كتابة النص: الأستاذ الدكتور يوسف أبو العدوس - جامعة جرش قراءة النص: الدكتور أحمد أبو دلو - جامعة اليرموك مونتاج وإخراج : الدكتور محمد أبوشقير، حمزة الناطور، علي ميّاس تصوير : الأستاذ أحمد الصمادي الإشراف العام: الأستاذ الدكتور يوسف أبو العدوس
فيديو بمناسبة الإسراء والمعراج - إحتفال كلية الشريعة بجامعة جرش 2019 - 1440
فيديو بمناسبة ذكرى المولد النبوي الشريف- مونتاج وإخراج الدكتور محمد أبوشقير- كلية تكنولوجيا المعلومات
التميز في مجالات التعليم والبحث العلمي، وخدمة المجتمع، والارتقاء لمصاف الجامعات المرموقة محليا واقليميا وعالميا.
المساهمة في بناء مجتمع المعرفة وتطوره من خلال إيجاد بيئة جامعية، وشراكة مجتمعية محفزة للابداع، وحرية الفكر والتعبير، ومواكبة التطورات التقنية في مجال التعليم، ومن ثم رفد المجتمع بما يحتاجه من موارد بشرية مؤهلة وملائمة لاحتياجات سوق العمل.
تلتزم الجامعة بترسيخ القيم الجوهرية التالية: الإلتزام الإجتماعي والأخلاقي، الإنتماء،العدالة والمساواة، الإبداع، الجودة والتميّز، الشفافية والمحاسبة، الحرية المنظبطة والمستقبلية.
Any organization that intends to use component-based software development, like outsourcing software, must first evaluate existing components against system requirements to find the best fit among many alternatives. As a result, there should be a mechanism to help with decision-making. Our proposed methodology tries to select the best alternative among available components, using the best decision- making approach. As an integrated method for order preference, the methodology in this paper uses two well-known criterion decision-making procedures, namely Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). By analyzing and selecting the optimal solution among a variety of Out Sourcing (OS) modules, the new model design makes the decision-making process easier. We evaluated two software attributes and predicted which was more effective. In this case, the advantage of utilizing AHP is that it allows the developer to evaluate the structure of the OS selection problem and calculate weights for the chosen criteria. After that, the SAW technique is used to calculate the alternatives ratings for OS components. The integration strategy used in our model and the resulting preference indication, which is produced as an explicit numeric value.
The extreme learning machine (ELM) is a method to train single-layer feed-forward neural networks that became popular because it uses a fast closed-form expression for training that minimizes the training error with good generalization ability to new data. The ELM requires the tuning of the hidden layer size and the calculation of the pseudo-inverse of the hidden layer activation matrix for the whole training set. With large-scale classification problems, the computational overload caused by tuning becomes not affordable, and the activation matrix is extremely large, so the pseudo-inversion is very slow and eventually the matrix will not fit in memory. The quick extreme learning machine (QELM), proposed in the current paper, is able to manage large classification datasets because it: (1) avoids the tuning by using a bounded estimation of the hidden layer size from the data population; and (2) replaces the training patterns in the activation matrix by a reduced set of prototypes in order to avoid the storage and pseudo-inversion of large matrices. While ELM or even the linear SVM cannot be applied to large datasets, QELM can be executed on datasets up to 31 million data, 30,000 inputs and 131 classes, spending reasonable times (less than 1 h) in general purpose computers without special software nor hardware requirements and achieving performances similar to ELM.
Abstract—Due to their large number of applications, eyetracking systems have gain attention recently. In this work, we propose 4 new features to support the most used feature by these systems, which is the location (x, y). These features are based on the white areas in the four corners of the sclera; the ratio of the whites area (after segmentation) to the corners area is used as a feature coming from each corner. In order to evaluate the new features, we designed a simple eye-tracking system using a simple webcam, where the users faces and eyes are detected, which allows for extracting the traditional and the new features. The system was evaluated using 10 subjects, who looked at 5 objects on the screen. The experimental results using some machine learning algorithms show that the new features are user dependent, and therefore, they cannot be used (in their current format) for a multiuser eye-tracking system. However, the new features might be used to support the traditional features for a better single-user eye-tracking system,
All Rights Reseved © 2023 - Developed by: Prof. Mohammed M. Abu Shquier Editor: Ali Mayyas
