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Author

  • Arzamastsev, Alexander A. (3)
  • Troitzsch, Klaus G. (3)
  • Zenkova, Natalia (3)
  • Kryuchin, Oleg V. (2)
  • Sletkov, Denis V. (1)

Keywords

  • artificial neural networks (2)
  • adaptive resonance theory (1)
  • blood analysis (1)
  • classification (1)
  • computer clusters (1)
  • gradient method of training weight coefficients (1)
  • information system (1)
  • mathematical model (1)
  • parallel calculations (1)
  • social object (1)
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Institute

  • Fachbereich 4 (3)
  • Institut für Wirtschafts- und Verwaltungsinformatik (3)

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Development of a technology of designing intelligent information systems for the estimation of social objects (2011)
Zenkova, Natalia ; Arzamastsev, Alexander A. ; Troitzsch, Klaus G.
The estimation of various social objects is necessary in different fields of social life, science, education, etc. This estimation is usually used for forecasting, for evaluating of different properties and for other goals in complex man-machine systems. At present this estimation is possible by means of computer and mathematical simulation methods which is connected with significant difficulties, such as: - time-distributed process of receiving information about the object; - determination of a corresponding mathematical device and structure identification of the mathematical model; - approximation of the mathematical model to real data, generalization and parametric identification of the mathematical model; - identification of the structure of the links of the real social object. The solution of these problems is impossible without a special intellectual information system which combines different processes and allows predicting the behaviour of such an object. However, most existing information systems lead to the solution of only one special problem. From this point of view the development of a more general technology of designing such systems is very important. The technology of intellectual information system development for estimation and forecasting the professional ability of respondents in the sphere of education can be a concrete example of such a technology. Job orientation is necessary and topical in present economic conditions. It helps tornsolve the problem of expediency of investments to a certain sphere of education. Scientifically validated combined diagnostic methods of job orientation are necessary to carry out professional selection in higher education establishments. The requirements of a modern society are growing, with the earlier developed techniques being unable to correspond to them sufficiently. All these techniques lack an opportunity to account all necessary professional and personal characteristics. Therefore, it is necessary to use a system of various tests. Thus, the development of new methods of job orientation for entrants is necessary. The information model of the process of job orientation is necessary for this purpose. Therefore, it would be desirable to have an information system capable of giving recommendations concerning the choice of a trade on the basis of complex personal characteristics of entrants.
Simulating medical objects simulation using an artificial neural network whose structure is based on adaptive resonance theory (2011)
Kryuchin, Oleg V. ; Arzamastsev, Alexander A. ; Zenkova, Natalia ; Troitzsch, Klaus G. ; Sletkov, Denis V.
This paper describes artificial neural networks which are based on the adaptive resonance theory. The usage of these artificial neural networks for classification tasks is presented. The example uses is the classification of patient health from the results of general blood analysis.
Simulating social objects with an artificial neural network using a computer cluster (2011)
Kryuchin, Oleg V. ; Arzamastsev, Alexander A. ; Troitzsch, Klaus G. ; Zenkova, Natalia
This paper describes results of the simulation of social objects, the dependence of schoolchildren's professional abilities on their personal characteristics. The simulation tool is the artificial neural network (ANN) technology. Results of a comparison of the time expense for training the ANN and for calculating the weight coefficients with serial and parallel algorithms, respectively, are presented.
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