DSpace at My UniversityThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.http://scholar.ppu.edu:802024-03-28T10:47:41Z2024-03-28T10:47:41ZJob Hubجبور, عمرابو خلف, محمودأبو صبحة, معتصمscholar.ppu.edu/handle/123456789/90452024-03-21T09:08:29Z2024-01-01T00:00:00ZJob Hub
جبور, عمر; ابو خلف, محمود; أبو صبحة, معتصم
مشروع تخرج بعنوان "Jon Hub" منصة توظيف عبر الإنترنت هو مشروع يهدف إلى تطوير وبناء
منصة إلكترونية توفر خدمات التوظيف بين الباحثين عن عمل والشركات وأصحاب العمل الحر. تهدف
المنصة إلى تسهيل عملية البحث عن وظائف لجميع التخصصات وتسهيل عملية توظيف الشركات
واكتشاف أصحاب المواهب المستقلين.
توفر المنصة واجهة سهلة الاستخدام للباحثين عن عمل لإنشاء حساباتهم وتحميل سيرتهم الذاتية و
استعراض الوظائف المتاحة في مختلف التخصصات. بالإضافة إلى ذلك، تتيح المنصة لأصحاب العمل
والشركات إنشاء حسابات خاصة بهم ونشر وظائف العمل المتاحة واستعراض الملفات الشخصية
للمتقدمين والتواصل معهم.
من المميزات التي يوفرها المشروع هي واجهة مستخدم جذابة وبسيطة، ونظام بحث متقدم يساعد الباحثين
عن عمل على العثور على الفرص المناسبة، ونظام تنبيهات واشعارات لإبلاغ المستخدمين بالوظائف
المتاحة والتحديثات الهامة، ونظام تقييم ومراجعة يساعد على بناء سمعة وثقة بين الباحثين عن عمل
وأصحاب العمل.
من خلال تنفيذ هذا المشروع، يتوقع أن يسهم في تحسين عملية التوظيف وتسهيل التواصل بين الباحثين
عن عمل وأصحاب العمل، وتوفير فرص وظيفية لمختلف التخصصات، ودعم الاقتصاد المحلي من خلال
تعزيز فرص العمل وتمكين العمل الحر.
no of pages 148 , نظم معلومات 2024
2024-01-01T00:00:00ZEnhanced Variants of Crow Search Algorithm Boosted with Cooperative Based Island Model for Global OptimizationThaher, ThaerAwad, MohammedSheta, AlaaAldasht, Mohammedscholar.ppu.edu/handle/123456789/90442024-03-21T08:49:28Z2024-03-15T00:00:00ZEnhanced Variants of Crow Search Algorithm Boosted with Cooperative Based Island Model for Global Optimization
Thaher, Thaer; Awad, Mohammed; Sheta, Alaa; Aldasht, Mohammed
The Crow Search Algorithm (CSA) is a swarm-based metaheuristic algorithm that simulates the intelligent foraging behaviors of crows. While CSA effectively handles global optimization problems, it suffers from certain limitations, such as low search accuracy and a tendency to converge to local optima. To address these shortcomings, researchers have proposed modifications and enhancements to CSA’s search mechanism. One widely explored approach is the structured population mechanism, which maintains diversity during the search process to mitigate premature convergence. The island model, a common structured population method, divides the population into smaller independent sub-populations called islands, each running in parallel. Migration, the primary technique for promoting population diversity, facilitates the exchange of relevant and useful information between islands during iterations. This paper introduces an enhanced variant of CSA, called Enhanced CSA (ECSA), which incorporates the cooperative island model (iECSA) to improve its search capabilities and avoid premature convergence. The proposed iECSA incorporates two enhancements to CSA. Firstly, an adaptive tournament-based selection mechanism is employed to choose the guided solution. Secondly, the basic random movement in CSA is replaced with a modified operator to enhance exploration. The performance of iECSA is evaluated on 53 real-valued mathematical problems, including 23 classical benchmark functions and 30 IEEE-CEC2014 benchmark functions. A sensitivity analysis of key iECSA parameters is conducted to understand their impact on convergence and diversity. The efficacy of iECSA is validated by conducting an extensive evaluation against a comprehensive set of well-established and recently introduced meta-heuristic algorithms, encompassing a total of seventeen different algorithms. Significant differences among these comparative algorithms are established utilizing statistical tests like Wilcoxon’s rank-sum and Friedman’s tests. Experimental results demonstrate that iECSA outperforms the fundamental ECSA algorithm on 82.6% of standard test functions, providing more accurate and reliable outcomes compared to other CSA variants. Furthermore, Extensive experimentation consistently showcases that the iECSA outperforms its comparable algorithms across a diverse set of benchmark functions.
2024-03-15T00:00:00ZDynamic and Distributed Intelligence over Smart Devices, Internet of Things Edges, and Cloud Computing for Human Activity Recognition Using Wearable SensorsWazwaz, Ayman, Khalid Amin, Noura Semary, and Tamer Ghanemscholar.ppu.edu/handle/123456789/90432024-03-21T08:48:46Z2024-01-02T00:00:00ZDynamic and Distributed Intelligence over Smart Devices, Internet of Things Edges, and Cloud Computing for Human Activity Recognition Using Wearable Sensors
Wazwaz, Ayman, Khalid Amin, Noura Semary, and Tamer Ghanem
2024-01-02T00:00:00ZA general family of fifth-order iterative methods for solving nonlinear equationsZein, Alischolar.ppu.edu/handle/123456789/90422024-03-21T08:48:18Z2023-10-30T00:00:00ZA general family of fifth-order iterative methods for solving nonlinear equations
Zein, Ali
A family of fifth-order iterative methods is proposed for solving nonlinear equations
using the weight function technique. This family offers flexibility through its structure and the choice of weight functions, resulting in a wide range of new specific schemes. It is demonstrated that this proposed family includes several well-known and recent methods as special cases. In addition, several new particular methods are designed to achieve better results than existing methods of the same type. Convergence analysis is conducted, and numerical examples in both real and complex domains are provided for several specific schemes within the proposed family. Comparisons between the existing methods within this family and the newly introduced methods generally indicate improved performance among the new members. Notably, the study of complex dynamics and basins of attraction reveals that our new specific schemes have broader sets of initial points that lead to convergence.
2023-10-30T00:00:00Z