة التأمین إجراء التغییر التنظیمي لتحقیق تطور فيالخدمات التأمینیة التي تقدمها للجمهور من تنوع التغطیات التأمینیة )الوثائق( والسرعة في تسویةالتعویضات وغیرها من الانشطة التي تمارسه

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.

In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob