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BİLGİ EDİNME HAKKI YASASI ÇERÇEVESİNDE YAPILAN ELEKTRONİK BAŞVURULARIN YAPAY SİNİR AĞLARI İLE SINIFLANDIRMASI

Year 2009, Volume: 23 Issue: 4, 27 - 41, 12.08.2010

Abstract

Bu çalışmada, Bilgi Edinme Hakkı Yasası çerçevesinde örgütlere
yapılan elektronik başvuruların sınıflandırılması esnasında ortaya çıkan işgücü
ve zaman kaybı gibi problemleri gidermek amacıyla bir elektronik başvuru
sınıflandırma sistemi geliştirilmiştir. Sistem, Erzincan Üniversitesine yapılan
elektronik başvuruları sınıflara ayırıp ilgili Daire Başkanlıklarına iletmektedir.
İş akışını önemli ölçüde hızlandırıp, iş gücü kaybının önüne geçen Yapay Sinir
Ağı destekli sistem, kelime tabanlı olup, danışmanlı öğrenmekte ve %98
doğrulukla çalışmaktadır.

References

  • Sayılı Bilgi Edinme Hakkı Kanunu, Resmi Gazete, Sayı: 25269, 24 Ekim 2003.
  • Benkhalifa, M., Bensaid, A. ve Mouradi, A. (1999) “Text Categorization Using the Semi-Supervised Fuzzy c-Means Algorithm”, 18th International Conference of the North American Fuzzy Information Processing Society, ss.561 565.
  • Bing, L., Yiyuan, X. ve Philip, S.Y. (2000) “Clustering Through Decision Tree Construction”, Ninth International Conference on Information and Knowledge Management, ss.20 29.
  • Burgin, R. (1995) “The Retrieval Effectiveness of Five Clustering Algorithms as a Function of Indexing Exhaustivity”, Journal of the American Society for Information Science, 46 (8), ss.562 572.
  • Can, F., Koçberber, S., Balçık, E., Kaynak, C., Öcalan, Ç. ve Vursavaş, O. (2008) “Information Retrieval on Turkish Texts”, Journal of the American Society for Information Science and Technology, ss.1 28.
  • Cardoso-Cachopo, A. ve Oliveira, A.L. (2007) “Semi-supervised Single-label Text Categorization Using Centroid-based Classifiers”, 2007 ACM Symposium on Applied Computing Proceeding, ss.844 851.
  • Chen J., Huang H., Shengfeng, H. ve Qu, Y. (2009) “Feature Selection for Text Classification with Naive Bayes”, Expert Systems with Applications, 36(3), ss.5432-5435.
  • Cybenko, G. (1989) “Approximation by Superposition of a Sigmoidal Function”, Math. Control Signals Systems, 2, ss.303 309.
  • Fu, L.M. (1994), Neural Networks in Computer Intelligence, McGraw-Hill, OH.
  • Guerrero-Bote, V.P., Lopez-Pujalte, C., Moya-Anegon, F.D. ve Herrero-Solana, V. (2003) “Comparison of Neural Models for Document Clustering”, International Journal of Approximate Reasoning, 34, ss.287 305.
  • Haikin, S. (1994), Neural Networks. A Compherensive Foundatition, Prentice- Hall, NJ.
  • Han H., Manavoglu E., Giles C.L. ve Zha, H. (2003) “Rule-based Word Clustering for Text Classification”, 26th annual international ACM SIGIR Conference on Research and Development in Informaion Retrieval, ss.445-446.
  • Han, E., Karypis, G. ve Kumar, V. (2001) “Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification”, Computer Science, 2035, ss.53 65.
  • Hara, K. ve Nakayama, K. (1994) “Comparison of Activation Functions in Multi Layer Neural Networks for Pattern Classification” ICAN”94,ss.819 822.
  • https://zemberek.dev.java.net/
  • Isa. D., Kallimani, V.P. ve Lee, L.H. (2009) “Using the Self Organizing Map for Clustering of Text Documents”, Expert Systems with Applications, 36(5), ss.9584 9591.
  • Iwayama, M. ve Tokunaga, T. (1995) “Hierarchical Bayesian Clustering for Automatic Text Classification”, International Joint Conference on Artificial Intelligence, ss.1 9.
  • Kazuyuki, H. ve Kenji, N. (2000) “Selection of Activate Function For Multilayer Neural Networks”, Reports of the Tokyo Metropolitan Technical College, ss.41 47.
  • Kohonen, T. (1990), Self-organization and Associative Memory, Springer- Verlag, Heidelberg.
  • Lai, K. ve Lam, W. (2001) “Automatic Textual Document Categorization Using Multiple Similarity-Based Models”, Lecture Notes in Computer Science, 2035, ss.78 89.
  • Lam, W. ve Low K. (1997) “Automatic Document Classification Based on Probabilistic Reasoning: Model and Performance Analysis”, IEEE International Conference on Systems, Man and Cybernetics, ss.2719 2723.
  • Levenberg, K. (1944) “A Method for the Solution of Certain Nonlinear Problems in Least Squares”, Quarterly of Applied Mathematics, 2, ss.164 168.
  • Lewis, D.D. (1992) “Feature Selection and Feature Extraction for Text Categorization”, Speech and Natural Language, ss.212 217.
  • Liu, R. Ve Lu, Y. (2002) “Incremental Context Mining for Adaptive Document Classification”, Eighth International Conference on Knowledge Discovery and Data Mining, ss.599 604.
  • Manevitz, L. ve Yousef, M. (2007) “One-Class Document Classification via Neural Networks”, Neurocomputing, 70, ss.1466 1481.
  • Marquardt, D.W. (1963) “An Algorithm for Least-Squares Estimation of Nonlinear Parameters”, Journal of the Society for Industrial and Applied Mathematics, 11, ss.431 441.
  • Nabiyev, V. (2005), Yapay Zekâ, Genişletilmiş İkinci Baskı, Seçkin Yayıncılık, Ankara.
  • Özgür, L., Güngör, T. ve Gürgen, F. (2004) “Adaptive Anti-Spam Filtering for Agglutinative Languages : A Special Case for Turkish”, Pattern Recognation Letters, 25, ss.1819 1831.
  • Sağıroğlu, Ş., Beşdok, E. ve Erler, M. (2003), Mühendislikte Yapay Zeka Uygulamaları 1 – Yapay Sinir Ağları, Ufuk Yayınevi, Kayseri.
  • Sahami, M., Yusufali, S. ve Baldonado, M. (1997) “Real-time Full-text Clustering of Networked Documents”, National Conference on Artificial Intelligence, ss.845 851.
  • Sandler, M. (2005) “On the Use of Linear Programming for Unsupervised Text Classification”, ACM–SIGKDD Conference on Knowledge Discovery and Data Mining, ss.256 264.
  • Schalkoff, J.R. (1997), Artificial Neural Network, McGraw-Hill, OH.
  • Song, W., Li, C. H. ve Park, S.C. (2009) “Genetic Algorithm for Text Clustering Using Ontology and Evaluating the Validity of Various Semantic Similarity Measures”, Expert Systems with Applications, 36(5), ss.9095 9104.
  • Tsoukalas, L.H. ve Uhrig, R.E. (1997), Fuzzy and Neural Approaches in Engineering, John Wiley & Sons Inc.
  • Vapnik, V.(2000), The Nature of Statistical Learning Theory, Springer, New York.
  • Yu, B., Xu, Z. ve Li, C. (2008) “Latent Semantic Analysis For Text Categorization Using Neural Network”, Knowledge-Based Systems, 21, ss.900 904.
  • Zamir, O. ve Etzioni, O. (1998) “Web Document Clustering:a Feasibility Demonstration”, 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ss. 46 54.
  • Zayani, R., Bouallegue, R. ve Roviras, D. (2008) “Levenberg-Marquardt Learning Neural Network for Adaptive Pre-0 Distortion for Time- Varying hpa with Memory in OFDM Systems”, 16. European Signal Processing Conference, ss.758 765
Year 2009, Volume: 23 Issue: 4, 27 - 41, 12.08.2010

Abstract

References

  • Sayılı Bilgi Edinme Hakkı Kanunu, Resmi Gazete, Sayı: 25269, 24 Ekim 2003.
  • Benkhalifa, M., Bensaid, A. ve Mouradi, A. (1999) “Text Categorization Using the Semi-Supervised Fuzzy c-Means Algorithm”, 18th International Conference of the North American Fuzzy Information Processing Society, ss.561 565.
  • Bing, L., Yiyuan, X. ve Philip, S.Y. (2000) “Clustering Through Decision Tree Construction”, Ninth International Conference on Information and Knowledge Management, ss.20 29.
  • Burgin, R. (1995) “The Retrieval Effectiveness of Five Clustering Algorithms as a Function of Indexing Exhaustivity”, Journal of the American Society for Information Science, 46 (8), ss.562 572.
  • Can, F., Koçberber, S., Balçık, E., Kaynak, C., Öcalan, Ç. ve Vursavaş, O. (2008) “Information Retrieval on Turkish Texts”, Journal of the American Society for Information Science and Technology, ss.1 28.
  • Cardoso-Cachopo, A. ve Oliveira, A.L. (2007) “Semi-supervised Single-label Text Categorization Using Centroid-based Classifiers”, 2007 ACM Symposium on Applied Computing Proceeding, ss.844 851.
  • Chen J., Huang H., Shengfeng, H. ve Qu, Y. (2009) “Feature Selection for Text Classification with Naive Bayes”, Expert Systems with Applications, 36(3), ss.5432-5435.
  • Cybenko, G. (1989) “Approximation by Superposition of a Sigmoidal Function”, Math. Control Signals Systems, 2, ss.303 309.
  • Fu, L.M. (1994), Neural Networks in Computer Intelligence, McGraw-Hill, OH.
  • Guerrero-Bote, V.P., Lopez-Pujalte, C., Moya-Anegon, F.D. ve Herrero-Solana, V. (2003) “Comparison of Neural Models for Document Clustering”, International Journal of Approximate Reasoning, 34, ss.287 305.
  • Haikin, S. (1994), Neural Networks. A Compherensive Foundatition, Prentice- Hall, NJ.
  • Han H., Manavoglu E., Giles C.L. ve Zha, H. (2003) “Rule-based Word Clustering for Text Classification”, 26th annual international ACM SIGIR Conference on Research and Development in Informaion Retrieval, ss.445-446.
  • Han, E., Karypis, G. ve Kumar, V. (2001) “Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification”, Computer Science, 2035, ss.53 65.
  • Hara, K. ve Nakayama, K. (1994) “Comparison of Activation Functions in Multi Layer Neural Networks for Pattern Classification” ICAN”94,ss.819 822.
  • https://zemberek.dev.java.net/
  • Isa. D., Kallimani, V.P. ve Lee, L.H. (2009) “Using the Self Organizing Map for Clustering of Text Documents”, Expert Systems with Applications, 36(5), ss.9584 9591.
  • Iwayama, M. ve Tokunaga, T. (1995) “Hierarchical Bayesian Clustering for Automatic Text Classification”, International Joint Conference on Artificial Intelligence, ss.1 9.
  • Kazuyuki, H. ve Kenji, N. (2000) “Selection of Activate Function For Multilayer Neural Networks”, Reports of the Tokyo Metropolitan Technical College, ss.41 47.
  • Kohonen, T. (1990), Self-organization and Associative Memory, Springer- Verlag, Heidelberg.
  • Lai, K. ve Lam, W. (2001) “Automatic Textual Document Categorization Using Multiple Similarity-Based Models”, Lecture Notes in Computer Science, 2035, ss.78 89.
  • Lam, W. ve Low K. (1997) “Automatic Document Classification Based on Probabilistic Reasoning: Model and Performance Analysis”, IEEE International Conference on Systems, Man and Cybernetics, ss.2719 2723.
  • Levenberg, K. (1944) “A Method for the Solution of Certain Nonlinear Problems in Least Squares”, Quarterly of Applied Mathematics, 2, ss.164 168.
  • Lewis, D.D. (1992) “Feature Selection and Feature Extraction for Text Categorization”, Speech and Natural Language, ss.212 217.
  • Liu, R. Ve Lu, Y. (2002) “Incremental Context Mining for Adaptive Document Classification”, Eighth International Conference on Knowledge Discovery and Data Mining, ss.599 604.
  • Manevitz, L. ve Yousef, M. (2007) “One-Class Document Classification via Neural Networks”, Neurocomputing, 70, ss.1466 1481.
  • Marquardt, D.W. (1963) “An Algorithm for Least-Squares Estimation of Nonlinear Parameters”, Journal of the Society for Industrial and Applied Mathematics, 11, ss.431 441.
  • Nabiyev, V. (2005), Yapay Zekâ, Genişletilmiş İkinci Baskı, Seçkin Yayıncılık, Ankara.
  • Özgür, L., Güngör, T. ve Gürgen, F. (2004) “Adaptive Anti-Spam Filtering for Agglutinative Languages : A Special Case for Turkish”, Pattern Recognation Letters, 25, ss.1819 1831.
  • Sağıroğlu, Ş., Beşdok, E. ve Erler, M. (2003), Mühendislikte Yapay Zeka Uygulamaları 1 – Yapay Sinir Ağları, Ufuk Yayınevi, Kayseri.
  • Sahami, M., Yusufali, S. ve Baldonado, M. (1997) “Real-time Full-text Clustering of Networked Documents”, National Conference on Artificial Intelligence, ss.845 851.
  • Sandler, M. (2005) “On the Use of Linear Programming for Unsupervised Text Classification”, ACM–SIGKDD Conference on Knowledge Discovery and Data Mining, ss.256 264.
  • Schalkoff, J.R. (1997), Artificial Neural Network, McGraw-Hill, OH.
  • Song, W., Li, C. H. ve Park, S.C. (2009) “Genetic Algorithm for Text Clustering Using Ontology and Evaluating the Validity of Various Semantic Similarity Measures”, Expert Systems with Applications, 36(5), ss.9095 9104.
  • Tsoukalas, L.H. ve Uhrig, R.E. (1997), Fuzzy and Neural Approaches in Engineering, John Wiley & Sons Inc.
  • Vapnik, V.(2000), The Nature of Statistical Learning Theory, Springer, New York.
  • Yu, B., Xu, Z. ve Li, C. (2008) “Latent Semantic Analysis For Text Categorization Using Neural Network”, Knowledge-Based Systems, 21, ss.900 904.
  • Zamir, O. ve Etzioni, O. (1998) “Web Document Clustering:a Feasibility Demonstration”, 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ss. 46 54.
  • Zayani, R., Bouallegue, R. ve Roviras, D. (2008) “Levenberg-Marquardt Learning Neural Network for Adaptive Pre-0 Distortion for Time- Varying hpa with Memory in OFDM Systems”, 16. European Signal Processing Conference, ss.758 765
There are 38 citations in total.

Details

Primary Language tr; en
Journal Section Makaleler
Authors

Yavuz Kılağız This is me

Ahmet Baran This is me

Publication Date August 12, 2010
Published in Issue Year 2009 Volume: 23 Issue: 4

Cite

APA Kılağız, Y., & Baran, A. (2010). BİLGİ EDİNME HAKKI YASASI ÇERÇEVESİNDE YAPILAN ELEKTRONİK BAŞVURULARIN YAPAY SİNİR AĞLARI İLE SINIFLANDIRMASI. Atatürk Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 23(4), 27-41.

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