Research Title: Autosomal recessive non-syndromic Keratoconus: homozygous frameshift variant in the candidate novel gene GALNT14

Author: Tawfiq Froukh, Published Year: 2018

68th Annual Meeting of the American Society of Human Genetics, San Diego, California, USA

Faculty: Science

Abstract: Background: Keratoconus (KC) is usually bilateral, noninflammatory progressive corneal ectasia in which the cornea becomes progressively thin and conical. Despite the strong evidence of genetic contribution in KC, the etiology of KC is not understood in most cases. Methods: In this study, we used whole-exome sequencing to identify the genetic cause of KC in two sibs in a consanguineous family. The Homozygous frameshift variant NM_001253826.1:c.60delC;p.Leu21Cysfs*6 was identified in the gene Nacetylgalactosaminyltransferase 14 (GALNT14). The variant does not exist in all public databases neither in our internal exome database. Moreover, no database harbours homozygous loss of function variants in the candidate gene. GALNT14 catalyses the initial reaction in O-linked oligosaccharide biosynthesis, the transfer of an N-acetyl-Dgalactosamine residue to a serine or threonine residue on target proteins especially Mucins. Conclusion: As alterations of mucin’s glycosylation are linked to a number of eye diseases, we demonstrate in this study an association between the truncated protein GALNT14 and KC.

Keywords: NGS, genome, ocular, epithelial, dry-eye, keratoconus.

Research Title: Genetic causes of intellectual disability in 102 consanguineous families from Jordan

Author: Tawfiq Froukh, Published Year: 2017

67th Annual Meeting of the American Society of Human Genetics, Orlando, USA

Faculty: Science

Abstract: We recruited 103 families from Jordan with neurodevelopmental disorders (NDD) and patterns of inheritance mostly suggestive of autosomal recessive inheritance. In each family, we investigated at least one affected individual using exome sequencing and an in-house diagnostic variant interpretation pipeline including a search for copy number variation. This approach led us to identify the likely molecular defect in established disease genes in 37 families. We could identify 25 pathogenic nonsense and 11 missense variants as well as 3 pathogenic CNVs and one repeat expansion. Notably, 11 of the disease-causal variants occurred de novo. In addition, we prioritized a homozygous frameshift variant in PUS3 in two sisters with intellectual disability. To our knowledge, PUS3 has been postulated only recently as a candidate disease gene for intellectual disability in a single family with three affected siblings. Our findings provide additional evidence to establish loss of PUS3 function as a cause of intellectual disability.

Keywords: exome sequencing, neurodevelopmental disorder, PUS3, Jordan

Research Title: Chromosomal translocation, cnv deletion and missense mutations associated with intellectual disability in consanguineous families from Jordan

Author: Tawfiq Froukh, Published Year: 2016

66th Annual Meeting of the American Society of Human Genetics, Vancouver, Canada

Faculty: Science

Abstract: Intellectual disability (ID), occurs in approximately 1 to 3% of the population and tends to be higher in low-income countries and in inbred communities. Despite the high rates of consanguineous marriages and the likely enrichment for recessive forms of ID, the genetic bases of ID in Jordan are largely unstudied. In this study, whole exome sequencing (WES) and homozygosity mapping were used to identify the genetic causes of ID in ten families from Jordan. The studied families are characterized by consanguineous marriage and having one or more progeny with ID. Likely disease-causing missense mutations were identified in eight families; four families are due to mutations in genes previously implicated with ID and the other four families are due to mutations in genes that are not previously implicated with ID. The novel genes include: BSN (Protein Basson), PTCHD2 (Protein dispatched homolog 3), DHRS3 (Short-chain dehydrogenase/reductase 3), and LGI3 (Leucine-rich repeat LGI family member 3). In addition, copy number variant (CNV) deletion and/or duplication were identified in 2 families; one family with 3.5 mega base (Mb) deletion on chromosome17 previously implicated with Smith Magenis Syndrome, and the other family with a novel combination of deletion and duplication in chromosomes 5 and 11. In this pilot study, four genes and one CNV deletion/duplication are identified for the first time in association with ID. The finding of this study further demonstrates the power of WES and homozygosity mapping for clinical diagnostics of ID in consanguineous families in small populations.

Keywords: epilepsy; heterozygous; IQ; mental retardation; next generation sequencing

Research Title: A dynamic rule-induction method for classification in data mining

Author: Issa Qabaja, Published Year: 2015

Journal of Management Analytics, Volume 2

Faculty: Information Technology

Abstract: Rule induction (RI) produces classifiers containing simple yet effective ‘If–Then' rules for decision makers. RI algorithms normally based on PRISM suffer from a few drawbacks mainly related to rule pruning and rule-sharing items (attribute values) in the training data instances. In response to the above two issues, a new dynamic rule induction (DRI) method is proposed. Whenever a rule is produced and its related training data instances are discarded, DRI updates the frequency of attribute values that are used to make the next in-line rule to reflect the data deletion. Therefore, the attribute value frequencies are dynamically adjusted each time a rule is generated rather statically as in PRISM. This enables DRI to generate near perfect rules and realistic classifiers. Experimental results using different University of California Irvine data sets show competitive performance in regards to error rate and classifier size of DRI when compared to other RI algorithms.

Keywords: data mining, classification rules, rule induction, expected accuracy.

Research Title: Constrained dynamic rule induction learning

Author: Issa Qabaja, Published Year: 2016

Expert Systems with Applications, Volume 63

Faculty: Information Technology

Abstract: One of the known classification approaches in data mining is rule induction (RI). RI algorithms such as PRISM usually produce If-Then classifiers, which have a comparable predictive performance to other traditional classification approaches such as decision trees and associative classification. Hence, these classifiers are favourable for carrying out decisions by users and therefore they can be utilised as decision making tools. Nevertheless, RI methods, including PRISM and its successors, suffer from a number of drawbacks primarily the large number of rules derived. This can be a burden especially when the input data is largely dimensional. Therefore, pruning unnecessary rules becomes essential for the success of this type of classifiers. This article proposes a new RI algorithm that reduces the search space for candidate rules by early pruning any irrelevant items during the process of building the classifier. Whenever a rule is generated, our algorithm updates the candidate items frequency to reflect the discarded data examples associated with the rules derived. This makes items frequency dynamic rather static and ensures that irrelevant rules are deleted in preliminary stages when they don't hold enough data representation. The major benefit will be a concise set of decision making rules that are easy to understand and controlled by the decision maker. The proposed algorithm has been implemented in WEKA (Waikato Environment for Knowledge Analysis) environment and hence it can now be utilised by different types of users such as managers, researchers, students and others. Experimental results using real data from the security domain as well as sixteen classification datasets from University of California Irvine (UCI) repository reveal that the proposed algorithm is competitive in regards to classification accuracy when compared to known RI algorithms. Moreover, the classifiers produced by our algorithm are smaller in size which increase their possible use in practical applications.

Keywords: ClassificationData mfiningPredictionPRISMRule inductionOnline security.

Research Title: A recent review of conventional vs. automated cybersecurity anti-phishing techniques

Author: Issa Qabaja, Published Year: 2018

Computer Science Review, Volume 29

Faculty: Information Technology

Abstract: In the era of electronic and mobile commerce, massive numbers of financial transactions are conducted online on daily basis, which created potential fraudulent opportunities. A common fraudulent activity that involves creating a replica of a trustful website to deceive users and illegally obtain their credentials is website phishing. Website phishing is a serious online fraud, costing banks, online users, governments, and other organisations severe financial damages. One conventional approach to combat phishing is to raise awareness and educate novice users on the different tactics utilised by phishers by conducting periodic training or workshops. However, this approach has been criticised of being not cost effective as phishing tactics are constantly changing besides it may require high operational cost. Another anti-phishing approach is to legislate or amend existing cyber security laws that persecute online fraudsters without minimising its severity. A more promising anti-phishing approach is to prevent phishing attacks using intelligent machine learning (ML) technology. Using this technology, a classification system is integrated in the browser in which it will detect phishing activities and communicate these with the end user. This paper reviews and critically analyses legal, training, educational and intelligent anti-phishing approaches. More importantly, ways to combat phishing by intelligent and conventional are highlighted, besides revealing these approaches differences, similarities and positive and negative aspects from the user and performance prospective. Different stakeholders such as computer security experts, researchers in web security as well as business owners may likely benefit from this review on website phishing.

Keywords: Classification, Computer Security, Phishing, Machine Learning, Web Security, Security Awareness.

Research Title: An Experimental Study for Assessing Email Classification Attributes Using Feature Selection Methods

Author: Issa Qabaja, Published Year: 2014

3rd International Conference on Advanced Computer Science Applications and Technologies, Amman, Jordan

Faculty: Information Technology

Abstract: Email phishing classification is one of the vital problems in the online security research domain that have attracted several scholars due to its impact on the users payments performed daily online. One aspect to reach a good performance by the detection algorithms in the email phishing problem is to identify the minimal set of features that significantly have an impact on raising the phishing detection rate. This paper investigate three known feature selection methods named Information Gain (IG), Chi-square and Correlation Features Set (CFS) on the email phishing problem to separate high influential features from low influential ones in phishing detection. We measure the degree of influentially by applying four data mining algorithms on a large set of features. We compare the accuracy of these algorithms on the complete features set before feature selection has been applied and after feature selection has been applied. After conducting experiments, the results show 12 common significant features have been chosen among the considered features by the feature selection methods. Further, the average detection accuracy derived by the data mining algorithms on the reduced 12-features set was very slight affected when compared with the one derived from the 47-features set.

Keywords: Data mining, Email Classification,Phishing, Online Security.

Research Title: A Classification Rules Mining Method based on Dynamic Rules' Frequency

Author: Issa Qabaja, Published Year: 2015

IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), Marrakech, Morocco

Faculty: Information Technology

Abstract: Rule based classification or rule induction (RI) in data mining is an approach that normally generates classifiers containing simple yet effective rules. Most RI algorithms suffer from few drawbacks mainly related to rule pruning and rules sharing training data instances. In response to the above two issues a new dynamic rule induction (DRI) method is proposed that utilises two thresholds to minimise the search space of items. Whenever a rule is generated, DRI algorithm ensures that all candidate items' frequencies are updated to reflect the deletion of the rule’s training data instances. Therefore, the remaining candidate items waiting to be added to other rules have dynamic frequencies rather static. This enables DRI to generate not only rules with accuracy 100% but rules with high accuracy as well. Experimental tests using a number of UCI data sets have been conducted using a number of RI algorithms. The results clearly show competitive performance in regards to classification accuracy and classifier size of DRI when compared to other RI algorithms.

Keywords: Classification Rules, Data Mining, Rule Induction, Dynamic, Experimental tests.

Research Title: Integration of location-based information into mobile learning management system to verify scientific formulas in informal learning environment

Author: Ali Ahmad Alawneh, Published Year: 2017

International Journal of Intelligent Enterprise, 4(1/2)

Faculty: Business

Abstract: Scientific formulas govern our life while moving from one location to another. Hence, this study investigates into the development of an adaptive mobile learning model that extends traditional learning systems that address content customisation issue, to include formulas verification feature with help of location-based information in informal learning environment. The main contribution of this novel model is to provide the mobile learner with a virtual-teacher anytime-anywhere to verify or even solve course-related tasks, especially in scientific courses such as math or physics formulas. The virtual-teacher is addressed in this study in the form of intermediate object. This object is built by merging data from three different resources: mobile learner profile, formulas profile, and location-based information. As a result, mobile learner will receive the location-aware personalised outcomes in form of answers that correspond to the pre-defined tasks. The instructors can also benefit from this adaptive model to demonstrate their lessons-related formulas, outside classrooms.

Keywords: formulas verification, location-based information, mobile learner, mobile learning management system, tasks solving

Research Title: The effects of e-banking quality on customers' trust-satisfaction and commitment: a field study on users of Jordanian banks

Author: Ali Ahmad Alawneh, Published Year: 2017

International Journal of Intelligent Enterprise, 4(1/2)

Faculty: Business

Abstract: Due to the complexity and the high competitiveness of banks' business environments in today's knowledge economy, keeping banks' customers in-hand and increasing their profitability are getting harder to be achieved. In the process of doing so, a superior banking service quality has to be delivered efficiently and quickly through advanced information and communication technologies (ICTs). This study assessed the experimentation of Alawneh et al. (2013) scale for measuring e-banking service quality in Jordan. In addition, it attempted to examine the casual relationships among the scale's five constructs (reliability, efficiency, security/privacy, responsiveness and communication) and the trust, satisfaction and commitment constructs. The survey data based on the aforementioned constructs were gathered from 400 e-banking users of Jordanian banks located in Irbid City of Jordan. The results of hypotheses testing and regression analysis have been discussed. Finally, recommendations of these results to banks' managers, practitioners and decision makers and proposed further works to scholars were presented.

Keywords: e-banking, service quality, trust, satisfaction, commitment, measurement scale, Jordan