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 تخصص علم الحاسوب من جامعة لنكلن النيوزلنديه 2013
ماجستير في في نظم المعلومات الحاسوييه من جامعه اليرموك 2005
بكالوريوس في علم الحاسوب جامعة مؤتة 2001
Doctor of Philosophy in Computer Science
Lincoln university, New Zealand
Master in Computer Information Systems (September 2005)
Yarmuk University, Jordan
Bachelor in Computer Science
Mu’tah University
Jerash University /Jordan
Assistant professor in Computer Science 2013-now
Member at Centre for Advanced Computational Solutions/New Zealand
Researcher 2011-2017
Al Balqa Applied University/Jordan
Lecturer in Computer Information Technology Department 2002-2009
During this period I was a lecturer for the following subjects
Artificial intelligence
System analysis and design
Data structure
Data mining
Algorithm
Essentials of bioinformatics
Database (SQL) MATLAB
Al-Huson Technical College, Al Balqa Applied University
2005-2006
Head of Information Technology Department
Currently, complex socio-ecological problems have increasingly prevailed with uncertainty that often dominates these domains. In order to better represent these problems, there is an urgent need to engage a wide range of different stakeholders' perspectives, regardless of their levels of expertise and knowledge. Then, these perspectives should be combined in an appropriate manner for a comprehensive and reasonable problem representation. Fuzzy cognitive map (FCM) has proven to be powerful and useful as a soft computing approach in addressing and representing such problem domains. By the FCM approach, the relevant stakeholders can represent their perspectives in the form of FCM system. Normally, relevant stakeholders have different levels of knowledge, and hence produce different representations (FCMs). Therefore, these FCMs should be weighted appropriately before the combination process. This paper uses fuzzy c-means clustering technique to assign different weights for different FCMs according to their importance in representing the problem. First, fuzzy c-means is used to compute the membership values of belonging of FCMs to the selected clusters based on the FCMs similarities that show how convergent and consistent they are. According to these membership values, the importance clusters' values are calculated, in which a cluster with a high membership value from all FCMs is the cluster with the high importance value, and vice versa. Next, the importance values for FCMs are derived from the importance values of the clusters by looking at the amount of contributions of FCMs memberships to the clusters. Finally, FCMs importance values are used to assign weight values to these FCMs, which are used when they are combined. The suitability of the proposed method is investigated using a real dataset that includes an appropriate number of FCMs collected from different stakeholders
Fuzzy cognitive map (FCM) is a qualitative soft computing approach addresses uncertain human perceptions of diverse real-world problems. The map depicts the problem in the form of problem nodes and cause-effect relationships among them. Complex problems often produce complex maps that may be difficult to understand or predict, and therefore, maps need to be simplified. Previous studies used subjectively simplification/condensation processes by grouping similar variables into one variable in a qualitative manner. This paper proposes a quantitative method for simplifying FCM. It uses the spectral clustering quantitative technique to classify/group related variables into new clusters without human intervention. Initially, improvements were added to this clustering technique to properly handle FCM matrix data. Then, the proposed method was examined by an application dataset to validate its appropriateness in FCM simplification. The results showed that the method successfully classified the dataset into meaningful clusters.
Cancer produces complex cellular changes. Microarrays have become crucial to identifying genes involved in causing these changes; however, microarray data analysis is challenged by the high-dimensionality of data compared to the number of samples. This has contributed to inconsistent cancer biomarkers from various gene expression studies. Also, identification of crucial genes in cancer can be expedited through expression profiling of peripheral blood cells. We introduce a novel feature selection method for microarrays involving a two-step filtering process to select a minimum set of genes with greater consistency and relevance, and demonstrate that the selected gene set considerably enhances the diagnostic accuracy of cancer. The preliminary filtering (Bi-biological filter) involves building gene coexpression networks for cancer and healthy conditions using a topological overlap matrix (TOM) and finding cancer specific gene clusters using Spectral Clustering (SC). This is followed by a filtering step to extract a much-reduced set of crucial genes using best first search with support vector machine (BFS-SVM). Finally, artificial neural networks, SVM, and K-nearest neighbor classifiers are used to assess the predictive power of the selected genes as well as to select the most effective diagnostic system. The approach was applied to peripheral blood profiling for breast cancer where Bi-biological filter selected 415 biologically consistent genes, from which BFS-SVM extracted 13 highly cancer specific genes for breast cancer identification. ANN was the superior classifier with 93.2% classification accuracy, a 14% improvement over the study from which data were obtained for this study (Aaroe et al., Breast Cancer Res 12:R7, 2010).
In this study, we present an investigation of comparing the capability of a big bang-big crunch metaheuristic (BBBC) for managing operational problems including combinatorial optimization problems. The BBBC is a product of the evolution theory of the universe in physics and astronomy. Two main phases of BBBC are the big bang and the big crunch. The big bang phase involves the creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into the BBBC’s effectiveness in assignment and scheduling problems. Where it was enhanced by incorporating an elite pool of diverse and high quality solutions; a simple descent heuristic as a local search method; implicit recombination; Euclidean distance; dynamic population size; and elitism strategies. Those strategies provide a balanced search of diverse and good quality population. The investigation is conducted by comparing the proposed BBBC with similar metaheuristics. The BBBC is tested on three different classes of combinatorial optimization problems; namely, quadratic assignment, bin packing, and job shop scheduling problems. Where the incorporated strategies have a greater impact on the BBBC's performance. Experiments showed that the BBBC maintains a good balance between diversity and quality which produces high-quality solutions, and outperforms other identical metaheuristics (e.g. swarm intelligence and evolutionary algorithms) reported in the literature.
Acne is common among young individuals. People with dark skin have a higher risk for developing pigmentary complications. Inflammation is an important factor in post-acne hyperpigmentation however other factors are also involved in developing this complication however these factors are not well studied. The aim of this study is to identify risk factors involved in post-acne hyperpigmentation. Clinical data related to acne, acne- related hyperpigmentation were collected. Data was analyzed for risk factors associated with acne pigmentation. Artificial neural network was used as predictive disease classifier for the outcome of pigmentation. Majority of patients in this study (339 patients) had dark skin phototypes (3 and 4). Post- acne hyperpigmentation was seen in more than 80% of patients. Females, darker skin color, severe acne, facial sites, and excessive sunlight exposure, squeezing or scratching lesions are important risk factors for post-acne hyperpigmentation. Post-acne hyperpigmentation is multifactorial. Several factors implicated in PAH are modifiable by adequate patient education (lesion trauma, excessive sunlight exposure). The use of ANN was helpful in predicting appearance of post-acne hyperpigmentation based on identified risk factors.
To investigate the impact of Ventriculo-Peritoneal Shunt (VPS) on the Health-Related Quality of Life (HRQOL) of children with the infantile hydrocephalus who underwent their first shunt insertion in the first year of life. To compare the outcome of health domains according to sex, follow-up period, etiology and shunt valve type (fixed versus programmable pressure). Methods: 102 children ≤1 years old at the time of new-onset hydrocephalus and shunt insertion. Age-appropriate PedsQL 4.0 versions were completed by the parents or caregivers with the assistance of single neurosurgery resident. Patients were divided into subgroups according to etiology; neural tube defect associated hydrocephalus (NTD-H), intra-ventricular hemorrhage associated with infantile hydrocephalus (IVH-H) and according to the shunt valve type; fixed versus programmable. Statistical analyses were performed using SPSS, IBM version 20. PedsQL 4.0 was presented using mean and standard deviations. Results: A decreasing social domain score at 1-3 years follow up (n=61) compared to 1 year follow up (n=41) was observed. The two groups did not differ significantly in sex distribution. The mean cognitive score was significantly lower in patients with IVH-H of prematurity compared to NTD-H. Better physical and cognitive domains in programmable shunts were compared to fixed pressure type. Conclusion: IVH-H associated with worse cognitive function possibly due to associated brain damage was reported. With long-term follow-up, social function decline probably due to the patients’ awareness of their disability was observed. Programmable shunt valve is recommended over fixed type due to the improvement in physical and cognitive functions. Sex of the patients did not affect the outcome.
E-learning is one of the growing areas especially in the higher education. There are several advantages for using e-leaning in the student performance. In third developing countries such as Jordan, important steps were taken for adopting the e-learning system. This is done by providing students with technological and communicational skills as well as to making students more adaptive to the technology of con-temporary societies. Several studies have been analysis the effect of the e-learning on the student performance and found that there is a tangible enhancement on the student performance and is considered as key element that positively affect the student motivations. Online courses at Jerash university like; computer skills accessed by students through electronic gate (Moodle).This study used SPSS to analysis the performance of 63 student (32 e-learning and 31 traditional learning).the study found that there are statistically significant differences between the two group in favor of experimental group.
Musculoskeletal pain is a heterogeneous condition with multiple risk factors, primary sources that can affect treatment and rehabilitation outcome. In this paper, we developed a prediction model for therapeutic subgrouping of musculoskeletal pain using ANN. A dataset of 27 patients with neck/shoulder pain was used. Patients received a single injection (0.2 ml) of 0.5% lidocaine at the trigger points. ANN model was used for predicting treatment outcome based on influential pre-treatment variables as inputs. Leave one out cross validation (LOOCV) method was used for validation. The strength of each predicting variable was tested using multilayer feed forward neural network with back propagation (MFFNN) and LOOCV. Then, the MFFNN prediction model was developed and designed based on the selected variables. Post-treatment endpoint follow-up (fourth week VAS) was selected as a good indicator of treatment outcome. Serum vitamin D and ferritin were relatively better predictors of treatment response in the current patient group. ANN obtained 85% prediction accuracy.
Digital storytelling plays a vital role in passing information via attractive and interactive digital media. Therefore, there are several methods that help in assessing the quality of the Digital Stories (DSs) and classifying them into successful or failed stories, rubrics can be a solution. This paper introduces an assessment method for numbers of DSs that have been collected (for a specific year, which is 2014) from the social media site YouTube to determine if they are interesting or boring. The aim of this study is to analyse the impact of some aspects upon the developed DSs, such as how much the developer followed the story regulations (e.g. the story seven elements), and the developer/narrator gender effect. Furthermore, to discover how much such aspects are effective. All this will be reflected in the number of every DS’s viewers. As results, three main elements have been revealed and discussed. Keywords: digital storytelling; digital stories’ classification; evaluation methods; rubrics; social media; data analysis.
Aim: Despite the high prevalence of chronic neck pain, there is limited consensus about the primary etiology, risk factors, diagnostic criteria and therapeutic outcome. Here, we aimed to determine if Ferritin and Vitamin D are modifiable risk factors with chronic neck pain using slandered statistics and artificial intelligence neural network (ANN). Methods: Fifty-four patients with chronic neck pain treated between February 2016 and August 2016 in King Abdullah University Hospital and 54 patients age matched controls undergoing outpatient or minor procedures were enrolled. Patients and control demographic parameters, height, weight and single measurement of serum vitamin D, Vitamin B12, ferritin, calcium, phosphorus, zinc were obtained. An ANN prediction model was developed. Results: The statistical analysis reveals that patients with chronic neck pain have significantly lower serum Vitamin D and Ferritin (p-value <.05). 90% of patients with chronic neck pain were females. Multilayer Feed Forward Neural Network with Back Propagation(MFFNN) prediction model were developed and designed based on vitamin D and ferritin as input variables and CNP as output. The ANN model output results show that, 92 out of 108 samples were correctly classified with 85% classification accuracy. Conclusions: Although Iron and vitamin D deficiency cannot be isolated as the sole risk factors of chronic neck pain, they should be considered as two modifiable risk. The high prevalence of chronic neck pain, hypovitaminosis D and low ferritin amongst women is of concern. Bioinformatics predictions with artificial neural network can be of future benefit in classification and prediction models for chronic neck pain. We hope this initial work will encourage a future larger cohort study addressing vitamin D and iron correction as modifiable factors and the application of artificial intelligence models in clinical practice.
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