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.
الدرجة العلمية Degree
التخصص Major
الجامعة Institution
تاريخ التخرج Date of Grad
دكتوراة PhD
الذكاء الإصطناعي / معالجة اللغات الطبيعيه Computer Science / NLP
جامعة ماليزيا الوطنية National University of Malaysia (UKM)
2008
PhD Thesis Title: Word Agreement and Ordering in English-Arabic Machine Translation: A Rule-Based Approach
ماجستير MSc
تكنولوجيا المعلومات Information Technology
جامعة العلوم الماليزية Science University of Malaysia
2003
بكالوريوس Bachelor
علوم الحاسوب /الرياضيات التطبيقية Computer Science / Applied Math
جامعة العلوم والتكنولوجيا الاردنية Jordan University of Science and Technology
2001
رئيس قسم علوم الحاسوب Head of Computer Science Department
Face recognition from non-identical face photos is a prominent area of research in pattern recognition and computer vision. Existing face recognition systems struggle with diverse changes like lighting conditions, expressions, and facial occlusions. This paper proposes a new Face Recognition (FR) approach that combines the Elastic Bunch Graph Matching (EBGM) approach with the greedy algorithm to automatically identify face landmarks. The proposed approach independently selects each optimal landmark of face image from different corresponding face images where the corresponding landmark of corresponding face image which achieves the best similarity is used rather than using one or at most two corresponding face images and computing the average between both. The locations of corresponding landmarks can be displaced to achieve maximum similarity with optimal landmarks. This proposed approach demonstrates improved recognition performance compared to contemporary face recognition methods. It effectively handles changing ratios of face parts and can recognize faces even with increasing occlusion sizes.
Predicting changes in air pollutant concentrations due to human and nature drivers are critical and challenging, particularly in areas with scant data inputs and high variability of parameters. This paper builds an Air Quality Index (AQI) model using Machine Learning algorithms and techniques. The paper employs Machine Learning Algorithms such as Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Random Forest (RF) and Logistic Regression. The model can predict the most pollutant factors from real readings published daily by the Jordan Ministry of Environment (MoEnv) for the period from January 2017 to April 2019. Jordan has prioritized air quality problems by establishing detection and monitoring stations in 12 positions across the country to measure Air Quality (AQ). Pollutant concentrations recorded by MoEnv use fully forewarn official organizations and individuals of daily air quality in the atmosphere over time and beneficially used by health and climate studies organizations. The study has detected the most contaminated sites and determined the pollutant concentrations. These estimates will indicate the most influenced pollutants and their behavior in the pollution process for further recommendations and actions to effects cardiopulmonary patients, environmental and climate researches, as well as to vulnerable ecosystems
Translation from/to Arabic has been widely studied recently. This study focuses on the translation of Arabic as a source language (SL) to Malay as a target language (TL). The proposed prototype will be conducted to map the SL ”meaning”with the most equivalent translation in the TL. In this paper, we will investigate Arabic-Malay Machine Translation features (i.e., syntactic, semantic, and morphology), our proposed method aims at building a robust lexical Machine Translation prototype namely (AMMT). The paper proposes an ongoing research for building a successful Arabic-Malay MT engine. Human judgment and bleu evaluation have been used for evaluation purposes, The result of the first experiment prove that our system(AMMT) has outperformed several well-regarded MT systems by an average of 98, while the second experiment shows an average score of 1-gram, 2-gram and 3-gram as 0.90, 0.87 and 0.88 respectively. This result could be considered as a contribution to the domain of natural language processing (NLP).
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.
Online education has positively influences student performance during universities lockdown nowadays due to COVID-19, in fact both educators and students have proven their ability to develop their teaching skills by emerging several technological tools. This article analyses the performance of two cohorts of students, the first cohort was taught traditionally while the other was taught online, the scope of this study is the students enrolled in programming languages at the Faculty of Computer Science and Information Technology at Jerash University, the study was carried out between the years 2017 - 2020. 1210 students have participated in the study. This study investigates a comparative study between different methods of delivering programming-languages courses over the 3-year period, the study also aims to shed light on the impact of traditional methods on delivering computer-programming courses and how it could be improved by emerging a SCORM learning multimedia and other learning modules, activities and resources. Result shows that online delivering of courses with the use of SCORM and other tools improves students’ scores and performance slightly, the article concludes that emerging technology to learning can improve the students' creativity, understanding and performance overall.
Text to speech (TTS) is a crucial tool needed in many domains, mainly for visually impaired users. The availability of TTS open sources improves access to computers and gives more valuable applications. eSpeak provides support for several languages. It is a tool that provides rules and phoneme files for more than 50 languages, besides, eSpeak is a light, fast, low memory consumption and used in multi-platforms. In this paper, we have explored the possibility to adapt the existing text to speech converters into Arabic language in eSpeak. We attempt to define new text to speech conversion rules, adapting existed phonemes and adding missing phonemes for Arabic under eSpeak. The contributions are quite significant; however, the software's developers will be able to integrated these enhancements within the new version, so that users who have problems with visual impairments or children with special needs will utilise this development of eSpeak. The availability of such support, open new fields to use Arabic in TTS environment, especially for blind persons.
Mixing languages together in text and in talking is a major feature in non-English languages in developing countries. This mixed grammar is also emerging in SMS, Facebook communication, searching the web and any future attempts also may increase the footprint of such a mixed language knowledge base. Traditional information retrieval (IR) and cross-language information retrieval (CLIR) systems do not exploit this natural human tendency as the underlying assumption is that user query is always monolingual. Accordingly, the majority of the text collections are either monolingual or multilingual. This paper explores the trends of mixed-language querying and writing. It also shows how the corpus is validated statistically and how an Arabic lexicon can be extracted using co-occurrence statistics. Results showed that the distribution of frequencies of words in the corpus is very skewed the vocabulary growth is a good fit. The results of how to handle mixed queries are also summarised.
This paper introduces a new enhanced Arabic stemming algorithm for solving the information retrieval problem, especially in medical documents. Our proposed algorithm is a light stemming algorithm for extracting stems and roots from the input data. One of the main challenges facing the light stemming algorithm is cutting off the input word, to extract the initial segments. When initiating the light stemmer with strong initial segments, the final extracting stems and roots will be more accurate. Therefore, a new enhanced segmentation based on deploying the Direct Acyclic Graph (DAG) model is utilized. In addition to extracting the powerful initial segments, the main two procedures (i.e., stems and roots extraction), should be also reinforced with more efficient operators to improve the final outputs. To validate the proposed enhanced stemmer, four data sets are used. The achieved stems and roots resulted from our proposed light stemmer are compared with the results obtained from five other well-known Arabic light stemmers using the same data sets. This evaluation process proved that the proposed enhanced stemmer outperformed other comparative stemmers.
مهارات لغات البرمجة عالية المستوى والسكريبتات
مهارات تقنية لتطوير المواقع والتطبيقات الذكيّة
All Rights Reseved © 2023 - Developed by: Prof. Mohammed M. Abu Shquier Editor: Ali Mayyas
