Medical Fuzzy-Expert System for Assessment of the Degree of Anatomical Lesion of Coronary Arteries.

Background: Today, cardiovascular diseases cause 47% of all deaths among the European population, which is 4 million cases every year. In Ukraine, CAD accounts for 65% of the mortality rate from circulatory system diseases of the able-bodied population and is the main cause of disability. The aim of this study is to develop a medical expert system based on fuzzy sets for assessing the degree of coronary artery lesions in patients with coronary artery disease.

Assessing the utility value of Hucul horses using classification models, based on artificial neural networks.

The aim of this study was to evaluate factors influencing the performance of Hucul horses and to develop a prediction model, based on artificial neural (AI) networks for predict horses' classification, relying on their performance value assessment during the annual Hucul championships. The Feedforward multilayer artificial neural networks, learned using supervised methods and implemented in Matlab programming environment were applied. Artificial neural networks with one and two hidden layers with different numbers of neurons equipped with a tangensoidal transition function, learned using the Levenberg-Marqiuardt method, were applied for the analysis.