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Implementation of Resonant Inverter with in-Built Boost Converter

R. Dharani Krishna, K. Dhanalakshmi

Abstract


The objective of this work is to develop and implement a decision support system for an automated diagnosis and classification of mammogram images. The proposed method distinguishes two categories namely normal and abnormal (benign and malignant). For the each pre-processed mammogram images, 12 features are extracted. A decision making system for image classification is constructed by integrating fuzzy rules and decision tree called fuzzy decision tree (FDT). For classifying the mammogram images, hybrid fuzzy decision tree support system is used. The performance of the hybrid fuzzy decision tree support system is improved and it provides higher classification efficiency than other techniques.

Keywords


Fuzzy Decision Tree, Image Mining, Feature Extraction, Classification

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References


Altam, E. I., Macro, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparison using linear discriminant analysis and neural networks. Journal of Banking and Finance, 18, 505–529.

Au, W.-H., & Chan, K. C. C. (2001). Mining fuzzy association rules in a bank-account Database. IEEE Transactions on Fuzzy System, 11, 238–248.

Bandyopadhyay, S., & Maulik, U. (2002). An evolutionary technique based on KMeans algorithm for optimal clustering in Rn. Information Sciences, 146(1–4), 221–237.

Bonneville, M., Meunier, J., Bengio, Y., & Soucy, J. P. (1998). Support vector machines for improving the classification of brain PET images. In Proceedings of the SPIE medical imaging symposium, San Diego, CA (Vol. 3338, pp.264–273).

Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Belmont, CA: Wadsworth.

Carvalho, D. R., & Freitas, A. A. (2004). A hybrid decision tree/genetic algorithm method for data mining. Information Sciences, 163(1–3), 13–35.

Chang, P. C., & Liu, C. H. (2008). A TSK type fuzzy rule based system for stock price prediction. Expert Systems with Applications, 34(1), 135–144.

Chang, P. C., Liu, C. H., & Wang, Y. W. (2005). A hybrid model by clustering and evolving fuzzy rules for sale forecasting in printed circuit board industry. Decision Support Systems, 42(3), 1254–1269.


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