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According to the Ministry of Labor, Health and Social Affairs of Georgia a new health management system has to be introduced in the nearest future. In this context arises the problem of structuring and classifying documents containing all the history of medical services provided. The present work introduces the instrument for classification of medical records based on the Georgian language. It is the first attempt of such classification of the Georgian language based medical records. On the whole 24.855 examination records have been studied. The documents were classified into three main groups (ultrasonography, endoscopy, and X-ray) and 13 subgroups using two well-known methods: Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results obtained demonstrated that both machine learning methods performed successfully, with a little supremacy of SVM. In the process of classification a “shrink” method, based on features selection, was introduced and applied. At the first stage of classification the results of the “shrink” case were better; however, on the second stage of classification into subclasses 23% of all documents could not be linked to only one definite individual subclass (liver or binary system) due to common features characterizing these subclasses. The overall results of the study were successful.

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Quantum effects in nanostructured magnetic solid state materials open the new ways for preparing the novel electromagnetic devices with unique characteristics. At the same the electron spin based quantum effects are fully determined of formation and growing properties of molecular clusters of these solid state materials which are united in plane and volume structures also according their quantum (elementary particle interactions) properties.

Selection, mutation and crossover are the parameters that stipulate the evolution process. These three methods are used by the genetic algorithms. We've tried to apply genetic algorithms in mining industry, particularly in concentrating the manganese. It's necessary to optimize several parameters for that. In Adaptive genetic algorithms were applied Hamming weight and Hamming distance for selection and crossover. By Hamming Distance we search in chromosomes the similarity combinations and define the crossover point.