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.
يرجى رفع السيرة الذاتية
حاصل على شهادتي البكالوريوس والماجستير من جامعة اليرموك. وشهادة الدكتوراه من الجامعة الأردنية. مجال البحث هو الذكاء الاصطناعي ومعالجة الصور
الثانوية: علمي 74.3 جيد
بكلوريوس: علم حاسوب 77 جيد جدا
ماجستير: علم حاسوب 80.8 ممتاز
دكتوراة: علم حاسوب "ذكاء اصطناعي ومعالجة الصور" 3.71 ممتاز
1- 2012-2013: Programming Language Teacher, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, Jordan.
2- 1/6/2022-Until now: Assistant Professor, Faculty of Computer Science and Information Technology, Jerash University, Jerash, Jordan.
This paper proposes a new enhanced algorithm called modernised genetic algorithm for solving the travelling salesman problem (MGA-TSP). Recently, the most successful evolutionary algorithm used for TSP problem, is GA algorithm. The main obstacles for GA is building its initial population. Therefore, in this paper, three neighbourhood structures (inverse, insert, and swap) along with 2-opt is utilised to build strong initial population. Additionally, the main operators (i.e., crossover and mutation) of GA during the generation process are also enhanced for TSP. Therefore, powerful crossover operator called EAX is utilised in the proposed MGA-TSP to enhance its convergence. For validation purpose, we used TSP datasets, range from 150 to 33,810 cities. Initially, the impact of each neighbouring structure on the performance of MGA-TSP is studied. In conclusion, MGA-TSP achieved the best results. For comparative evaluation. MGA-TSP is able to outperform six comparative methods in almost all TSP instances used.
Distributed security is an evolving sub-domain of information and network security. Security applications play a serious role when data exchanging, different volumes of data should be transferred from one site to another safely and at high speed. In this paper, the parallel International Data Encryption Algorithm (IDEA) which is one of the security applications is implemented and evaluated in terms of running time, speedup, and efficiency. The parallel IDEA has been implemented using message passing interface (MPI) library, and the results have been conducted using IMAN1 Supercomputer, where a set of simulation runs carried out on different data sizes to define the best number of processor which can be used to manipulate these data sizes and to build a visualization about the processor number that can be used while the size of data increased. The experimental results show a good performance by reducing the running time, and increasing speed up of encryption and decryption processes for parallel IDEA when the number of processors ranges from 2 to 8 with achieved efficiency 97% to 83% respectively.
The Traveling Salesman Problem (TSP) is a Combinatorial Optimization Problem (COP), which belongs to NP-hard problems and is considered a typical problem for many real-world applications. Many researchers used the Genetic Algorithm (GA) for solving the TSP. However, using a suitable mutation was one of the main obstacles for GA. This paper proposes for GA an Efficient Mutation (GA-EM) for solving TSP. The efficient mutation can balance between deeply searching and preventing stuck on local optima to ensure a better convergence rate and diversity. Therefore, in this paper, a local search method based on three neighborhood structure operators; namely, transpose, shift-and-insert, and swap, is proposed to produce the efficient mutation for GA. The performance of the proposed algorithm is validated by three TSP datasets; including, TSPLIB, National TSPs, and VLSI Data Set. These datasets have different graphs’ structures and sizes. The sizes of the datasets range from 150 to 18512 cities. For comparative evaluation, the results obtained from the proposed GA-EM are compared with those obtained by four relatively recent approaches using the same TSP instances. These approaches are the Modernised Genetic Algorithm for solving TSP (MGA-TSP), List-Based Simulated Annealing algorithm (LBSA), Symbiotic Organisms Search optimization algorithm based on Simulated Annealing (SOS-SA), and Multiagent Simulated Annealing algorithm with Instance-Based Sampling (MSA-IBS). The GA-EM outperformed these approaches in all used TSP instances in terms of accuracy. The Traveling Salesman Problem (TSP) is a Combinatorial Optimization Problem (COP), which belongs to NP-hard problems and is considered a typical problem for many real-world applications. Many researchers used the Genetic Algorithm (GA) for solving the TSP. However, using a suitable mutation was one of the main obstacles for GA. This paper proposes for GA an Efficient Mutation (GA-EM) for solving TSP. The efficient mutation can balance between deeply searching and preventing stuck on local optima to ensure a better convergence rate and diversity. Therefore, in this paper, a local search method based on three neighborhood structure operators; namely, transpose, shift-and-insert, and swap, is proposed to produce the efficient mutation for GA. The performance of the proposed algorithm is validated by three TSP datasets; including, TSPLIB, National TSPs, and VLSI Data Set. These datasets have different graphs’ structures and sizes. The sizes of the datasets range from 150 to 18512 cities. For comparative evaluation, the results obtained from the proposed GA-EM are compared with those obtained by four relatively recent approaches using the same TSP instances. These approaches are the Modernised Genetic Algorithm for solving TSP (MGA-TSP), List-Based Simulated Annealing algorithm (LBSA), Symbiotic Organisms Search optimization algorithm based on Simulated Annealing (SOS-SA), and Multiagent Simulated Annealing algorithm with Instance-Based Sampling (MSA-IBS). The GA-EM outperformed these approaches in all used TSP instances in terms of accuracy.
الجدول الدراسي:
نماذج المحاكاة 2.00 - 3.30 اثنين اربعاء مدمج
قواعد البيانات 2.00 - 3.30 احد ثلاثاء مدمج
نظم التشغيل 12.30 - 2.00 اثنين اربعاء مدمج
الذكاء الاصطناعي 8.00 - 9.30 ثلاثاء اربعاء الكتروني
ساعات مكتبية:
احد 1.00 - 2.00
اثنين 11.30 - 12.30
All Rights Reseved © 2023 - Developed by: Prof. Mohammed M. Abu Shquier Editor: Ali Mayyas
