Research on Internet Monitoring System Based on Multi-Layer Text Information Filtering Method through Artificial Neural Networks

Jian Qing Liu; Mei Luo

doi:10.4028/www.scientific.net/AMR.532-533.1036

Paper Titles

Earthquake Prediction Research Based on the Mining in Time Series of Groundwater Temperature
p.1016

Wavelet Shrinkage Based Variational Image Decomposition Model
p.1021

Research of Adaptive Synthesis Rule for Conflict Evidences
p.1026

The Application of MLAMBDA for Time Series Radar Interferometry
p.1031

Research on Internet Monitoring System Based on Multi-Layer Text Information Filtering Method through Artificial Neural Networks
p.1036

An Adaptive Image De-Noising Method Based on Singular Value Decomposition
p.1041

Evaluation for the Hypersonic Near-Space Target Threat Based on GRA-TOPSIS Model
p.1046

Database Encryption for Dababase-as-a-Service
p.1051

The Visualization for SMT Based on Raster Data Engine
p.1056

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 532-533Research on Internet Monitoring System Based on...

Research on Internet Monitoring System Based on Multi-Layer Text Information Filtering Method through Artificial Neural Networks

Abstract:

Internet monitoring has recently been the focus of media attention and public debate. This paper proposed a novel method of multi-layer smart monitor system to filter unhealthy information using artificial neural networks (ANN). This method classified the text into multilayer and uses RPROP algorithm to implement the text classifier. Finally, the test was deployed and the feasibility of this algorithm was proven.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 532-533)

Pages:

1036-1040

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.532-533.1036

Citation:

Cite this paper

Online since:

June 2012

Authors:

Jian Qing Liu, Mei Luo

Keywords:

Artificial Neural Network (ANN), Information Filter, Unhealthy Information

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Platform for Internet Content Selection on http: /www. w3. org/PICS.

Google Scholar

[2] Jian Lin, Fan Zhang. Research on Filtering Unhealthy Information. information system, 4 (3) (2007).

Google Scholar

[3] Tak W Yan, Hector Garcia-Molina. SIFT-A tool for wide area information dissemination. Proc. USENIX Winter 1995 Technical Conference, New Orleans, p.177~186 (1995).

Google Scholar

[4] Curt Stevens. Knowledge-Based Assistance for Accessing Large, Poorly Structured Information Spaces. [PhD thesis], Boulder: University of Colorado, Department of Computer Science, (1993).

Google Scholar

[5] Wen-shuai Zhou, Su Feng. Research Status and Application on Chinese SegmentTechnology. Journal of Shanxi Normail University (Natural Science Edition), 20(1), p.25~29 (2006).

Google Scholar

[6] Chun Zeng, Xiao-chun Xing, Li-zhu Zhou. A Personalized Search Algorithm by Using Content Filtering. Journal of Software, 14(5), p.999~1004 (2003).

Google Scholar

[7] Li Liang, Zhang-qin Huang, and Yi-bin Hou. Research and Implementation of the Network Information Filter System. MINI MICRO SYSTEMS, 24(2), p.195~198 (2003).

Google Scholar

[8] T. Y. Kwok, D. Y. Yeung. Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems. IEEE Transactions on Neural Networks, (8), pp.630-645, (1997).

DOI: 10.1109/72.572102

Google Scholar

[9] T. Y. Kwok and D. Y. Yeung. Objective Functions for Training New Hidden Units in Constructive Neural Network. IEEE Transactions on Neural Networks, 8, pp.1131-1148 (1997).

DOI: 10.1109/72.623214

Google Scholar

[10] M. Lehtokangas. Modeling with Constructive Backpropagation. Neural Network, (1999), 12, pp.707-716.

DOI: 10.1016/s0893-6080(99)00018-0

Google Scholar

[11] Li-hui Zhou, Pu Han, Song-ming Jiao, Bi-hua Lin. Feedforward Neural Networks Using RPROP Algorithm And Its Application In System Identification. Pro. Machine Learning and Cybernetics, Nov. (2002).

DOI: 10.1109/icmlc.2002.1175396

Google Scholar

[12] Reed, R.D., Marks. Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks. Cambridge, MA: The MIT Press.

DOI: 10.7551/mitpress/4937.001.0001

Google Scholar

[13] Kai-li Zhou, Yao-hong Kang, Neural Network Model and MATALAB Emulating Programming. Beijiing, Tsinghua Press, p.69~100 (2005).

Google Scholar