Accepted Papers

  • Knowledge Protection Mechanisms in Collaborative Innovation Network: Case of Aerospace Inter-Organizational Network
    Yasser Bentahar,Khawarizmi International College, UAE

    The aim of this paper, is to investigate knowledge protection mechanisms adopted by aerospace industries into a collaborative innovation network. Our research is an exploratory study designed to build a real practical case. Data of our research are the result of a lengthy investigation using several international institutions' reports (Europe Innova; EU commission, Forbes, etc.), as well as internal documents of certain industries and testimonies gathered from some top managers in aerospace both military and civil sectors. This study has shown, that within inter-organizational aerospace collaboration, both legal and strategic knowledge protection mechanisms are used. Moreover, our research describes some in-house secured environment platforms adopted by aerospace companies.

  • A Comparative Study of Gene Selection Methods for Microarray Cancer Classification
    Hala Alshamlan, Ghada Badr, and Yousef Alohali,King Saud University,Kingdom of Saudi Arabia

    In recent years, a DNA microarray technique has gained more attraction in both scientific and in industrial fields. It is impor- tant to determine the informative genes that cause the cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. In order to gain deep insight into the cancer classification problem, it is necessary to take a closer look at the proposed gene selection methods. We believe that they should be an integral preprocessing step for cancer classification. Furthermore, finding an accurate gene selection method is very significant issue in a cancer classification area, because it re-duces the dimensionality of microarray dataset and selects informative genes. In this paper, we review, classify and compare the state-of-art gene selection methods. We proceed by evaluating the performance of each gene selection approach based on their classification accuracy and number of informative genes. In our evaluation, we will use four bench- mark microarray datasets for cancer diagnosis (Leukemia, Colon, Lung,and Prostate). In addition, we compare the performance of gene selection method to investigate the effective gene selection method that has theability to identify a small set of marker genes, and ensure high cancer classification accuracy.

  • Customized Mechanism for Cloud Based Data Sharing Over Intranet – Study Based on Survey Conducted for Pakistani Business Industry
    Qazi Shahab Azam1 and Muhammad Zulqarnain Siddiqui2,1Iqra University,Pakistan2Malaysia University of Science and Technology, Malaysia

    Open distributed storage gives access to the clients of an association to store and share their information. Since all the execution of administrations is done by means of web, an association have absence of control over the information or the system, now the inquiry emerges here that how the association get guarantee the confirmation of the security of their information and that won't be gotten to by another person that are not authentic proprietor furthermore execution will be affected in the event that somebody got access.Iterative procedure model offers separate structure for the improvement of use in which one can break an extensive application into little modules. In Iterative improvement, source code is planned and created and tried in rehashed cycles until it is in appropriate working structure then can be conveyed to clients. With every cycle new components can be included as well.

  • Checking Behavioural Compatibility in Service Composition with Graph Transformation
    Redouane Nouara and Allaoua Chaoui,Laboratory MISC University Abdel Hamid Mehri Constantine 2,Constantine Algeria

    The success of Service Oriented Architecture (SOA) largely depended on the success of automatic service composition. Dynamic service selection process should ensure full compatibility between the services involved in the composition. This compatibility must be both on static proprieties, called interface compatibility which can be easily proved and especially on behavioural compatibility that needs composability checking of basic services. In this paper, we propose (1) a formalism for modelling composite services using an extension of the Business Process (BP) modelling approach proposed by Benatallah et al. and (2) a formal verification approach of service composition. This approach uses the Graph Transformation (GT) methodology as a formal verification tool. It allows behavioural compatibility verification of two given services modelled by their BPs, used as the source graph in the GT operation. The idea consists of (1) trying to dynamically generate a graph grammar R (a set of transformation rules) whose application generates the composite service, if it exists, in this case (2) the next step consist in checking the deadlock free in the resulting composite service. To this end we propose an algorithm that we have implemented using the AGG, an algebraic graph transformation API environment under eclipse IDE.

  • Predicting Popularity of Korean Contents in Arab Countries Using a Data Mining Technique
    Park Young Eun1, Soumaya Chaffar2, Kim Myoung Sook3 and Ko Hye Young4,1,2Prince Sultan University,Saudi Arabia,3,4Seoul Women’s University,Korea

    Recently, many people in the Middle East and North Africa enjoy watching a variety of Korean contents such as Korean dramas, films, broadcasting programs and listening to Korean Pops. The Korean wave refers to the phenomenon of Korean entertainment and popular culture rolling over the world with TV dramas, films and pop music. Also it is known as "Hallyu " literally meaning 'flow from Korea' in Korean. This study examines the analysis of pattern on Arab countries (Middle East and North Africa) Consumers' consumption of the Korean Contents using social media, Facebook data. Then we focus on developing Predictive System using a Data Mining Technique.

  • Fast algorithms for unsupervised learning in large data sets
    Syed Quddus and Adil M. Bagirov,Federation University Australia,Australia.

    The ability to mine and extract useful information automatically, from large datasets, is a common concern for organizations (having large datasets), over the last few decades. Data over the internet is rapidly increasing day by day and the ability to collect and store large amounts of data is significantly increasing.Existing clustering algorithms are not always efficient and accurate in solving clustering problems for large datasets.However, the development of accurate and fast data classification algorithms for very large scale datasets is still a challenge. In this paper, various algorithms and techniques especially, approach using non-smooth optimization formulation of the clustering problem, are proposed for solving the minimum sum-of-squares clustering problems in very large datasets. This research also develops accurate and real time L2-DC algorithm based with the incremental approach to solve the minimum sum-of-squared clustering problems in very large datasets, in a reasonable time.

  • Text Extraction From Raster Maps Using Colour Space Quantization
    Sanaz Hadipour Abkenar and Alireza Ahmadyfard Shahrood University of Technology, Shahrood, Iran

    Maps convey valuable information by relating names to positions. In this paper we present a new method for text extraction from raster maps using color space quantization. Previously, most researches in this field were focused on Latin texts and the results for Persian or Arabic text were poor. In our proposed method we use a Mean-Shift algorithm with proper parameter adjustment and consequently, we apply colour transformation to make the maps ready for K-Means algorithm which quantizes the colors in maps to six levels. By comparing to a threshold the text layer candidates are then limited to three. The best layer can afterwards be chosen by user. This method is independent of font size, direction and the color of the text and can find both Latin and Persian/Arabic texts in maps. Experimental results show a significant improvement in Persian text extraction.

  • Clustering with Neural Networks for precise forecasting of ATM cash repository
    Pankaj KumarJadwal,Sonal Jain ,Umesh Gupta JK Lakshmipat University, Jaipur,India

    Optimal forecasting of ATM cash repository is a complex task. This paper deals with cash demand forecasting of NN5 time series data using neural networks. NN5 reduced Dataset is a subsample of 11 time series of complete dataset of 111 daily time series drawn from homogeneous population of empirical cash demand time series. Main objective of this paper is to forecast cash demand forecasting. In very first step, Neural Network is applied on individual ATMS and forecasting is being done. In the Second Step, Neural Network is applied on Clusters of ATMS. Discrete time wrapping is used as distance measure for creating Clusters. Root mean square error has been calculated for such clustered group of ATMs and average is calculated. Root Mean Square error indicates applications of clustering before applying Neural Network increases precision in forecasting of ATM Cash Repository.