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.
دكتوراة نظم المعلومات الحاسوبية
PH.D Degree in Computer Information Systems, University of Banking and Financial Sciences, Jordan-2011
Master’s Degree in Information Systems, Arab Academy for Banking and Financial Sciences, Jordan -2000
High Diploma in Information Systems, Arab Academy for Banking and Financial Sciences, Jordan -1999
2010-2011 Manager Department of Computer, Amaken Plaza Hotel, Jordan Amman.
2008-2012 Head of Applied Arts and Information Technology Dept, Al-Andalus Collage.
2000-2011 lecturer, The Arab Collage, Jordan Amman.
2008 Part-time lecturer, NewHorizons , Jordan Amman .
2000-2003 Part-time lecturer, The Arab Academy For Banking And Financial Sciences “AABFS”,Jordan Amman .
2002-2003 Part-time lecturer, Alisra Private UNV, Jordan Amman .
2002-2007 Part-time lecturer, Princess Alia UNV Collage, Jordan Amman.
2001-2002 Part-time lecturer, Al-Fashir UNV, Jordan Amman.
2001-2004 Part-time lecturer, The Kadecy Collage, Jordan Amman.
1999-2000 MIS officer, Multi Base Systems company, Jordan Amman.
Speech recognition is an important part of human-machine interaction which represents a hot area of researches in the field of computer systems, electronic engineering, communications, and artificial intelligence. While speech signal is very complex and contains huge number of sampling points, the extraction of features from its time and frequency domain is very complex by analytical methods. The neural network capabilities to estimate the complex functions make it very reliable in such applications. This paper presents speech recognizer based on feed forward neural network with multi layer perceptron structure. The speech is preprocessed by two methods; discrete wavelet transformation (DWT) and principal component analysis (PCA). The results and structure are presented and comparison is make over them.
Many clustering algorithms with different methodologies are subjected to be common techniques and main step in many applications in the computer science world. The need of adapting efficient clustering algorithm increases in critical applications (i.e. wireless sensors networks). Utilizing the Fuzzy Logic power; Fuzzy C-mean (FCM) clustering has a major role in most clustering applications. But in many cases, the result of FCM is considered to be non-complete clustering strategy. This paper adapted the FCM algorithm to enable of generating clusters with equal sizes. Also, scattered points that are located far away from all clusters are grouped out of clusters. Another modification is to localize specific points that have ability to locate in more than one cluster; hence this has a non-negligible importance in some fields such as cellular communications.
Clustering is a type of classification under optimization problems, which is considered as a critical area of data mining. Medical clustering problem is a type of unsupervised learning in data mining. This work present a hybridization between our previous proposed Iterative Simulated Annealing (ISA) and Modified Great Deluge (MGD) algorithms for medical clustering problems. The aim of this work is to produce an effective algorithm for partitioning N objects into K clusters. The idea of the hybridization between MGD and ISA is to incorporate the strength of one approach with the strength of the other hoping a more promising algorithm. Also this combination can help to diverse the search space. Experimental results obtained two way of calculating the minimal distance that have been tested on six benchmark medical datasets show that, ISA-MGD is able to outperform some instances of MGD and ISA algorithms.
As a completion of Arabic hand written recognition, the Arabic numbers which are commonly known as “Old-Indian Numbers”are taken place in full text recognition and applications. Hand written Arabic numbers have less complexity than Arabiccharacters, but such researches are has few interests. The curves shape of these numbers make its characteristics difficult to berecognized by intelligent systems. This paper adapts and implements neural network to takes the features of number segmentand recognize it in high reliability and accuracy. The numbers are being segmented by morphological approach. A MATLABbased program is developed to validity testing of the proposed system enhance the accuracy to by 98% over the testing data set.
the use of biometric information has been widely known for both people identification andsecurity application. It is common knowledge that each person can be identified by the unique characteristicsof one or more of biometric features. One most unique and identifiable biometric characteristics is the iris,wherever the second is the voice, and the third is finger print. This research attempts to apply iris recognitiontechniques based on the technology invented by Dr. John G. Daugman, an attempt of implementing a buildan end user application. Iris Recognition is expected to play a major role in a wide range of applications inwhich a person's identity must be established or confirmed in high reliability and high privacy, Includingaccess controls, authorizations, ID detection, etc. This research depends on standard iris images was tokenfrom CASIA database. The most efficient computer language for simulation and technical computing(MATLAB) will be used to make the problem statement and result in addition to mathematical and AImodelling more easier and reliable.
In the rise rabid research related to biometrics, bio-informatics and genome; many researches, fields, and issues are still undergoing any uncertainties. One of the hottest areas in this field of research is the proteins informatics, that is relates the protein data with the modern information technology and it includes portions mapping and classification. This paper contributes an intelligent system which consists of adaptive neuro-fuzzy computations that is able to recognize and classify the proteins in families. An intelligent trainer will be structured based on Perceptron neural network in order to build an intelligent fuzzy inference system that is capable of predicting and classifying that data into categories according to the function of each protein. The structured system preprocesses that data set and extracts unique features from it. The system was built using a highly developed programming language. This paper will clearly show the results that such system achievement about 92% of accuracy when over 1000 inputs sequence of the validation sample was processed.
Complexity of Arabic writing language makes its handwritten recognition very complex in terms of computeralgorithms. The Arabic handwritten recognition has high importance in modern applications. The contour analysisof word image can extract special contour features that discriminate one character from another by the mean ofvector features. This paper implements a set of pre-processing functions over a handwritten Arabic characters, withcontour analysis, to enter the contour vector to neural network to recognize it. The selection of this set of preprocessingalgorithms was completed after hundreds of tests and validation. The feed forward neural networkarchitecture was trained using many patterns regardless of the Arabic font style building a rigid recognition model.Because of the shortcomings in Arabic written databases or datasets, the testing was done by non-standard dataset. The presented algorithm structure got recognition ratio about 97%.
Fingerprint recently hasquite unique applications in manyfields such as crime investigationand workers attendance control.The fingerprint is a personalidentification tool as it differs fromone person to another. Recentadvances in computer programmingand software production led tofacilitate the use of fingerprint as apowerful computerized tool. Thispaper aims to develop a modifiedhybrid fingerprint recognitionsystem, based on artificial neuralnetwork with Kohonen selforganizedmap (SOM), withminutiae matching technique. Inorder to recognize fingerprints, theacquired fingerprint data setsshould be processed to enhancementthe image for minutiae extraction.Image segmentation technique wasimplemented to localize the effectivearea of the fingerprint followedbefore applying the minutiaeextraction that includes ridgethinning and minutiae marking.Then removal of the H-breaks,isolated points and false minutiae isapplied.
Software maintainability has been considered as a maincharacteristic in many software product quality models. However,these models have different definitions for maintainability andsub characteristics. ISO 9126 is one of the main and mostfrequently used models in software product quality. This modelhas been revised and replaced by ISO 25010 as a new model ofsoftware product quality. In addition to the many modificationsthat were performed on ISO 9126 model, maintainability was oneof the main modified characteristics. However, it was developedunclearly without any standard base, and with no clear definitionor evidence of how the sub characteristics were defined andmodified. This paper investigates these modifications and thedifferences between the definitions of the maintainability in thetwo models, ISO 9126 and ISO 25010. As a result of thisdiscussion, it has been concluded that both models ISO 9126 andISO 25010 lack of a clear definition or standard base for definingsoftware maintainability and its sub characteristics.
Complexity of Arabic writing language makes its handwritten recognition very complex in terms of computer algorithms. The Arabic handwritten recognition has high importance in modern applications. The contour analysis of word image can extract special contour features that discriminate one character from another by the mean of vector features. This paper implements a set of pre-processing functions over a handwritten Arabic characters, with contour analysis, to enter the contour vector to neural network to recognize it. The selection of this set of pre-processing algorithms was completed after hundreds of tests and validation. The feed forward neural network architecture was trained using many patterns regardless of the Arabic font style building a rigid recognition model. Because of the shortcomings in Arabic written databases or datasets, the testing was done by non-standard data set. The presented algorithm structure got recognition ratio about 97%.
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