Riyadh, Saudi Arabia
Riyadh, Saudi Arabia

Al Yamamah University is a university based in Riyadh, Saudi Arabia and recognized by the Ministry of Higher Education. It was founded by Al Khudair family, who in 1957 established Al-Tarbiyah Al-Namouthajiyah Schools , the first private schools in Riyadh.The university comprises the Colleges of Business Administration and Computing and Information Systems as well as the Deanship of Continuing Education and Community Service, and admits male and female students. It maintains a number of collaborative relationships with other academic institutions for the purpose of designing its curricula, enriching its programs, and providing its students and faculty members with opportunities for exchanging information and experience.Students at the university are eligible for financial support from the Ministry of Higher Education in Saudi Arabia. Wikipedia.


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Jabbar S.,Bahria University | Jabbar S.,COMSATS Institute of Information Technology | Minhas A.A.,Bahria University | Minhas A.A.,Al Yamamah University | And 2 more authors.
Journal of Supercomputing | Year: 2014

Cluster-based network is a proven architecture for energy-aware routing, but more attention is required to ameliorate the energy consumption aspect of its cluster designing process. In this research work, we introduce a novel design of clustered network architecture. The proposed design technique is innovative in its idea. The general trend in this scene is either centralized decision at base station for cluster head selection and its members or distributed decision by exchanging information between neighboring nodes until the cluster head and its members are selected. Both the techniques drastically create mess in energy consumption due to too much broadcasting, especially in large networks as well as message exchange until some final decision is made. Our novel layer-based hybrid algorithm for cluster head and cluster member selection comes up to novel communication architecture. Since its substantial constituent is cluster designing, we named it Multilayer Cluster Designing Algorithm (MCDA). The proposed design not only has effect on lessening blind broadcasting, but also on decreasing the message exchange in a passionate way. It also encapsulates the beauty of efficient centralized decision making for cluster designing and energy-aware distributed cluster head selection and cluster member allocation process. Comprehensive experimentations have been performed on the comparative analysis of MCDA with state-of-the-art centralized and distributed cluster designing approaches present in published literature. Calculation of energy consumption in various operational parametric values, number of clusters designed and the number of packets broadcasted during cluster designing are the main performance evaluation parameters. It has been found that MCDA outperforms compared to its three competing algorithms with respect to the aforementioned parameters due to its multilayered synergistic mating approach. © 2014 Springer Science+Business Media New York.


Saba T.,Prince Sultan University | Almazyad A.S.,King Saud University | Rehman A.,Al Yamamah University
Neural Computing and Applications | Year: 2015

Arabic script classification is a complex area of research in the field of computer vision. The issue of offline Arabic script classification has been a concern of many researchers interest currently as it is assumed that online Arabic script recognition is comparatively simple and significant achievements have been attained. Numerous researchers deal with these issues evolved in pre-processing and post-processing techniques of Arabic script and presented various approaches to improve its accuracy rate. However, offline Arabic script classification and its related issues are still fresh. In this paper, we focus on pre-processing to post-processing techniques and highlight several issues in each phase in order to highlight need of high classification performance for Arabic script classification (offline and online). Additionally, top experimental results are reported, discussed and compared, and current challenges are also discussed. Finally, online versus offline Arabic script recognition achievements are also compared. © 2015 The Natural Computing Applications Forum


Azam A.,Al Yamamah University
International Journal of e-Business Research | Year: 2016

The last couple of decades have witnessed rapid technological advancements, which have consequently caused dramatic changes in the lives of consumers and their purchase behavior. Of the many causes of the rapid growth of Internet use in the last few decades, most researchers confer that the critical contribution has been the growth of Web content. This paper attempts to report a study investigating the impact of utilitarian website features on Saudi Arabian customer loyalty for booking flights online. Data collected from 340 respondents were used to test the hypotheses. Structural Equation Modeling was deployed to analyze valid data points. The study found the importance of creating loyalty by focusing on utilitarian website features. Calculative commitment is significantly influenced by utilitarian features whereas affective commitment didn't showed significantly influence by utilitarian features. Limitations, managerial implications and future research directions are discussed at the end of the paper. © 2016, IGI Global.


Almazyad A.S.,Al Yamamah University
Neural Computing and Applications | Year: 2016

Securing mobile ad hoc networks (MANET) has been the interest of researchers recently because of its use in important security sectors such as police, rescue teams, and the military. One method to ensure a secure ad hoc network is to identify malicious nodes (hostile) from good nodes by their reputation based on the past experience of packet delivery. In this paper, we explore by applying reputation in various ways the effect to the throughput of a MANET ad hoc network. We simulate four different scenarios where the node reputation is evaluated to choose the most reliable route and eliminate the effect of malicious nodes performing gray-hole attack. One of the applied scenarios is a hybrid method where a sender node takes in consideration the reputation of all the nodes forming a route to choose the most reliable route. We find that by applying the hybrid method the performance of the network is the best and data packets are more likely to be delivered successfully to the intended destination in a very hostile environment. © 2016 The Natural Computing Applications Forum


Koo C.,Kyung Hee University | Chung N.,Kyung Hee University | Nam K.,Al Yamamah University
International Journal of Information Management | Year: 2015

In this study, we investigated the determinants of perceived usefulness of smart green information technology (IT) device in reducing electricity consumption. Though there are many determinants of intention to use a technology, perceived usefulness is a key determinant of continuance intention. We used motivation theory to explain the causal relationship between motivational variables and perceived usefulness. By using reference group theory, we emphasized how a reference group moderates the relationship between motivations and the perceived usefulness of smart green IT device. We used partial least squares (PLS) regression for analysis and found that intrinsic motivations (perceived enjoyment) significantly relate to the perceived usefulness of smart green IT device. We also found that extrinsic motivations (saving money and legislative pressure) strongly relate to the perceived usefulness of this device. In summary, we found that perceived usefulness strongly impacts the continued use of a smart green IT device and that a reference group partially moderates the independent variables and moderator variables. © 2014 Elsevier Ltd.


Vani V.,Al Yamamah University | Mohan S.,Al Yamamah University
2014 International Computer Science and Engineering Conference, ICSEC 2014 | Year: 2014

3D content streaming and rendering system has attracted a significant attention from both academia and industry. However, these systems struggle to provide comparable quality to that of locally stored and rendered 3D data. Since the rendered 3D content on to the client machine is controlled by the users, their interactions have a strong impact on the performance of 3D content streaming and rendering system. Thus, considering user behaviors in these systems could bring significant performance improvements. In this paper, an Artificial Neural Network (ANN) based predictor is proposed for 3D content streaming and rendering. The user interactions on various 3D contents are profiled and used as information to train the Neural Network predictors. The 3D content could be static or dynamic 3D object / scene. We test our model through another set of interactions over the 3D contents by same users. The tested result shows that our model can learn the user interactions and is able to predict several interactions to help in optimizing the streaming and rendering for better performance. We also propose various approaches based on traces collected from the same/different users to accelerate the learning process of the neural network. © 2014 IEEE.


Gounder M.S.,Al Yamamah University | Iyer V.V.,Al Yamamah University | Mazyad A.A.,Al Yamamah University
2016 3rd MEC International Conference on Big Data and Smart City, ICBDSC 2016 | Year: 2016

This paper presents a detailed survey on existing Business Intelligence (BI) tools for developing a dashboard in a typical academic setup. As a survey, few most popular BI tools like SpagoBI, Tableau, Pentaho, Qliksense, Jaspersoft and Jedox are considered based on the ease of use, support in terms of training and minimal initial cost. At the end, a sample dashboard has been developed using one of the tools Tableau to demonstrate the feasibility of the same for the university data visualization. © 2016 IEEE.


Fauzi F.,Al Yamamah University | Belkhatir M.,University of Lyon
Information Processing and Management | Year: 2013

In this paper, we describe a user-centered design of an automated multifaceted concept based indexing framework which analyzes the semantics of the Web image contextual information and classifies it into five broad semantic concept facets: signal, object, abstract, scene, and relational; and identifies the semantic relationships between the concepts. An important aspect of our indexing model is that it relates to the users' levels of image descriptions. Also, a major contribution relies on the fact that the classification is performed automatically with the raw image contextual information extracted from any general webpage and is not solely based on image tags like state-of-the-art solutions. Human Language Technology techniques and an external knowledge base are used to analyze the information both syntactically and semantically. Experimental results on a human-annotated Web image collection and corresponding contextual information indicate that our method outperforms empirical frameworks employing tf-idf and location-based tf-idf weighting schemes as well as n-gram indexing in a recall/precision based evaluation framework. © 2012 Elsevier Ltd.


Vani V.,Al Yamamah University | Mohan S.,Al Yamamah University
Advances in Intelligent Systems and Computing | Year: 2016

3D content streaming and rendering system has attracted a significant attention from both academia and industry. However, these systems struggle to provide comparable quality to that of locally stored and rendered 3D data. Since the rendered 3D content on to the client machine is controlled by the users, their interactions have a strong impact on the performance of 3D content streaming and rendering system. Thus, considering user behaviours in these systems could bring significant performance improvements. To achieve this, we propose a symbolic decision tree that captures all attributes that are part of user interactions. The symbolic decision trees are built by pre-processing the attribute values gathered when the user interacts with the 3D dynamic object. We validate our constructed symbolic tree through another set of interactions over the 3D dynamic object by the same user. The validation shows that our symbolic decision tree model can learn the user interactions and is able to predict several interactions with very limited set of summarized symbolic interval data and thus could help in optimizing the 3D content streaming and rendering system to achieve better performance. © Springer India 2016.


Mohan S.,Al Yamamah University | Vani V.,Al Yamamah University
Advances in Intelligent Systems and Computing | Year: 2016

3D content streaming and rendering system has attracted a significant attention from both academia and industry. However, these systems struggle to provide comparable quality to that of locally stored and rendered 3D data. Since the rendered 3D content on to the client machine is controlled by the users, their interactions have a strong impact on the performance of 3D content streaming and rendering system. Thus, considering user behaviours in these systems could bring significant performance improvements. Towards the end, we propose a decision tree that captures all parameters making part of user interactions. The decision trees are built from the information found while interacting with various types of 3D content by different set of users. In this, the 3D content could be static or dynamic 3D object/scene. We validate our model through another set of interactions over the 3D contents by same set of users. The validation shows that our model can learn the user interactions and is able to predict several interactions helping thus in optimizing these systems for better performance. We also propose various approaches based on traces collected from the same/different users to accelerate the learning process of the decision tree. © Springer India 2016.

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