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Designing and developing computer-assisted image processing techniques to help doctors improve their diagnosis has received considerable interests over the past years. In this paper, we used the kolmogorov complexity model to analyze the CT images of the healthy liver and multiple daughter hydatid cysts. Before the complexity characteristic calculating, the image preprocessing methods had been used for image standardization. From the kolmogorov complexity model, complexity characteristic were calculated in order to quantify the complexity, between healthy liver and multiple daughter hydatid cysts. Then we use statistical method to analyze the complexity characteristic of those two types of images. Our preliminary results show that the complexity characteristic has statistically significant (p<0.05) to analyze these two types CT images, between the healthy liver and the multiple daughter hydatid cysts. Furthermore, the result leads us to the conclusion that the kolmogorov complexity model could use for analyze the hydatid disease and will also extend the analysis the other lesions of liver.
In this study, the technical indicators are used in
forecasting whether stock prices will rise, fall or will be constant at the
following day. The indicators are generated by taking into account the daily
stock returns. If the daily stock returns are positive, the indicator is coded as “+1”; if the daily stock returns are
constant, the indicator is
coded as “0” and at least if the daily stock returns are negative, the indicator is coded as “-1”. These indicator values express the
dependent variable of ordered choice models which independent variables are
technical indicators. The ordered choice models are applied to all of the
stocks of ISE (Istanbul Stock Exchange).