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2010 International Conference on Computational Aspects of Social Networks
Language Identification Using Wavelet Transform and Artificial Neural Network
Taiyuan, China
September 26-September 28
ISBN: 978-0-7695-4202-7
| ASCII Text | x | ||
| Shawki A. Al-Dubaee, Nesar Ahmad, Jan Martinovic, Václav Snášel, "Language Identification Using Wavelet Transform and Artificial Neural Network," Computational Aspects of Social Networks, International Conference on, pp. 515-520, 2010 International Conference on Computational Aspects of Social Networks, 2010. | |||
| BibTex | x | ||
| @article{ 10.1109/CASoN.2010.121, author = {Shawki A. Al-Dubaee and Nesar Ahmad and Jan Martinovic and Václav Snášel}, title = {Language Identification Using Wavelet Transform and Artificial Neural Network}, journal ={Computational Aspects of Social Networks, International Conference on}, volume = {0}, year = {2010}, isbn = {978-0-7695-4202-7}, pages = {515-520}, doi = {http://doi.ieeecomputersociety.org/10.1109/CASoN.2010.121}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Computational Aspects of Social Networks, International Conference on TI - Language Identification Using Wavelet Transform and Artificial Neural Network SN - 978-0-7695-4202-7 SP515 EP520 A1 - Shawki A. Al-Dubaee, A1 - Nesar Ahmad, A1 - Jan Martinovic, A1 - Václav Snášel, PY - 2010 KW - Wavelet Transform KW - artificial neural network KW - language identification KW - cross language KW - Unicode VL - 0 JA - Computational Aspects of Social Networks, International Conference on ER - | |||
In traditional language identification methods, it is not so easy for search engines to find relevant language database of a given query. Therefore, there is a need to identify the relevant user’s natural language query of unknown document database in a better way by automatic language identification. This novel approach presents an automatic method for classification of English and Arabic language identification. The classifier used is a three-layered feed-forward artificial neural network and the feature vector is formed by calculating the wavelet coefficients. Three wavelet decomposition functions (filters), namely Haar, Bior 2.2 and Bior 3.1 have been used to extract the feature vector set and their performance has been compared.
Index Terms:
Wavelet Transform, artificial neural network, language identification, cross language, Unicode
Citation:
Shawki A. Al-Dubaee, Nesar Ahmad, Jan Martinovic, Václav Snášel, "Language Identification Using Wavelet Transform and Artificial Neural Network," cason, pp.515-520, 2010 International Conference on Computational Aspects of Social Networks, 2010
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