AQU Master Theses الرسائل الجامعية الخاصة بجامعة القدس

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الرسائل الجامعية

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3D Environment Based on Desktop Metaphor A Usability Study
(AL-Quds University, 2004-12-30) سوزان جميل حلمي سلطان.; Suzanne Jamil Hilmi Sultan; غسان القيمري; Rashid Jayousi; Yousef Asad Hassouneh
3D Environment Based on Desktop Metaphor: A Usability Study
(AL-Quds University, 2004-12-30) سوزان جميل سلطان; suzanne jamil sultan; غسان القيمري; Rashid Jayousi; Yousef Asad Hassouneh
An Aggregate Scalable Scheme for Expanding the Crossbar Switch Network; Design and Performance Analysis
(AL-Quds University, 2004-09-05) أحلام محمد درويش قريع; Ahlam Mohammed Darwish Qrie; عبدالكريم عياد; Rashid JAYOUSI; Luai MALHIS
New computer network topology, called Penta-S, is simulated. This network is built of cross bar switch modules. Each module connects 32 computer nodes. Each node has two ports, one connects the node to the crossbar switch module and the other connects the node to a correspondent client node in another module through a shuffle link. The performance of this network is simulated under various network sizes, packet lengths and loads. The results are compared with those obtained from Macramé project for Clos multistage interconnection network and 2D-Grid network. The throughput of Penta-S falls between the throughput of Clos and the throughput of 2D-Grid networks. The maximum throughput of Penta-S was obtained at packet length of 128 bytes. Also the throughput grows linearly with the network size. On the opposite of Clos and 2D-Grid networks, the per-node throughput of Penta-S improves as the network size grows. The per-packet latency proved to be better than that of Clos network for large packet lengths and high loads. Also the packet latency proved to be nearly constant against various loads. The cost-efficiency of Penta-S proved to be better than those of 2D-Grid and Clos networks for large number of nodes (>200 nodes in the case of 2D-Grid and >350 nodes in the case of Clos).On the opposite of other networks, the cost-efficiency of Penta-S grows as its size grows. So this topology suits large networks and high traffic loads.
Analysis Study of Classification Techniques for Web Services
(AL-Quds University, 2017-10-07) دعاء وليد عطا فرعون; Duaa Waleed Ata Faroun; رشيد الجيوسي; Nidal Kafri; Derar Eleyan
Analytical study of E-Learning Platforms
(AL-Quds University, 2007-04-03) ماجد احمد عبد الله أبو ريان; Majed Ahmed Abdallah Abu Rayyan; رشيد جيوسي; نضال; يوسف ابو زر
Application–Based Statistical Approach for Identifying Appropriate Queuing Model
(AL-Quds University, 2015-05-14) نعيمه 'محمد سالم' صالح حرباوي; Naimeh Mohammad Salem Saleh Hirbawi; بديع سرطاوي; رائد الزغل; اسامه سلامه
Queuing theory is a mathematical study of queues or waiting lines. It is used to model many systems in different fields in our life, whether simple or complex systems. The key idea in queuing theory of a mathematical model is to improve performance and productivity of the applications. Queuing models are constructed in order to compute the performance measures for the applications and to predict the waiting times and queue lengths. This thesis is depended on previous papers of queuing theory for varies application which analyze the behavior of these applications and shows how to calculate the entire queuing statistic determined by measures of variability (mean, variance and coefficient of variance) for variety of queuing systems in order to define the appropriate queuing model. Computer simulation is an easy powerful tool to estimate approximately the proper queuing model and evaluate the performance measures for the applications. This thesis presents a new simulation model for defining the appropriate models for the applications and identifying the variables parameters that affect their performance measures. It depends on values of mean, variance and coefficient of the real applications, comparing them to the values for characteristics of the queuing model, then according to the comparison the appropriate queuing model is approximately identified.The simulation model will measure the effectiveness performance of queuing models A/B/1 where A is inter arrival distribution, B is the service time distributions of the type Exponential, Erlang, Deterministic and Hyper-exponential. The effectiveness performance of queuing model are: *L : The expected number of arrivals in the system. *Lq : The expected number of arrivals in the queue. *W : The expected time required a customer to spend in the system. *Wq : The expected time required a customer to spend in Queue. *U : the server utilization.
Arabic Alphabet Deaf Sign Gestures Recognition Based on Deep Machine Learning Methods
(AL-Quds University, 2019-07-13) عهود عادل سلامة الدرابيع; Ohood Adel Salameh Darabee; عبد الله كمال; بديع سرطاوي; احمد حساسنة
Automatic Matching Engine: Towards Enhanced Finding of Jobs & Learning Opportunities
(AL-Quds University, 2016-10-20) يوسف مشهور ابراهيم صباح; Yousef Mashhour Ibrahim Sabbah; جاد النجار; Dr. Badie Sartawi; Dr. Ahmed Ewais
Benchmark for Tuning Metaheuristic Optimization Technique to Optimize Traffic Light Signals Timing
(AL-Quds University, 2015-12-10) رامي كمال عزت ابوشهاب; Rami Kamal Izzat Abu Shehab; بديع سرطاوي; Baker Abdalhaq; رشيد جيوسي; Emad Barhoumi
Traffic congestion at intersections is an international problem in the cities. This problem causes more waiting time, air pollution, petrol consumption, stress of people and healthy problems. Against this background, this research presents a benchmark iterative approach for optimal use of the metaheuristic optimization techniques to optimize the traffic light signals timing problem. A good control of the traffic light signals timing on road networks may help in solving the traffic congestion problems. The aim of this research is to identify the most suitable metaheuristic optimization technique to optimize the traffic light signals timing problem, thus reducing average travel time (ATT) for each vehicle, waiting time, petrol consumption by vehicles and air pollution to the lowest possible level/degree. The central part of Nablus road network has a huge traffic congestion at the traffic light signals. It was selected as a research case study and was represented by the SUMO simulator. The researcher used a random algorithm and three different metaheuristic optimization techniques: three types of Genetic Algorithm (GA), Particle Swarm Algorithm (PS) and five types of Tabu Search Algorithm (TS). Parameters in each metaheuristic algorithm affect the efficiency of the algorithm in finding the optimal solutions. The best values of these parameters are difficult to be determined; their values were assumed in the previous traffic light signals timing optimization research. The efficiency of the metaheuristic algorithm cannot be ascertained of being good or bad. Therefore, the values of these parameters need a tuning process but this cannot be done by using SUMO simulator because of its heavy computation. The researcher used a benchmark iterative approach to tune the values of them etaheuristic algorithm parameters by using a benchmark function. The chosen function has similar characteristics to the traffic light signals timing problem. Then, through the use of this approach, the researcher arrived at the optimal use of the metaheuristic optimization algorithms to optimize traffic light signals timing problem. The efficiency of each metaheuristic optimization algorithm, tested in this research, is in finding the optimal or near optimal solution after using the benchmark iterative approach. The results of metaheuristic optimization algorithm improved at some values of the tuned parameters. The researcher validated the research results by comparing average results of the metaheuristic algorithms, used in solving the traffic light signals optimization problem after using benchmark iterative approach, with the average results of the same metaheuristic algorithms used before using the benchmark iterative approach; they were also compared with the results of Webster, HCM methods and SYNCHRO simulator. In the light of these study findings, the researcher recommends trying the benchmark iterative approach to get ore efficient solutions which are very close to the optimal solution for the traffic light signals timing optimization problem and many complex practical optimization problems that we face in real life.
Comparison of Data Mining and Statistical Techniques for Prediction Model
(AL-Quds University, 2012-05-10) امجد عبد المنعم محمود حرب; AMJAD A. M. HARB; رشيد الجيوسي; Nidal Kafri; Yousef Abuzir
The aim of this research is to perform a comparison study between statistical and data mining modeling techniques. These techniques are statistical Logistic Regression, data mining Decision Tree and data mining Neural Network. The performance of these prediction techniques were measured and compared in terms of measuring the overall prediction accuracy percentage agreement for each technique and the models were trained using eight different training datasets samples drawn using two different sampling techniques. The effect of the dependent variable values distribution in the training dataset on the overall prediction percent and on the prediction accuracy of individual “0” and “1” values of the dependent variable values was also experimented. For a given data set, the results shows that the performance of the three techniques were comparable in general with small outperformance for the Neural Network. An affecting factor that makes the percent prediction accuracy varied is the dependent variable values distribution in the training dataset, distribution of “0” and “1”. The results showed that, for all the three techniques, the overall prediction accuracy percentage agreement was high when the dependent variable values distribution ratio in the training data was greater than 1:1 but at the same time they, the techniques, fails to predict the individual dependent variable values successfully or in acceptable prediction percent. If the individual dependent variable values needed to be predicted comparably, then the dependent variable values distribution ratio in the training data should be exactly 1:1.
A Comparison Study on the Performance of Different Applications using MANET Routing Protocols under Various Circumstances
(AL-Quds University, 2015-03-15) شذى داود حسين نجم; Shatha Dawod Husien Nijim; د. نضال الكفري; Dr. Raed Al-Zagal; د. أحمد سعدة
Mobile adhoc network is a collection of mobile devices that communicate amongst each other using message passing to collaborate in a wireless medium, without any centralized management; each device acts as a router, sends and receive packets. Nodes can move freely and can set itself in any adhoc network. Adhoc networks are widely use in the absence of the wired network infrastructure. Quality of service of routing in ad hoc networks is an important and complicated issue with a changing topology. In this work we carried out a comparison study in a simulation scenarios on the performance of different routing protocols i.e., proactive and reactive, with different standard applications such as FTP, HTTP and database under various circumstances by means of network size, load, and speed of nodes. As a conclusion of this study, results show when measuring performance of delay and throughput of FTP, HTTP and Database traffic, delay and throughput metrics, using AODV, DSR, OLSR routing protocols, under 10, 50 and 100 nodes with spee of 10, 30 m/s. When using DSR routing protocol it showed the worst results under various network size and speed between other protocols, while when using AODV routing protocol it performed in a better way in which it showed a good performance in small and medium network size. OLSR routing protocol performed the best to be used in all network size especially in large network size.
Consolidated Ranking and Recommendation Framework for Learning Objects Based on Usage Data
(AL-Quds University, 2013-05-10) بهاء حمزة محمد هرشه; Baha Hamzah Mohammad Harasheh; جاد النجار; Raid Zaghal; Derar Eleyan
Customize Social Network Analysis for Telecommunications Companies
(AL-Quds University, 2017-07-05) اكثم فهيم عقل صوان; aktham faheem aqel sawan; رشيد الجيوسي; Dr. Nidal Kafri; Dr.Yousef Abu Zir
Social Network Analysis (SNA) is created to analyze social network data. Therefore, the main companies in the data mining filed (such as IBM, SAS, R and python) have created their own SNA algorithms. The aim of this research is to create customized SNA algorithm for telecom companies because the current algorithms were not designed just for the telecom networks, in addition when current algorithms were used for telecom many high value customer not include in final result plus results coverage just 55% from input customers, so in the new algorithm relation strength and extenders were used to enhance final results 300 million records that belong to around 4 million customers in the last three were collected from (Jawwal Telecommunications Company) as case study. The current algorithms and the new algorithm were used the same data. In this research six experiments were applied based on call duration, call count and ratio between call duration and call count, in addition two groups size were used (15 and 20), Oracle Sql-PL/SQL was used to implement algorithm. The results that approved by Jawwal were based on parameters that used in experiment number six (ratio between calls count and call duration with group size till 20 customer), it has increased the coverage of NW to be 75.9% instead of around 55% for current algorithms, in addition all high valued customers has included in results for the new algorithm, moreover algorithm have applied in Mobily in Saudi Arabia and the same positive results have been found same as Jawwal. New novelty ideas have created in this research such as, extenders this type of customers used for customer who is influencer in one group and follower in the other group. Also relation strength used to create groups and assign followers to their most related influencer; furthermore, Super Group used as new layer to connect related groups in one group and find super influencer.
Data Mining in E-Government: Design of Palestinian E-Government and Identifying Suitable Mining Model
(AL-Quds University, 2011-03-29) خلود عبدالحليم محمد طويل; KHOLOUD ABED HALIM NMOHAMMED TAWIL; بديع السرطاوي; رائد الزغل; رضوان طهبوب
Design Principles of Learning Management Systems
(AL-Quds University, 2016-12-25) أحمد محمد داود رداد; AHMED MOHAMMED DAWOD RADDAD; جاد نجار; Dr. Badie Sartawi; Dr. Raid Al-Zaghal; Dr. Muath Sabha
DIHA : data integrity algorithm using hashing authentication
(AL-Quds University, 2013-08-20) محمد احمد محمود جاموس; Mohammed Ahmad Mahmoud Jamoos; رشدي حمامرة; نضال كفري; امين ابو سمرة
DSDV Extension to Enhance the Performance of Ad Hoc Networks in High Diverse-Velocity
(AL-Quds University, 2018-12-12) مضاء عبد اللطيف يوسف عبد الجواد; MADA ABDEL LATIF YOUSEF ABDEL JAWAD; سعيد صلاح; رائد الزغل; نضال كفري; اياد طومار
Dynamic Learning Profile (DLP) based on Achieved Learning Outcomes
(AL-Quds University, 2017-12-05) بهاء محمد غالب خضر ثابت; Baha Mohammed Ghaleb Khader Thabet; بديع سرطاوي; Kamel Hashem; Fatima Hallak
Dynamic User-Oriented Role-Based Access Control Model (DUO-RBAC)
(AL-Quds University, 2018-04-10) حازم مالك حسني كيوان; Hazem Malek Husni Kiwan; رشيد الجيوسي; Badie Sartawi; Radwan Tahboub
Most researchers now trend to use role mining to generate role-based access control model from the existing user-permission assignments. User-oriented role-based access control is a type of role-based access control model, which aims to use role mining from end user perspective to generate a user-oriented RBAC model, since the user almost prefer a simple and minimum role assignments. This research is the first for generating a dynamic user-oriented rolebased access control model (DUO-RBAC) for inserting a new user-permission assignments (new UPA) to the existing user-oriented RBAC model. In a quick clarification, if there is a system which has user-permission assignments, a user-oriented RBAC model can be generated which contains new roles, each one assigns to users and permissions. Then, if we have a new users with new permissions should enter the system which has the model, we will regenerate a new model with new roles assignments to include these new users. Re-generating roles will be done by our dynamic model, with three constraints. First, there are no changes in the number of role assignments for each user in the system after the inserting process, since the user will be conflicted if he has different number of roles from time to time. Second, the permissions that each user has before the inserting process must be the same after generating the new model. Last one, will take into account that each user assign to number of roles no more than t (maximum number of roles that each user can assign), where t is predefined in the existing user-oriented RBAC model. Also, we develop a new algorithm, which based on user-oriented role mining to find the optimal way for inserting the new user permission assignments to the existing model. Our experiments applied on benchmark “Access Control” real datasets to evaluate the results and show the effectiveness of our developed algorithm of several measures. Those measures are: optimal number of roles to make the objective function minimized, optimal number of user-role assignments and generating a new model from end user perspective (keep the new generated model suitable from end-user perspective).
E-Learner Recommendation Model Based on Level of Learning Outcomes Achievement
(AL-Quds University, 2018-06-03) عبير حسن عبد الرحيم دار موسى; ABEER H A dar mousa; بديع سرطاوي; Kamel Hashem; Ahmad Ewais
Students in any learning environment differ in their level of knowledge, achieved learning outcomes, learning style, preferences, misunderstand and attempts in solving and addressing problems when their expectations are not met. When a student searches the web as an attempt to solve a problem, he suffers from the large number of resources which are, in most cases, not related to his “needs”, or may be related but complex and advance. The result of his search might make him more confused, scattered, depressed and finally result in wasting his time which – in some cases -may have negative effects on his achievements. From here comes the need for an intelligent learning system that can guide studentsbased on their needs. This research attempts to design and build an educational recommender system for a web-based learning environment in order to generate meaningful recommendations of the most interested and relevant learning materials that suit students’ needs based on their profiles1 . This can be achieved by accessing students’ history, exploring their learning navigation patterns and making use of similar students’ experiences and their success stories. The study proposed a design for a hybrid recommender system architecture which consists of two recommendation approaches: the content and collaborative filtering. The study concentrates on the collaborative recommender engine which will recommend learning materials based on students’ level of knowledge, looking at active students' profiles, and achievements in both learning outcomes and learning outcomes levels making use of similar students’ success stories and reflecting their good experience on active student who are in the same level of knowledge. The design of the collaborative recommender engine includes the “learning” module from which the engine learns past students’ access pattern and the “advising” module from which the engine reflects the experience of similar success stories on active students. The content base recommender engine with its suggested stages is considered as future work, the research used the k-mean cluster algorithm to find out similar students where five distance function are used: Euclidean, Correlation. Jaccard,cosine and Manhattan. The cosine function shows to be the most accurate distance function with the minimum SSE but the highest processing time that doesn’t differ a lot when compared the rest functions. The best number of clusters for the selected dataset was determined using three methods Elbow, Gap-statistic and average Silhouette approach where the best number of cluster shows to be three. The research used the two result rating matrices of similar good and good students with Learnings material in order to calculate learning material weights and rank them based on highest weights which results in a final recommendation list.

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