Dubai, United Arab Emirates

Canadian University of Dubai

www.cud.ac.ae/
Dubai, United Arab Emirates

The Canadian University of Dubai, Also known as CUD for short, is one of the few Canadian universities in the United Arab Emirates. Located in the heart of the city on Sheikh Zayed Rd, CUD was founded in 2006. The Canadian University of Dubai is the best choice for anyone hoping to get a Canadian education or transfer to Canadian universities. Programs are offered within 5 different schools, with some programs such as in the School of Engineering, Applied Science and Technology leading to future opportunities as researchers within the University. The Canadian University of Dubai has an Office of Research Services that is networked with the top engineering research centers in Canada. Any program at the Canadian University of Dubai requires at least a 60% average, while individuals can apply with 50% and above to be placed on a wait list and possibly offered probationary acceptance. Wikipedia.


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Baadel S.,Canadian University of Dubai | Baadel S.,University of Huddersfield | Majeed A.,Birmingham City University | Kabene S.,Canadian University of Dubai
ACM International Conference Proceeding Series | Year: 2017

In today's technologically advanced world, it is crucial that instructors continuously stay up to date with the ever changing uses of technology in the classroom. Technologies transform the way instructors conduct their lectures and present course material, thereby fundamentally altering the way students learn. It is essential that some challenges such as training needs, funding, and dealing with perceptions are addressed and aligned with one another in order to facilitate technology adoption in institutions of higher education. These challenges are discussed in this paper in the context of gulf countries. © 2017 ACM.


Majeed A.,QA Higher Education | Baadel S.,Canadian University of Dubai | Baadel S.,University of Huddersfield | Williams M.L.,QA Higher Education
ACM International Conference Proceeding Series | Year: 2017

In the past few decades, learning through simulation and games has evolved in response to a continuous demand for new methods of teaching learners - thus helping academics to deliver their courses effectively. This research paper aims to identify the impact(s) of simulation-based virtual learning environment on postgraduate studentslearning abilities. To achieve the learning objectives; positive relations between simulations and students learning are gathered based on the systematic review of the literature. A successful use of simulations depends on four factors: the role of the instructor, integration in the course, the courses technical specifications and, the practical exposure as an integrative dynamic virtual learning environment. We utilized a quantitative methodology to compile data from online bloggers and analysed the content. The findings in this paper reveal that students from all groups, disaggregated by gender and ethnicity, showed significant learning gains after playing these challenging simulation games. We also present some recommendations that can help alleviate some of the constraints experienced by institutions of higher education that integrate simulations into classrooms. © 2017 ACM.


Thabtah F.,Canadian University of Dubai | Hammoud S.,Brunel University
Parallel Processing Letters | Year: 2013

Association rule is one of the primary tasks in data mining that discovers correlations among items in a transactional database. The majority of vertical and horizontal association rule mining algorithms have been developed to improve the frequent items discovery step which necessitates high demands on training time and memory usage particularly when the input database is very large. In this paper, we overcome the problem of mining very large data by proposing a new parallel Map-Reduce (MR) association rule mining technique called MR-ARM that uses a hybrid data transformation format to quickly finding frequent items and generating rules. The MR programming paradigm is becoming popular for large scale data intensive distributed applications due to its efficiency, simplicity and ease of use, and therefore the proposed algorithm develops a fast parallel distributed batch set intersection method for finding frequent items. Two implementations (Weka, Hadoop) of the proposed MR association rule algorithm have been developed and a number of experiments against small, medium and large data collections have been conducted. The ground bases of the comparisons are time required by the algorithm for: data initialisation, frequent items discovery, rule generation, etc. The results show that MR-ARM is very useful tool for mining association rules from large datasets in a distributed environment. © 2013 World Scientific Publishing Company.


Abdelhamid N.,Auckland Institute of Studies | Jabbar A.A.,Canadian University of Dubai | Thabtah F.,Nelson Marlborough Institute of Technology
Proceedings of the International Conference on Parallel Processing Workshops | Year: 2016

Association rule mining involves discovering concealed correlations among variables often from sales transactions to help managers in key business decision involving items shelving, sales and planning. In the last decade, association rule mining methods have been employed in deriving rules from classification dataset in different business domains. This has resulted in an emergence of new classification approach called Associative Classification (AC), which often produces higher predictive classifiers than classic approaches such as decision trees, greedy and rule induction. Nevertheless, AC suffers from noticeable challenges some of which have been inherited from association rules and others have been resulted from building the classifier phase. These challenges are not limited to the massive numbers of candidate ruleitems found, the very large classifiers derived, the inability to handle multi-label datasets, and the design of rule pruning, ranking and prediction procedures. This article highlights and critically analyzes common challenges faced by AC algorithms that are still sustained. Hence, it opens the door for interested researchers to further investigate these challenges hoping to enhance the overall performance of this approach and increase it applicability in research domains. © 2016 IEEE.


Mohamed E.E.,United Arab Emirates University | Mohamed E.E.,Canadian University of Dubai | Barka E.,United Arab Emirates University
International Journal of Communication Systems | Year: 2011

Multicast communications concern the transfer of data among multiple users. Multicast communications can be provided at the network layera-an example is IP multicasta-or at the application layer, also called overlay multicast. An important issue in multicast communications is to control how different usersa-senders, receivers, and delivery nodesa-access the transmitted data as well as the network resources. Many researchers have proposed solutions addressing access control in IP multicast. However, little attention has been paid to overlay multicast. In this paper, we investigate the access control issues in overlay multicast and present OMAC: a new solution to address these issues. OMAC provides access control for senders, receivers, and delivery nodes in overlay multicast. The proposed architecture, which is based on symmetric key cryptosystem, centralizes the authentication process in one server whereas it distributes the authorization process among the delivery nodes. Moreover, delivery nodes are utilized as a buffer zone between end systems and the authentication server, making it less exposed to malicious end systems. To evaluate our work, we have used simulation to compare the performance of OMAC against previous solutions. Results of the simulation show that OMAC outperforms previous multicast access control schemes. © 2010 John Wiley & Sons, Ltd.


Enyinda C.I.,Canadian University of Dubai | Mbah C.H.,American University of Nigeria
Thunderbird International Business Review | Year: 2016

The food industry plays a significant role in food supply. However, it is increasingly facing a significant number of risks to tackle. This article provides insight into sources and quantification of risk, which can restrict food operations and supply chain performance. Certainly, risks imposed by today's constantly changing global environment makes it imperative for food and agribusiness firms to develop purposeful proactive and predictive risk management for their global supply chains. We proposed the analytic hierarchy process (AHP) model to analyze sources of risks attached to the focal firm's global food operations and supply chain. The identified risks were from a review of relevant literature, expert opinions from the focal firm supply chain C-level executive, and consultants in the food industry. We grouped the identified risks into seven categories and discussed the risk mitigation strategies. We validated the proposed model using a case study involving a focal food and agribusiness firm with global presence. © 2016 Wiley Periodicals, Inc.


Abdelhamid N.,De Montfort University | Ayesh A.,De Montfort University | Thabtah F.,Canadian University of Dubai
Expert Systems with Applications | Year: 2014

Website phishing is considered one of the crucial security challenges for the online community due to the massive numbers of online transactions performed on a daily basis. Website phishing can be described as mimicking a trusted website to obtain sensitive information from online users such as usernames and passwords. Black lists, white lists and the utilisation of search methods are examples of solutions to minimise the risk of this problem. One intelligent approach based on data mining called Associative Classification (AC) seems a potential solution that may effectively detect phishing websites with high accuracy. According to experimental studies, AC often extracts classifiers containing simple "If-Then" rules with a high degree of predictive accuracy. In this paper, we investigate the problem of website phishing using a developed AC method called Multi-label Classifier based Associative Classification (MCAC) to seek its applicability to the phishing problem. We also want to identify features that distinguish phishing websites from legitimate ones. In addition, we survey intelligent approaches used to handle the phishing problem. Experimental results using real data collected from different sources show that AC particularly MCAC detects phishing websites with higher accuracy than other intelligent algorithms. Further, MCAC generates new hidden knowledge (rules) that other algorithms are unable to find and this has improved its classifiers predictive performance. © 2014 Elsevier Ltd. All rights reserved.


Abdelhamid N.,De Montfort University | Thabtah F.,Canadian University of Dubai
Journal of Information and Knowledge Management | Year: 2014

Associative classification (AC) is a promising data mining approach that integrates classification and association rule discovery to build classification models (classifiers). In the last decade, several AC algorithms have been proposed such as Classification based Association (CBA), Classification based on Predicted Association Rule (CPAR), Multi-class Classification using Association Rule (MCAR), Live and Let Live (L3) and others. These algorithms use different procedures for rule learning, rule sorting, rule pruning, classifier building and class allocation for test cases. This paper sheds the light and critically compares common AC algorithms with reference to the abovementioned procedures. Moreover, data representation formats in AC mining are discussed along with potential new research directions. © 2014 World Scientific Publishing Co.


Rabie T.,Canadian University of Dubai
Communications in Computer and Information Science | Year: 2012

This work describes a framework for image hiding that exploits spatial domain color properties of natural images combined with spectral properties of the Fourier magnitude and phase of these images. The theory is that as long as the Fourier phase of an image is maintained intact, the overall appearance of an image remains specious if the Fourier magnitude of the image is slightly modified. This hypothesis leads to a data hiding technique that promises high fidelity, double the capacity of previous methods, higher security, and robustness to tampering. Experimental results are presented throughout the paper which demonstrate the effectiveness of this novel approach. © Springer-Verlag Berlin Heidelberg 2012.


Hamam H.,University of Moncton | Hamam H.,Canadian University of Dubai
Applied Optics | Year: 2010

We propose an iterative method to optimize the phase profile of the initial field so that its intensity profile is observed periodically along the longitudinal (propagation) axis. The new method is inspired from the Gerchberg-Saxton technique, where the Fresnel transform is used, instead of the Fourier transform, for retrieving the phase profile of several light distributions (for example, 15 planes), instead of a Fourier pair of distributions. The additional challenge, with respect to the conventional Gerchberg-Saxton technique, is that the planes where constraints are applied number more than two. It turned out that when the number of periods increased, the spectrum of the obtained initial field converges toward including Montgomery's rings (self-imaging condition). © 2010 Optical Society of America.

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