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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.

Thabtah F.,Canadian University of Dubai | Hammoud S.,Brunel University | Abdel-Jaber H.,Arab Open University
Parallel Processing Letters | Year: 2015

Associative classification (AC) is a research topic that integrates association rules with classification in data mining to build classifiers. After dissemination of the Classification-based Association Rule algorithm (CBA), the majority of its successors have been developed to improve either CBA's prediction accuracy or the search for frequent ruleitems in the rule discovery step. Both of these steps require high demands in processing time and memory especially in cases of large training data sets or a low minimum support threshold value. In this paper, we overcome the problem of mining large training data sets by proposing a new learning method that repeatedly transforms data between line and item spaces to quickly discover frequent ruleitems, generate rules, subsequently rank and prune rules. This new learning method has been implemented in a parallel Map-Reduce (MR) algorithm called MRMCAR which can be considered the first parallel AC algorithm in the literature. The new learning method can be utilised in the different steps within any AC or association rule mining algorithms which scales well if contrasted with current horizontal or vertical methods. Two versions of the learning method (Weka, Hadoop) have been implemented and a number of experiments against different data sets have been conducted. The ground bases of the comparisons are classification accuracy and time required by the algorithm for data initialization, frequent ruleitems discovery, rule generation and rule pruning. The results reveal that MRMCAR is superior to both current AC mining algorithms and rule based classification algorithms in improving the classification performance with respect to accuracy. © 2015 World Scientific Publishing Company. Source

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. Source

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. Source

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. Source

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. Source

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