INFORMATION TECHNOLOGY OF DIAGNOSIS MODELS SYNTHESIS BASED ON PARALLEL COMPUTING

A. Oliinyk, S. Subbotin, S. Skrupsky, V. Lovkin, T. Zaiko

Abstract


Context. The problem of diagnosis models synthesis in the big data processing based on parallel computing is solved. The object of the research is the process of diagnosis models synthesis. The subject of the research are the methods and information technologies for diagnosis models synthesis.

Objective. The research objective is to develop diagnosis models synthesis information technology.

Method. The paper deals with information technology of diagnosis models synthesis which is a set of diagrams graphically describing structural elements of the system as well as the behavioral aspects of their interaction at various stages of diagnostics objects models construction. The developed information technology enables to perform the construction of distributed diagnostics systems where computationally complex stages of diagnosis models synthesis are performed on high-performance server equipment, which makes it possible to significantly increase the practical threshold for using diagnostics systems in the processing of big data sets for solving of the tasks of training sample data reduction, rules extraction, diagnosis models construction and retraining.

Results. The software which implements the proposed information technology and allows to synthesize diagnosis models based on the given data samples has been developed.

Conclusions. The conducted experiments have confirmed the proposed information technology operability and allow to recommend it for solving the problems of big data processing for technical and biomedical diagnostics in practice. The prospects for further researches may include the modification of the developed information technology by introducing of other methods of diagnosis models synthesis.

Keywords


Data sample; diagnosis; rule extraction; feature selection; parallel computing; model synthesis.

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References


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Oliinyk A. O., Skrupsky S. Yu., Subbotin S. A. Experimental Investigation with Analyzing the Training Method Complexity of Neuro-Fuzzy Networks Based on Parallel Random Search, Automatic Control and Computer Sciences, 2015, Vol. 49, Issue 1, pp. 11–20. DOI: 10.3103/S0146411615010071.

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GOST Style Citations


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3. Encyclopedia of machine learning / [eds. C. Sammut, G. I. Webb]. – New York: Springer, 2011. – 1031 p. DOI: 10.1007/978-0-387- 30164-8.

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7. Subbotin S. The Dimensionality Reduction Methods Based on Computational Intelligence in Problems of Object Classification and Diagnosis / S. Subbotin, A. Oliinyk // Recent Advances in Systems, Control and Information Technology. Advances in Intelligent Systems and Computing. – 2017. – Vol. 543. – P. 11– 19. DOI: 10.1007/978-3-319-48923-0_2.

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34. Making database systems usable / [H. V. Jagadish, Adriane Chapman, Aaron Elkiss, et al] // International Conference on Management of Data. – 2007. – P. 13–24. DOI: 10.1145/1247480.1247483

35. Sumathi S. Fundamentals of Relational Database Management Systems / S. Sumathi, S. Esakkirajan. – Springer-Verlag Berlin Heidelberg, 2007. – 792 p. DOI: 10.1007/978-3-540-48399-1

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37. Subbotin S. Individual prediction of the hypertensive patient condition based on computational intelligence / S. Subbotin, A. Oliinyk, S. Skrupsky // Information and Digital Technologies : International Conference IDT’2015, Zilina, 7–9 July 2015 : proceedings of the conference. – Zilina : Institute of Electrical and Electronics Engineers, 2015. – P. 336–344. DOI: 10.1109/ DT.2015.7222996.

38. Oliinyk A. O. Experimental Investigation with Analyzing the Training Method Complexity of Neuro-Fuzzy Networks Based on Parallel Random Search / A. O. Oliinyk, S. Yu. Skrupsky, S. A. Subbotin // Automatic Control and Computer Sciences. – 2015. – Vol. 49, Issue 1. – P. 11–20. DOI: 10.3103/ S0146411615010071.

39. Intelligent information technology of automated diagnostic and pattern recognition systems development: Monograph / [S. A Subbotin, A. Oliinyk, Ye. Gofman et all]. – Kharkov : “Company Smith”, 2012. – 317 p. (In russian).

40. Smith S. Genetic and Evolutionary Computation: Medical Applications / S. Smith, S. Cagnoni. – Chicehster : John Wiley & Sons, 2011. – 250 p. DOI: 10.1002/9780470973134.

41. Michael Creel Multi-core CPUs, Clusters, and Grid Computing: A Tutorial / Michael Creel, William L. Goffe // Computational Economics, 2008. – Volume 32, Issue 4. – P. 353–382. DOI: 10.1007/s10614-008-9143-5

42.Kshitij Gupta A study of Persistent Threads style GPU programming for GPGPU workloads / Kshitij Gupta, Jeff A. Stuart, John D. Owens // Parallel Computing. – 2012. – P. 1–14. DOI: 10.1109/InPar.2012.6339596

43. NVIDIA CUDA Compute Unified Device Architecture 5.5. Santa Clara : NVIDIA Corporation, 2014. – 117 p.




DOI: https://doi.org/10.15588/1607-3274-2017-3-16



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