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3 2010

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APPLICATIONS OF LOGIC IN MEDICINE

100%

Jankowska D. , Milewska A. J. , Górska U.

Studies in Logic, Grammar and Rhetoric

2010

nr 21(34)

7-24

This paper presents the importance of logic in the medical field. Efficient and proper medical work is difficult without the knowledge of the rules of logic. Therefore, the paper will consider ways of implementing both classical logic and non-classical approach, e.g. temporal and fuzzy logic. The thesis will be supported by numerous examples illustrating how indispensable is the cognition of logic and showing how applying logic can effectively improve work in medicine.

THE USE OF FUZZY LOGIC IN ESTIMATING THE RISK OF GRANTING OF A RENEWABLE CREDIT LIMIT (Zastosowanie logiki rozmytej do oceny ryzyka przyznania limitu odnawialnego)

100%

Konopka P.

Optimum. Studia Ekonomiczne

2010

nr 2 (46)

169-182

In his article the author presents the model of risk assessment concerning granting of a renewable credit limit. For this purpose the notions of fuzzy set theory and fuzzy logic have been utilized. The presented model is based on two linguistic input variables being risk factors, and one linguistic variable describing the risk itself. Based on expert knowledge and assumptions concerning the credit policy of the bank, the analyst assigned relevant fuzzy sets to each variable and determined their limits. Having obtained in this way risk factors, one can construct the fuzzy rules base. In order to determine the fuzzy value describing the risk, defuzzyfication is used by means of the centre of gravity method. The final part of the paper presents a proposal to generalize the model utilizing a bigger number of risk factors and it indicates further possible research trends.

EFFECTIVENESS OF EXPERTS' SYSTEM AND ARTIFICIAL INTELIGENCE IN FORECASTING BANCRUPTCY OF COMPANIES (Skutecznosc systemu eksperckiego i sztucznej inteligencji w prognozowaniu upadlosci firm)

100%

Korol T.

Pieniądze i Więź

2010

tom 13

nr 1(46)

155-164

This article is about the prediction of bankruptcy of companies in Poland. The author compares two methods of forecasting the risks of bankruptcy of companies: artificial neural networks and fuzzy logic. In the conducted research he used data of 185 companies listed on the Warsaw Stock Exchange. This population of firms was divided into learning and testing set data. Each company has been analyzed using the absolute values of 14 financial ratios and the dynamics of change of these ratios. The author's developed models of fuzzy logic are characterized by high efficiency. These studies are the first attempt to use fuzzy logic to predict the bankruptcy of companies in Poland and one of the first in the world. Obtained results demonstrate the great potential of this method.