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1860799936800489472
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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2013-10-02 16:28:29
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Restricted Document
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7989
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UniSZA
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[1] K. S. Candan and M. L. Sapino, “Data Management for Multimedia Retrieval”, Cambridge University Press (2010). [2] S. Dai, H. Xu and H. Zhou, “Research on Multimedia Resource Management System Based on Web Services”, Proc. of International Conference on Industrial and Information Systems, (2009), pp. 259-262. [3] M. Farham, M. A. R. Nordin and M. A. Lazim, “The Development of Temporal-Based Multimedia Data Management Application Using Web Services”, Proc. Of International Conference on Intelligent Systems Design and Application, (2011), pp. 487-492. [4] m. Farham, M. A. R. Nordin, M. L. Yuzarimi and B. M. Saiful, “Managing Multimedia Data: A Temporal�Based Approach”, International Journal of Multimedia and Ubiquitous Engineering, vol. 7, no. 4, (2012), pp. 73-85. [5] J. Griffioen, B. Seales, R. Yavatkar and K. S. Kiernan, “Content based multimedia data management and efficient remote access”, Extrait de la Revue Informatique et Statisque dens les Sciences Humaines, vol. 1, no. 4, (1997), pp. 213-233. [6] A. E. Hassanien and J. M. H. Ali, “Rough Set Approach for Generation of Classification Rules of Breast Cancer Data”, INFORMATICA, vol. 15, no. 1, (2008), pp. 22-38. [7] X. Hu, N. Shan, N. Cercone and W. Ziarko, “DBROUGH: A rough set based knowledge discovery system, methodologies for intelligent systems”, Z. Ras and M. Zemankova, Eds. Springer-Verlag Press, (1994). [8] S. K. Jalal, “Multimedia Database: Content and Structure”, Workshop on Multimedia and Internet Technologies, (2001). [9] M. Y. Kamir, M. A. R. Nordin and M. M. A. Atar, “Ontology and Semantic Web Approaches for Heterogeneous Database Access”, International Journal of Database Theory and Application, vol. 4, no. 4, (2011), pp. 13-23. [10] M. Y. Kamir, M. A. R. Nordin and A. Atiqah, “Reducing of Inconsistent Data Using Fuzzy Multi Attribute Decision Making for Accessing Data from Database”, International Journal of Database Theory and Application, vol. 6, no. 1, (2013), pp. 1-11. [11] T. E. McKee and T. Lensberg, “Genetic Programming and Rough Sets: A hybrid approach to bankruptcy classification”, European Journal of Operational Research, vol. 138, (2002), pp. 436-451. [12] H. Midelfart, J. Komorowski, K. Norsett, F. Yedetie, A. K. Sandwick and A. Largreid, “Learning Rough Set Classifier from Gene Expression and Clinical Data”, Fundamental Informaticae, vol. 53, (2002), pp. 155- 183. [13] M. A. R. Nordin, A. W. Fauziah, I. Rohana and U. Norlina, “A Comprehensive Innovation Management Model for Malaysians Public Higher Learning Educations”, International Journal of Software Engineering and Its Applications, vol. 7, no. 1, (2013), pp. 45-55. [14] M. A. R. Nordin, M. L. Yuzarimi and M. Farham, “Applying Rough Set Theory in Multimedia Data Classification”, International Journal on New Computer Architecture and Their Applications, vol. 1, no. 3, (2011), pp. 706-716. [15] M. A. R. Nordin, M. L. Yuzarimi, M. Farham, S. Suhailan, M. D. Sufian and M. Y. Kamir, “Rough Set Theory Approach for Classifying Multimedia Data”, (Ed. Jasni, M.Z.) in Software Engineering and Computer Systems, Springer Verlag, (2011), pp. 116-124. [16] Z. Pawlak, “Rough set: Theoretical aspect of reasoning about data”, Kluwer Academic Publisher, Dordrent, (1991). [17] L. Shen and S. Chen, “Research of Customer Classification Based on Rough Set using Rosetta Software”, American Journal of Engineering and Technology Research, vol. 11, no. 9, (2011), pp. 1279-1285. [18] X. Wang, N. Liu and K. Xie, “Application of Rough Set Theory on Scene Image Classification”, Chinese Control and Decision Conference, (2008), pp. 2338-2342. [19] M. L. Yuzarimi, M. A. R. Nordin and M. Farham, “Clustering Model of Multimedia Data By Using Rough Sets Theory”, Proceedings of International Conference on Computers and Information Sciences, (2012), pp. 336-340. [20] The ROSETTA homepage http://www.idi.ntnu.no/~aleks/rosetta/. Norwegian University of Science and Technology, Department of Computer and Information Science. (Downloaded on January 2012).
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7989 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=7989 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf 10 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in Springer-SBM 2013-10-02 16:28:29 3817-01-FH02-FIK-14-00608.pdf UniSZA Private Access Rules Generation for Multimedia Data Classifying using Rough Sets Theory International Journal of Hybrid Information Technology An efficient multimedia data management process is very important in multimedia system application. Huge size of multimedia data that distributed in multi locations makes multimedia data management more complicated. Rapid development of multimedia applications created a vast volume of multimedia data and it is exponentially incremented from time to time. This situation requires for efficient data classification and organization technique for providing effective multimedia data manipulation process. Using rough sets theory and web services technology, this paper proposed a new rules generation for multimedia data classifying in collaborative environment. ROSETTA tool is applied to verify the reliability of the generated results. The experiments show that the rough sets theory based for multimedia data classifying is suitable to be executed in web services environment. 6 5 209-218 [1] K. S. Candan and M. L. Sapino, “Data Management for Multimedia Retrieval”, Cambridge University Press (2010). [2] S. Dai, H. Xu and H. Zhou, “Research on Multimedia Resource Management System Based on Web Services”, Proc. of International Conference on Industrial and Information Systems, (2009), pp. 259-262. [3] M. Farham, M. A. R. Nordin and M. A. Lazim, “The Development of Temporal-Based Multimedia Data Management Application Using Web Services”, Proc. Of International Conference on Intelligent Systems Design and Application, (2011), pp. 487-492. [4] m. Farham, M. A. R. Nordin, M. L. Yuzarimi and B. M. Saiful, “Managing Multimedia Data: A Temporal�Based Approach”, International Journal of Multimedia and Ubiquitous Engineering, vol. 7, no. 4, (2012), pp. 73-85. [5] J. Griffioen, B. Seales, R. Yavatkar and K. S. Kiernan, “Content based multimedia data management and efficient remote access”, Extrait de la Revue Informatique et Statisque dens les Sciences Humaines, vol. 1, no. 4, (1997), pp. 213-233. [6] A. E. Hassanien and J. M. H. Ali, “Rough Set Approach for Generation of Classification Rules of Breast Cancer Data”, INFORMATICA, vol. 15, no. 1, (2008), pp. 22-38. [7] X. Hu, N. Shan, N. Cercone and W. Ziarko, “DBROUGH: A rough set based knowledge discovery system, methodologies for intelligent systems”, Z. Ras and M. Zemankova, Eds. Springer-Verlag Press, (1994). [8] S. K. Jalal, “Multimedia Database: Content and Structure”, Workshop on Multimedia and Internet Technologies, (2001). [9] M. Y. Kamir, M. A. R. Nordin and M. M. A. Atar, “Ontology and Semantic Web Approaches for Heterogeneous Database Access”, International Journal of Database Theory and Application, vol. 4, no. 4, (2011), pp. 13-23. [10] M. Y. Kamir, M. A. R. Nordin and A. Atiqah, “Reducing of Inconsistent Data Using Fuzzy Multi Attribute Decision Making for Accessing Data from Database”, International Journal of Database Theory and Application, vol. 6, no. 1, (2013), pp. 1-11. [11] T. E. McKee and T. Lensberg, “Genetic Programming and Rough Sets: A hybrid approach to bankruptcy classification”, European Journal of Operational Research, vol. 138, (2002), pp. 436-451. [12] H. Midelfart, J. Komorowski, K. Norsett, F. Yedetie, A. K. Sandwick and A. Largreid, “Learning Rough Set Classifier from Gene Expression and Clinical Data”, Fundamental Informaticae, vol. 53, (2002), pp. 155- 183. [13] M. A. R. Nordin, A. W. Fauziah, I. Rohana and U. Norlina, “A Comprehensive Innovation Management Model for Malaysians Public Higher Learning Educations”, International Journal of Software Engineering and Its Applications, vol. 7, no. 1, (2013), pp. 45-55. [14] M. A. R. Nordin, M. L. Yuzarimi and M. Farham, “Applying Rough Set Theory in Multimedia Data Classification”, International Journal on New Computer Architecture and Their Applications, vol. 1, no. 3, (2011), pp. 706-716. [15] M. A. R. Nordin, M. L. Yuzarimi, M. Farham, S. Suhailan, M. D. Sufian and M. Y. Kamir, “Rough Set Theory Approach for Classifying Multimedia Data”, (Ed. Jasni, M.Z.) in Software Engineering and Computer Systems, Springer Verlag, (2011), pp. 116-124. [16] Z. Pawlak, “Rough set: Theoretical aspect of reasoning about data”, Kluwer Academic Publisher, Dordrent, (1991). [17] L. Shen and S. Chen, “Research of Customer Classification Based on Rough Set using Rosetta Software”, American Journal of Engineering and Technology Research, vol. 11, no. 9, (2011), pp. 1279-1285. [18] X. Wang, N. Liu and K. Xie, “Application of Rough Set Theory on Scene Image Classification”, Chinese Control and Decision Conference, (2008), pp. 2338-2342. [19] M. L. Yuzarimi, M. A. R. Nordin and M. Farham, “Clustering Model of Multimedia Data By Using Rough Sets Theory”, Proceedings of International Conference on Computers and Information Sciences, (2012), pp. 336-340. [20] The ROSETTA homepage http://www.idi.ntnu.no/~aleks/rosetta/. Norwegian University of Science and Technology, Department of Computer and Information Science. (Downloaded on January 2012).
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| spellingShingle |
Rules Generation for Multimedia Data Classifying using Rough Sets Theory
|
| summary |
An efficient multimedia data management process is very important in multimedia system application. Huge size of multimedia data that distributed in multi locations makes multimedia data management more complicated. Rapid development of multimedia applications created a vast volume of multimedia data and it is exponentially incremented from time to time. This situation requires for efficient data classification and organization technique for providing effective multimedia data manipulation process. Using rough sets theory and web services technology, this paper proposed a new rules generation for multimedia data classifying in collaborative environment. ROSETTA tool is applied to verify the reliability of the generated results. The experiments show that the rough sets theory based for multimedia data classifying is suitable to be executed in web services environment.
|
| title |
Rules Generation for Multimedia Data Classifying using Rough Sets Theory
|
| title_full |
Rules Generation for Multimedia Data Classifying using Rough Sets Theory
|
| title_fullStr |
Rules Generation for Multimedia Data Classifying using Rough Sets Theory
|
| title_full_unstemmed |
Rules Generation for Multimedia Data Classifying using Rough Sets Theory
|
| title_short |
Rules Generation for Multimedia Data Classifying using Rough Sets Theory
|
| title_sort |
rules generation for multimedia data classifying using rough sets theory
|