A Framework to Channel Undesirable Messages and Pictures from OSN’s Clients Divider

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

One principal issue in today On-line Social Systems (OSNs) is to give clients the capacity to control the messages and images posted all alone private space to dodge that undesirable substance is shown. Up to now OSNs give little backing to this prerequisite. This is accomplished through an adaptable guideline based framework further more, a Machine Learning based delicate classifier consequently marking messages in backing of substance based sifting. In this paper, we likewise propose a novel way to deal with CBIR(Content Based Image Retrieval) framework in view of Genetic Algorithm to channel undesirable pictures.

References

[1] A. Adomavicius, G.and Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Transaction on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005.

[2] M. Chau and H. Chen, “A machine learning approach to web page filtering using content and structure analysis,” Decision Support  Systems, vol. 44, no. 2, pp. 482–494, 2008.

[3] R. J. Mooney and L. Roy, “Content-based book recommending using learning for text categorization,” in Proceedings of the Fifth ACM

Conference on Digital Libraries. New York: ACM Press, 2000, pp. 195–204.

[4] F. Sebastiani, “Machine learning in automated text categorization,” ACM Computing Surveys, vol. 34, no. 1, pp. 1–47, 2002.

[5] M. Vanetti, E. Binaghi, B. Carminati, M. Carullo, and E. Ferrari, “Content-based filtering in on-line social networks,” in Proceedings

of ECML/PKDD Workshop on Privacy and Security issues in Data Mining and Machine Learning (PSDML 2010), 2010.

[6] N. J. Belkin and W. B. Croft, “Information filtering and information retrieval: Two sides of the same coin?” Communications of the ACM, vol. 35, no. 12, pp. 29–38, 1992.

[7] N. S. Vassilieva, \Content Based Image Retrieval Methods", Programming and Computer Software Vol. 35, No. 3, 158 { 180 (2009).

[8] Y. Liu, D. Zhang, G. Lu, W. Y. Ma, \A Survey of Content-Based Image Retrieval with High Level Semantics", Pattern Recognition 40, 262 { 282 (2007).

[9] R. C. Veitkamp, M. Tanase, \Content | Based Image Retrieval Systems: A Survey", Technical report, UU-CS-2000-34, University of Utrecht (2000).

[10] S. Antani, R. Kasturi, R. Jain, \A Survey of the Use of Pattern Recognition Methods for Abstrac- tion, Indexing and Retrieval", Pattern Recognition 1, 945 { 965 (2002).

[11] X. S. Zhou, T. S. Huang, \Relevance Feedback in Content-Based Image Retrieval:Some Recent Advances", Information Science 48, 124 { 137 (2002).

[12] T. C. Lu, C. C. Chang, \Color Image Retrieval Technique Based on Color Features and Image Bitmap", Information Processing and Management 43, 461 { 472 (2007)

Keywords

Filtering Rules (FL), Blacklist (BL), Text based(TBIR) and Content based(CBIR), Machine Learning (ML).