• East Asian mathematical journal
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• 제23권2호
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• pp.151-158
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• 2007

In this paper we introduce the concept of fuzzy ${\beta}-compact^*$ spaces. Besides giving some interesting properties of fuzzy ${\beta}-compact^*$ spaces we also give a characterization on fuzzy $\beta$-compact spaces by making use of newly introduced concept of fuzzy $\beta$-filters.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권4호
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• pp.314-318
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• 2010

Predicting Alpha-helicies, Beta-sheets and Turns of a proteins secondary structure is a complex non-linear task that has been approached by several techniques such as Neural Networks, Genetic Algorithms, Decision Trees and other statistical or heuristic methods. This project introduces a new machine learning method by combining Bayesian Inference with offline trained Multilayered Perceptron (MLP) models as the likelihood for secondary structure prediction of proteins. With varying window sizes of neighboring amino acid information, the information is extracted and passed back and forth between the Neural Net and the Bayesian Inference process until the posterior probability of the secondary structure converges.