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Tianyong Hao and Yingying Qu Carayannis, E. G. (2012). Knowledge Arbitrage, Serendipity, and Acquisition Formality: Their Effects on Sustainable Entrepreneurial Activity in Regions. IEEE Transactions on Engineering Management, 58(3), 546-577. Carenini, G., & Moore, J. (1993). Using the UMLS Semantic Network as a Basis for Constructing a Terminological Knowledge Base: a preliminary report. In Proceedings of Annual Symposium on Computer Applica-tions in Medical Care, pp. 725-729. Dawoud, K., Qabaja, A., Shang Gao, Alhajj, R., & Rokne, J. (2012). Identifying Cancer Biomarkers by Knowledge Discovery From Medical Literature. In Proceeedings of IEEE International Conference on Computational Advances in Bio and Medical Sciences. Deng, H., & Han, J. (2012). Uncertainty Reduction for Knowledge Discovery and Information Extraction on the World Wide Web. Proceedings of the IEEE, Vol. 100, Issue: 9, pp. 2658-2674. Dependency grammar. (2012). Retrieved from http://en.wikipedia.org/wiki/Dependency_grammar/. Fan, J., Kalyanpur, A., Gondek, D. C., & Ferrucci, D. A. (2012). Automatic Knowledge Extraction From Documents. IBM Journal of Research and Development, 56(3.4), 5:1- 5:10. Firdaus, O. M., Suryadi, K., Govindaraju, R., & Samadhi, T. M. A. A. (2012). Medical Knowledge Sharing Guideline: A Conceptual Model. In Proceedings of International Conference on ICT and Knowledge Engineering, pp. 22- 26. Hao, T. Y., Ni, X. L., Quan, X. J., & Liu, W. Y. (2009). Automatic Construction of Semantic Dictionary for Question Categorization. Journal of Systemics, Cybernetics and Informatics, 7(6), 86-90. Hull, R., & Gomez, F. (1999). Automatic Acquisition of Biographic Knowledge From Encyclopedic Texts. Expert Systems with Applications, 16(3), 261-270. Minpar Evaluation. (2012). http://www.cs.ualberta.ca/~lindek/downloads.htm. OpenNLP. (2012). http://opennlp.sourceforge.net/projects.html. Smith, M. K., Welty, C., & McGuinness, D. L. (2004). OWL Web Ontology Language Guide. W3C. Tanaka, K., & Jatowt, A. (2010). Automatic Knowledge Acquisition from Historical Document Archives: Historiographical Perspective. LNCS, 6259, 161-172. TreeTagger. (2012). Retrieved from http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger/. Wang, Y. M., Johanna, V., & Haase, P. (2006). Towards Semi-automatic Ontology Building Supported by Large-scale Knowledge Acquisition. In AAAI Fall Symposium On Semantic Web for Collaborative Knowledge Acquisition, Vol. FS-06-06, pp.70-77. WordNet. (2012). Retrieved from http://wordnet.princeton.edu/. Yahoo! Answers. (2012). Retrieved from http://answers.yahoo.com/. 182 Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013)
ISSN: 0128-7680 © 2013 Universiti Putra Malaysia Press. Pertanika J. Sci. & Technol. 21 (1): 183 - 192 (2013) SCIENCE & TECHNOLOGY Journal homepage: http://www.pertanika.upm.edu.my/ Issues on Trust Management in Wireless Environment Abubakr Sirageldin*, Baharum Baharudin and Low Tang Jung Computer and Information Science Department, University Technology Petronas, Bandar Seri Iskandar, 31750 Tronoh, Perak, Malaysia ABSTRACT Developing a trust management scheme in mobile computing environment is increasingly important, and the effective trust management model is a challenging task. Business, education, military, and entertainment have motivated the growth of ubiquitous and pervasive computing environments, which are always available due to the widespread of portable and embedded devices. Wireless and mobile computing are good example of ubiquitous and pervasive computing environments. Due to the uncertainty and mobility in such environments, the issue of trust has been regarded as an important security problem. Malicious nodes are a major threat to these networks; the trust system can monitor the behaviour of nodes and accordingly rewards well-behaved nodes and punishes misbehaving ones. At present, there are a lot of endeavours on the trust model of the pervasive computing environment. In this paper, a trust management framework for mobile computing is presented. The hybrid framework is based on a fusion of the support vector machine (SVM) and fuzzy logic system. From the results, it can be stated that the framework is effective, dynamic, lightweight, and applicable. Keywords: Trust management, support vector machine, fuzzy logic, membership, interaction, pervasive, recommendation, central node, relationship INTRODUCTION Due to the rapid growth in network and communication technology, and the widespread of various types of computing devices, and the constant availability of services, security, confidentiality and the reliability are required in such an environment. Devices interact, collect and transfer Article history: Received: 31 March 2012 Accepted: 31 August 2012 E-mail addresses: abubakrsirag@gmail.com (Abubakr Sirageldin), baharbh@petronas.com.my (Baharum Baharudin), lowtanjung@petronas.com.my (Low Tang Jung) *Corresponding Author information with simplicity, and minimal technical expertise without being previously introduced to each other. This necessitates a certain concept of security such as trust. In order to maintain a secure, dependable, and reliable environment, a smart security system without or with the least human participation is
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International Advisory Board Adarsh
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Selected Articles from UPM-Malaysia
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CONCLUSION Heng, S. C., Ibrahim, Z.
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Surface Roughness Y. Yusuf, J. M. J
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DISCUSSION Y. Yusuf, J. M. Juoi, Z.
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Modelling of Motion Resistance Rati
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Assessment of Heavy Metal Pollution
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TABLE 1: (continued) Physico-Chemic
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Norhashila Hashim et al. effectiven
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Statistical Analysis Norhashila Has
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Norhashila Hashim et al. Post-hoc a
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INTRODUCTION Tajau, R. et al. A hig
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REFERENCES Yogan Jaya Kumar, Naomie
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Hasiah Mohamed@Omar, Azizah Jaafar
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The Role of Similarity Measurement
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REFEREES FOR THE PERTANIKA JOURNAL
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Guidelines for Authors Publication
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table should be prepared on a separ
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Usability of Educational Computer G
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