Time filter

Source Type

Beirut, Lebanon

Global University is an educational institution established in 1992.Global University currently comprises three faculties: Faculty of Administrative science Faculty of Health science Faculty of Literature and Humanities Wikipedia.

Hashim N.,Global University
Local Environment | Year: 2015

The Urban Farming Movement is relatively young in Louisville, Kentucky but it seems to be off to a good start. Being in its earliest stages, assessments of this effort must be made cautiously, even tentatively. Examining a movement that is in progress requires a study of the state's land use and segregation policies, both of which have been dynamic in Kentucky. Further, this study is also relevant as there is a growing convergence of national and regional attitudes towards American obesity, while there are competing notions of food preference and food choice. All of which are influenced by racial politics, and there is a deep concern regarding food security. Considering all of these, it is necessary to examine the Government's efforts to address obesity/food imbalance in a variety of settings, especially where urban farming is expanding, such as Chicago and Detroit. In New York, roof-top gardens dot the landscape and the urban farm movement is largely a “success”. So, city leaders have chosen to address another health issue – trans fat. Rather than joining the food choice/preference debate by banning fast-food restaurants, city leaders have chosen to enact a trans-fat ban. Unpopular at first, other cities and states have followed, and the Federal Government will mandate a trans-fat ban this year. In Louisville, Chicago and Detroit, where the trans-fat bans did not succeed, the urban farm movement may be the primary means of addressing malnutrition, in the form of obesity. © 2014 Taylor & Francis.

Rasheed F.,University of Calgary | Alshalalfa M.,University of Calgary | Alhajj R.,Global University
IEEE Transactions on Knowledge and Data Engineering | Year: 2011

Periodic pattern mining or periodicity detection has a number of applications, such as prediction, forecasting, detection of unusual activities, etc. The problem is not trivial because the data to be analyzed are mostly noisy and different periodicity types (namely symbol, sequence, and segment) are to be investigated. Accordingly, we argue that there is a need for a comprehensive approach capable of analyzing the whole time series or in a subsection of it to effectively handle different types of noise (to a certain degree) and at the same time is able to detect different types of periodic patterns; combining these under one umbrella is by itself a challenge. In this paper, we present an algorithm which can detect symbol, sequence (partial), and segment (full cycle) periodicity in time series. The algorithm uses suffix tree as the underlying data structure; this allows us to design the algorithm such that its worst-case complexity is O(k. n2), where k is the maximum length of periodic pattern and n is the length of the analyzed portion (whole or subsection) of the time series. The algorithm is noise resilient; it has been successfully demonstrated to work with replacement, insertion, deletion, or a mixture of these types of noise. We have tested the proposed algorithm on both synthetic and real data from different domains, including protein sequences. The conducted comparative study demonstrate the applicability and effectiveness of the proposed algorithm; it is generally more time-efficient and noise-resilient than existing algorithms. © 2006 IEEE.

Kaya M.,Firat University | Alhajj R.,University of Calgary | Alhajj R.,Global University
Information Sciences | Year: 2014

A common task in many applications is to find people who are knowledgeable about a given topic, topics which are suitable for a given author or venue, and venues which are attractive for a given author or topic. This problem has many real-world applications and has recently attracted considerable attention. However, the existing methods are not very efficient in providing flexibility for multi-dimensional and multi-level view from different perspectives. In this paper, we first propose and develop three different academic networks with a novel data cube based modeling method, and then we perform automated decision processes on these networks. As the first step of the study, we integrate DBLP and CiteSeerX by employing a simple technique called canopy clustering. After the integration of the databases, the modeling stage of the academic networks is performed. In this study, each node as apart from the studies described in the literature is represented by a corresponding data cube with respect to the kind of the network being considered. In order to appropriately analyze the data cube, the OLAP technology is utilized. As the next step of the study, our aim is to automatically find relevant persons, topics and venues from each network. However, it is not an easy task to extract knowledge with low running time and high accuracy from such very huge information networks. In order to overcome this problem, a multi-agent based algorithm is proposed. We evaluate our method with the author network using a benchmark dataset of how well the expertise of the proposed experts matches a given query topic. Our experiments covering other networks show that the proposed strategies are all effective to improve the retrieval accuracy. © 2013 Elsevier Inc. All rights reserved.

Hsu H.Y.,National Central University | Tsou H.-T.,Global University
International Journal of Information Management | Year: 2011

Blogs have recently become an influential medium and have demonstrated enormous marketing power. Consumers can freely conduct ongoing information searches through this new channel. However, the credibility of blogs plays an important role in creating opportunities for positive customer experiences that can shape consumers' product/service purchase intentions and decisions. In light of this observation, this study proposes a theoretical framework that delineates the relationship among information credibility, customer experiences, and purchase intention in the blog environment. Data collected from 468 subjects in specific corporate blogs provide support for the proposed model using partial least squares (PLS). The results indicate that information credibility is critical for facilitating customer experiences, which, in turn, is necessary to enhance purchase intention. Additionally, greater involvement with blog significantly increases the effect of customer experiences on purchase intention. The detailed theoretical and managerial implications are presented. © 2011 Elsevier Ltd. All rights reserved.

Zhang M.,University of Calgary | Alhajj R.,University of Calgary | Alhajj R.,Global University
Knowledge and Information Systems | Year: 2010

Similarity search (e.g., k-nearest neighbor search) in high-dimensional metric space is the key operation in many applications, such as multimedia databases, image retrieval and object recognition, among others. The high dimensionality and the huge size of the data set require an index structure to facilitate the search. State-of-the-art index structures are built by partitioning the data set based on distances to certain reference point(s). Using the index, search is confined to a small number of partitions. However, these methods either ignore the property of the data distribution (e.g., VP-tree and its variants) or produce non-disjoint partitions (e.g., M-tree and its variants, DBM-tree); these greatly affect the search efficiency. In this paper, we study the effectiveness of a new index structure, called Nested-Approximate-eQuivalence-class tree (NAQ-tree), which overcomes the above disadvantages. NAQ-tree is constructed by recursively dividing the data set into nested approximate equivalence classes. The conducted analysis and the reported comparative test results demonstrate the effectiveness of NAQ-tree in significantly improving the search efficiency. © Springer-Verlag London Limited 2009.

Discover hidden collaborations