admin

Issue 4 (194), article 5

DOI:https://doi.org/10.15407/kvt194.04.079

Kibern. vyčisl. teh., 2018, Issue 4 (194), pp.

Vovk M.I., PhD (Biology), Senior Researcher,
Head of Bioelectrical Control & Medical Cybernetics Department
e-mail: vovk@irtc.org.ua; imvovk3940@gmail.com

International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
Glushkov ave., 40, Kiev, 03680 GSP, Ukraine

INFORMATION TECHNOLOGY OF MOVEMENT CONTROL. EVOLUTION OF SYNTHESIS AND DEVELOPMENT PROSPECTS

Introduction. Movement training is one of the main factors to mobilize person’s reserves for movement restoration

The purpose of the article is to consider the theoretical and technological bases of the evolution of synthesis of biotechnical systems for motion control, to show the role of new information technologies and means of digital medicine in the synthesis of systems for personal control of movements for the restoration of motor and speech functions that are affected by pathology.

Results. The evolution of the synthesis of technologies of bioelectric control of human movements is given in the analysis of several generations of programmed muscle electrostimulators such as MIOTON, MIOSTIMUL and the new class of digital medicine devices TRENAR®. The main feature of these devices is the use of specially processed electromyographic (EMG) signals as programs to control signals of electrical stimulation and feedback. The principles, criteria, methods, programs, on the basis of which the innovative technology of personal training / restoration of movements TRENAR® is synthesized are considered. The computer program-apparatus complex “PROMOVА-1” is presented, that implements new technology of personal reconstruction of oral speech after a stroke based on the original techniques of fine motor hand training. Prospective studies are aimed at the synthesis of mobile informational and consulting assistance to the doctor in diagnosing the deficit of motor and speech functions and the formation of individual rehabilitation plans; at the synthesis of technologies to control muscle activity coordination during the performance of coordinated movements and rehabilitation treatment of posture defects.

Conclusion. Current researches are aimed at the further development of such priority areas in medicine as an individual approach to treatment, digital medicine, mobile health based on new information technologies.

Keywords: bioelectric control, movement, speech, coordination, posture, personal rehabilitation, methods, programs, myelectrostimulation, digital medicine.

Download full text (ua)!

REFERENCES

1. Inventor’s certificate 190525 USSR. The method of motor control / L. Aleev, S. Bunimovich. No 1019769/31-16; claimed 26.06.65; published 29.12.66, Bull. No 2. (in Russian).

2. Aleev L.S. Bioelectrical system “Mioton” and motor functions of a person. Bull. of AS of USSR. 1969. Iss. 4. P. 70–80 (in Russian).

3. Aleev L.S., Vovk M.I., Gorbanev V., Shevchenko A. «Mioton» in motor control. Kiev: Nauk. dumka, 1980. 142 p. (in Russian).

4. Inventor’s certificate 321 245 USSR. The method of motor control of a person / L. Aleev, S. Bunimovich, M. Vovk, V. Gorbanev, A. Shevchenko. No1455753/31-16; claimed 22.06.1970; registered 03.09.1971. (in Russian).

5. Inventor’s certificate 929 054 USSR. Multichannel device for adaptive bioelectrical motor control of a person / L. Aleev, M. Vovk, V. Goranev, A. Shevchenko. No 2428608/28-13; claimed 13.12.76; published 23.05.82, Bull. № 19 (in Russian).

6. Inventor’s certificate 976 952 USSR Multichannel device for adaptive bioelectrical motor control of a person / L. Aleev, M. Vovk, V. Goranev, A. Shevchenko. No 2436412/28-13; claimed 03.01.77; published 30.11.82, Bull. №44. (in Russian).

7. Bioelectrically controlled electric stimulator of human muscles: United States Patent 4,165,750 Aug. 28, 1979.

8. Gritsenko V.I., Kotova A., Vovk M et.al. Information technology in Biology and Medicine. Lecture course. Kyiv: Nauk. Dumka, 2007. 382 p. (in Ukrainian).

9. Vovk M.I. Bioinformatic technology of motor control of a person. Kibernetika i vyčislitelnaâ tehnika. 2010. Iss. 161. P. 42–52 (in Russian).

10. “Trenar” — innovative technology for motor restoration. Materials of the International scientific — practical forum «The Science and Business — a basis of development of economy». Dnepropetrovsk, 2012. P. 204.

11. Vovk M.I. Bioinformatic technology of motor control as the direction of biological and medical cybernetics. Kibernetika i vyčislitelnaâ tehnika. 2013. № 174. P. 56–70 (in Russian).

12. Vovk M.I., Galyan Ye.B. Restoring of motor component of speech based on muscle movement control. Theoretical grounding. Kibernetika i vyčislitelnaâ tehnika. 2012. № 167. P. 51–60 (in Russian).

13. The way to treat speech desorders: pat. UA, A61N 1/36, no. 111388, claimed 03.06.2014, publshed 25.04.2016, Bulletin no 18 (in Ukrainian).

14. Vovk M.I., Galyan Ye.B. Personаlized biotechnical system to restore speech. Kibernetika i vyčislitelnaâ tehnika.. 2015. Iss. 179. P. 5–19 (in Russian).

15. Vovk M.I., Galyan Ye.B. Organization of Intelligent Hand Movements Control to Restore Speech. Kibernetika i vyčislitelnaâ tehnika. 2016. Iss. 184. P. 25–43 (in Russian).

16. Galyan Ye.B. Specialized software module of speech rehabilitation technology, architecture and functional interaction of its components. Control Systems and Machines. 2014. Iss. 6. P. 52–58 (in Russian).

17. Vovk M.I., Peleshok S.R., Galyan Ye.B. Ovcharenko M.A. The method of assessment of motor and sensory speech disorders. Collected papers of scientific-information center “Knowledge” based on XІ International correspondence scientific-practical conference: «The development of science in the XXI century», part 3. Kharkiv: collected papers. Donetsk: Scientific-information center “Knowledge”, 2016. P. 70–76 (in Russian).

18. Vovk M.I., Kutsyak O.A., Lauta A.D., Ovcharenko M.A. Information Support of Researches on the Dynamics of Movement Restoration After the Stroke. Kibernetika i vyčislitelnaâ tehnika.. 2017. № 3 (189). P. 61–78 (in Ukrainian).

19. Vovk M.I., Galyan Ye.B., Kutsyak O.A., Lauta A.D. Formation of individual complex of control actions for motor and speech rehabilitation after a stroke. Kibernetika i vyčislitelnaâ tehnika. 2018. № 3 (193). P. 43–63. (in Ukrainian).

Received 14.09.2018

admin

Issue 4 (194), article 4

DOI:https://doi.org/10.15407/kvt194.04.061

Kibern. vyčisl. teh., 2018, Issue 4 (194), pp.

Kozak L.M., DSc (Biology), Senior Researcher,
Leading Researcher of the Medical Information Systems Department
e-mail: lmkozak52@gmail.com

Kovalenko A.S., DSc (Medicine), Professor,
Head of the Medical Information Systems Department
e-mail: askov49@gmail.com

Krivova O.A., Researcher of the Medical Information Systems Department
e-mail: ol.kryvova@gmail.com

Romanyuk O.A., Junior Researcher of Medical Information Systems Department
e-mail: ksnksn7@gmail.com

International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
Glushkov ave., 40, Kiev, 03187, Ukraine

DIGITAL TRANSFORMATION IN MEDICINE: FROM FORMALIZED MEDICAL DOCUMENTS TO INFORMATION TECHNOLOGIES OF DIGITAL MEDICINE

Introduction. According to the Concept of Ukraine`s Digital Economy and Society Development in 2018-2020, the key components of “digitalization” are the development of digital infrastructure — broadband Internet throughout Ukraine, and the promotion of digital transformations in various sectors of the economy and society, including medicine.

The purpose of the paper is to analyze the stages of digital transformation in medicine and the results of authors and their colleagues of the MIS department for the development of information technologies of digital medicine.

Results. A generated model of digital transformation in medicine is presented and several main stages of this transformation are highlighted: І — digital transformation of primary medical information; ІІ — development of support systems for the diagnostic and treatment process; ІІІ — development of technologies and systems for supporting the physicians` activities with digital information; IV — mobile medicine; V — the digital medicine globalization. The method of determining the markers of the functional state of the cardiovascular system based on mathematical models of forecasting and classification with the use of Data Mining is proposed. The method allows detecting and determining the prognostic values of ECG parameters of the CVS functional state for different groups of patients. The developed IT for supporting the processes of receiving, transmitting and storing digital medical images is aimed at ensuring the effective operation of a physician with digital information from various sources: functional diagnostic complexes, digital medical data storage and images using Picture Archiving and Communication Systems (PACS) and cloud technologies . The proposed telemedicine systems theory including the formulated principles of organizing these systems, criteria and methods for analyzing digital medical data has been implemented for elaborating and functioning the Telemedicine Centre. It enables to cover the population in more than 20 Ukraine`s regions with qualified medical assistance.

Conclusions. The digital transformation in medicine like any new process takes place with a gradual complication of tasks, methods and means of their implementation: from formalization of primary medical information to improvement of methods of its analysis, transfer and storage to improve the quality of medical care for patients at any point of the world.

Keywords: digital transformation in medicine, formalized medical records, Data Mining, IT for assessing human state and physiological systems` state, telemedicine, m-medicine.

Download full text!

REFERENCES

1. About the conceptualization of the concept of the development of the digital economy of Ukraine and 2018–2020 on the basis of the plan set for the project: Disposition of the Government of Ukraine. URL: http://www.me.gov.ua/Documents/ (Last accessed: 06.07.18) (in Ukranian).

2. The Nine Elements of Digital Transformation. URL:  https://sloanreview.mit.edu/article/the-nine-elements-of-digital-transformation/?social_token=d65abc6db70ba459408562abb8de32bc &utm_source= facebook&utm_medium=social&mmmmmt (Last accessed: 27.06.18)

3. Medical information system. Kyiv: Nauk. Dumka, 1975. 508 p. (in Russian).

4. A. p. № 2002032456 Ukraine MKI. Method for the diagnosis of local changes in the myocardium state. V.A. Petrukhin, V.N. Mamaev, A.S. Kovalenko, T.V. Petrukhina, V.A. Shumakov. Announced 15.01.2003; publ. 03.28.2003. (in Russian).

5. Provotar A.I., Vasilik P.V. Model waves and interaction: Theoretical and applied as-pects. Kyiv: Nauk. Dumka, 2014. 296 p (in Russian).

6. Vasilik P.V., Lychak M.M. Possible interactions in the Solar System and the synchronism of cyclical variations in solar activity with climatic changes on Earth. Geophysical journal. 2012. V. 34, No. 1. P. 138–158. (in Russian).

7. Vasilik P.V., Vasilega A.G., Chekaylo M.A. Influence of disturbances of space environmental factors on the accident rate of the objects of ground infrastructure and the accident rate on transport. Kibernetika i vyčislitel`naâ tehnika, 2011, Issue 166. P. 74–84 (in Russian).

8. Kozak L.M., Lukashenko M.V. The use of information models and integral assessments of the functional state of students for the formation of programs of psychological support. Integrative anthropology. 2008. №2 (12). P. 51–57 (in Russian).

9. Kozak L.M., Lukashenko M.V. Monitoring and correction students ’functional state. Kibernetika i vyčislitel`naâ tehnika. 2014. Issue 176. P. 74–84.

10. M.L. Kochina, L.M. Kozak, A.S. Yevtushenko Analysis of changes in the factor structures of indicators of the functional state of a person with different types of visual load. Bulletin of problems of biology and medicine. 2013, Iss. 1, Vol. 1 (98), pp. 41–45 (in Russian).

11. Evtushenko A.S., Kozak L.M., Kochina M.L. Evaluation of the relationship structure be-tween the functional indicators of operators in visual work using factor models. Kiber-netika i vyčislitel’naâ tehnika. 2016. Vol. 185. P. 60–76 (in Russian).

12. Rogozinskaya N.S., Kozak L.M. Information support of technology for automated moni-toring of the health of the population. Kibernetika i Sistemnyj Analiz. 2013. № 6. P. 162-173 (in Russian).

13. Rogozinskaya N.S., Kozak L.M. Complex indicators for the analysis of causal mortality of the population. Clinical informatics and telemedicine. 2013. Vol. 9, Iss. 10. P. 108–116 (in Russian).

14. Rogozinskaya N.S., Kozak L.M. Information technology research of the state of health of the population of the region. Upravlâûŝie sistemy i mašiny. 2013. № 6. P. 59–67 (in Russian).

15. Krivova O.A., Kozak L.M. Comprehensive assessment of regional demographic devel-opment. Kibernetika i vyčislitel`naâ tehnika. Issue 182. 2015. P. 70–84 (in Russian).

16. Krivova, OA, Tchaikovsky, I.A., Kalnish, VV, Kozak, L.M. Vidbіr informative shows the variability of the rhythm of the heart – the markers of the reaction to his stimulation. Medical informatics and engineering. 2016. No. 2. pp. 37–44 (in Russian).

17. Tchaikovsky I.A. The concept of multilateral analysis of the electrocardiogram using portable electrocardiographs as part of a preventive medical examination. Preventive medicine. 2014. No. 17 (2). P. 42–48 (in Russian).

18. Sposib Universalnoyi Balnoyi Otcinky EKG: Budnik M.M., Staryska G.A., Tchaikovsky І.A. Pat. 104827 Ukraine, IPC: A61B 5/0402, A61B 5/0205; declare 07.13.2015; publ. 02/25/2016, Bul. No. 4.

19. EN ISO 12052: 2011. Health informatics. Digital work, including workflow and data management URL: http://iso.org. (Last accessed: 23.01.18)

20. Romanyuk O. A., Kovalenko A.S., Kozak L.M. Information support interoperability of instrumental studies and long-term storage of digital medical imaging in health care sys-tem. Kibernetika i vyčislitel`naâ tehnika. 2016. Iss. 184. P. 56–71 (in Russian).

21. Kovalenko A.S., Kozak L.M., Romanyuk O.A. Information technology of digital medi-cine. Kibernetika i vyčislitel`naâ tehnika. 2017. №1(187). P.67–79. (in Russian).

22. Kovalenko A.S., Kozak L.M., Ostashko V.G. Telemedicine — the development of a sin-gle medical information space. Upravlâûŝie sistemy i mašiny. 2005. № 3. P. 86–92 (in Russian).

23. Gritsenko V.I., Kozak L.M., Kovalenko A.S., Pezenzali A.A., Rogozinskaya N.S., Ostashko V.G. Medical information systems as elements of a unified medical informa-tion space. Kibernetika i vyčislitel`naâ tehnika, 2013, Iss. 174. P 30-46 (in Russian).

24. Kovalenko O.S., Kozak L.M., Romaniuk O.O., Maresova T.A., Nenasheva L.V., Fyniak G.I. Mobile applications in the structure of modern medical information systems. Upravlâûŝie sistemy i mašiny, 2018, №4. P. 57-69.

 

Received 29.08.2018

admin

Issue 4 (194), article 3

DOI:https://doi.org/10.15407/kvt194.04.041

Kibern. vyčisl. teh., 2018, Issue 4 (194), pp.

Stepashko V.S., DSc (Engineering), Professor,
Head of Dep. for Information Technologies of Inductive Modeling
e-mail: stepashko@irtc.org.ua

International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
Acad. Glushkov av., 40, Kyiv, 03187, Ukraine

FORMATION AND DEVELOPMENT OF SELF-ORGANIZING INTELLIGENT TECHNOLOGIES OF INDUCTIVE MODELING

Introduction. Effective solution of control and decision-making tasks in complex systems should use the results of mathematical modeling. To construct adequate predictive models, many modern methods and tools are available which may be generally based on two principal approaches: theory-driven (deductive) and data-driven (inductive) ones. The data-driven methods are basic for solving typical tacks of data mining; they implement an inductive process of transition from particular data to models generalizing the data. Among all such methods, very notable are those being developed within the area of GMDH-based inductive modeling founded several decades ago by academician O.H. Ivakhnenko.

The purpose of this paper is analysing the background of the GMDH invention by Ivakhnenko and the evolution of model self-organization ideas, methods and tools during the half-century historical period of successful development of the inductive modeling methodology.

Results. Professor Ivakhnenko acquired broad knowledge in the areas of automatic control, engineering cybernetics and emerging neuroscience initiated by the idea of percep-tron. These were those prerequisites which helped Ivakhnenko to synthesize his original self-organizing approach to solving tasks of constructing models of objects and processes on the basis of experimental data. The paper tracks evolution of scientific ideas and views of Ivakhnenko and main achievements in development of GMDH during the period 1968-1997. Contributions of researchers from different countries to the GMDH modification and application are characterized. Results of further developments of inductive modeling meth-ods and tools in the ITIM department are presented and the most promising prospects of in-vestigations in this field are indicated.

Conclusions. Main prerequisites facilitating the creation of the GMDH by O.H. Ivakhnenko were analysed, basic fundamental, technological and applied achievements of the half-century development of inductive modeling both in Ukraine and abroad were characterized, as well as the most prospective ways of further research were formulated.

Keywords: mathematical modeling, data-driven modeling, model self-organization, GMDH, inductive modeling, noise-immune modeling, information technology, case study.

Download full text!

REFERENCES

1. Ivakhnenko A.G., Muller J.-A. Recent Developments of Self-Organizing Modeling in Prediction and Analysis of Stock Market. Microelectronics Reliability. 1997. No. 37. P. 1053–1072.

2. Anastasakis L., Mort N. The Development of Self-Organization Techniques in Modelling: A Review of the Group Method of Data Handling (GMDH). ACSE Research Report 813. The University of Sheffield, 2001. 39 p.

3. Snorek M., Kordik P. Inductive Modelling World Wide the State of the Art. Proc. of 2nd Int. Workshop on Inductive Modelling (Prague, 19—23rd of Sept., 2007) Prague, 2007. P. 302–304.

4. Stepashko V. Developments and Prospects of GMDH-Based Inductive Modeling. In: Advances in Intelligent Systems and Computing II: Selected Papers from the Intern. Conf. on Computer Science and Information Technologies, CSIT 2017, Lviv, Ukraine / N. Shakhovska, V. Stepashko, Editors. AISC book series, Vol. 689. Cham: Springer, 2018, pp. 474–491. https://doi.org/10.1007/978-3-319-70581-1_34

5. Ivakhnenko A.G. Electroautomatics. Kiev: Gostekhizdat UkrSSR, 1957. 452 p. (In Russian)

6. Ivakhnenko A.G., Petina N.V. Voltage stabilizers with combined control. Kiev: AS UkrSSR publisher, 1958. 247 p. (In Russian)

7. Ivakhnenko A.G. Engineering cybernetics. Kiev: Gostekhizdat UkrSSR, 1959. 432 p. (In Russian)

8. Ivakhnenko A.G., Lapa V.G. Cybernetic predicting devices. Kiev: Naukova dumka, 1965. 213 p. (In Russian)

9. Rosenblatt F. Principles of Neurodynamic: Perceptrons and the Theory of Brain Mechanisms. Washington: Spartan Books, 1962. 616 p.

10. Ivakhnenko A.G. Method of Group Using of Arguments as a Rival of Stochastic Approximation Method. Avtomatyka. 1968. No 3. P. 58–72. (In Ukrainian)

11. Ivakhnenko A.G. Group Method of Data Handling as a Rival of Stochastic Approximation Method. Soviet Automatic Control. 1968. No. 3. P. 43–55.

12. Ivakhnenko A.G. Heuristic Self-Organization in Problems of Automatic Control. Automatica (IFAC). 1970. No. 6. P. 207–219. https://doi.org/10.1016/0005-1098(70)90092-0

13. Ivakhnenko A.G. Polynomial theory of complex systems. IEEE Trans. Sys., Man and Cyb. 1971. 1, No 4. P. 364–378.

14. Ivakhnenko A.G. Heuristic self-organization systems in engineering cybernetics. Kiev: Tekhnika, 1971. 392 p. (In Russian)

15. Ivakhnenko A.G. Inductive method of self-organization of complex systems. Kiev: Naukova dumka, 1982. 296 p. (In Russian)

16. Madala H.R., Ivakhnenko A.G. Inductive Learning Algorithms for Complex Systems Modeling. London, Tokyo: CRC Press Inc., 1994. 384 p.

17. Ivakhnenko A.G., Zaichenko Yu.P., Dimitrov V.D. Decision making based on self-organization. Moscow: Sov. radio, 1976. 280 p. (In Russian)

18. Ivakhnenko A.G., Stepashko V.S. Noise-immunity of modeling. Kiev: Naukova dumka, 1985. 216 p. (In Russian)

19. Ivakhnenko A.G., Yurachkovskiy Yu.P. Modeling of complex systems from experimental data. Moscow: Radio i svyaz, 1987. 120 p. (In Russian)

20. Stepashko V.S. A Combinatorial Algorithm of the Group Method of Data Handling with Optimal Model Scanning Scheme. Soviet Automatic Control. 1981. 14(3). P. 24–28.

21. Stepashko V.S. A Finite Selection Procedure for Pruning an Exhaustive Search of Models. Soviet Automatic Control. 1983. 16(4). P. 88–93.

22. Shelud’ko O.I. GMDH Algorithm with Orthogonalized Complete Description for Synthesis of Models by the Results of a Planned Experiment. Soviet Automatic Control. 1974. 7(5). P. 24–33.

23. Yurachkovsky Yu.P. Convergence of Multilayer Algorithms of the Group Method of Data Handling. Soviet Automatic Control. 1981. 14(3). P. 29–34.

24. Yurachkovsky Yu.P. Restoration of Polynomial Dependencies Using Self-Organization. Soviet Automatic Control. 1981. 14(4). P. 17–22.

25. Ivakhnenko A.G. Long-term forecasting and control of complex systems. Kiev: Tekhnika, 1975. 311 p. (In Russian)

26. Ivakhnenko A.G., Karpinsky A.M. Computer-Aided Self-Organization of Models in Terms of the General Communication Theory (Information Theory). Soviet Automatic Control. 1982. 15(4). P. 7–15.

27. Stepashko V.S. Potential noise stability of modelling using the combinatorial GMDH algorithm without information regarding the noise. Soviet Automatic Control. 1983. 16(3). P. 15–25.

28. Stepashko V.S., Kostenko Yu.V. A GMDH Algorithm for Two-level Modeling of Multidimensional Cyclic Processes. Soviet Automatic Control. 1987. 20(4). P. 49–57.

29. Ivakhnenko A.G., Osipenko V.V., Strokova T.I. Prediction of Two-dimensional Physical Fields Using Inverse Transition Matrix Transformation. Soviet Automatic Control. 1983. 16(4). P. 10–15.

30. Ivakhnenko A.G. Inductive Sorting Method for the Forecasting of Multidimensional Random Processes and Events with the Help of Analogs Forecast Complexing. Pattern Recogn. and Image Analysis. 1991. 1(1). P. 99–108.

31. Ivakhnenko A.G. Kostenko Yu.V. System Analysis and Long-Term Prediction on the Basis of Model Self-organisation (OSA algorithm). Soviet Automatic Control. 1982. 15(3). P. 11–17.

32. Ivakhnenko A.G. Objective Computer Clasterization Based on Self-Organisation Theory. Soviet Automatic Control. 1987. 20(6). P. 1–7.

33. Vysotskiy V.N., Ivakhnenko A.G., Cheberkus V.I. Long Term Prediction of Oscillatory Processes by Finding a Harmonic Trend of Optimum Complexity by the Balance-of-Variables Criterion. Soviet Automatic Control. 1975. 8(1). P. 18–24.

34. Ivakhnenko A.G., Krotov G.I. A Multiplicative-Additive Nonlinear GMDH Algorithm with Optimization of the Power of Factors. Soviet Automatic Control. 1984. 17(3). P. 10–15.

35. Kocherga Yu.L. J-optimal Reduction of Model Structure in the Gauss-Markov Scheme. Soviet J. of Automation and Information Sciences. 1988. 21(4). P. 34–36.

36. Aksenova T.I., YurachkovskyYu.P. A Characterization at Unbiased Structure and Conditions of Their J-Optimality. Sov. J. of Automation and Information Sciences. 1988. 21(4). P.36–42.

37. Ivakhnenko A.G., Kovalchuk P.I., Todua M.M., Shelud’ko O.I., Dubrovin O.F. Unique Construction of Regression Curve Using a Small Number of Points — Part 2. Soviet Automatic Control. 1973. 6(5). P. 29–41.

38. Stepashko V.S. Asymptotic Properties of External Criteria for Model Selection Soviet Journal of Automation and Information Sciences. 21, No. 6. (1988). P. 84–92.

39. Aksenova T.I. Sufficient conditions and convergence rate using different criteria for model selection, Systems Analysis Modelling Simulation 1995. vol. 20, no. 1–2. P.69–78.

40. Ivakhnenko A.G., Ivakhnenko G.A., Mueller J.A. Self-Organization of Neuronets with Active Neurons. Pattern Recognition and Image Analysis. 1994. 4(4). P. 177–188.

41. Muller J.-A., Lemke F. Self-organizing data mining. An intelligent approach to extract knowledge from data. Berlin, Dresden: Libri BoD, 1999. 225 p.

42. Self-organizing methods in modeling: GMDH type algorithms / Ed. S.J. Farlow. New York, Basel: Marcel Decker Inc., 1984. 350 p.

43. Voss M.S., Xin Feng. A new methodology for emergent system identification using particle swarm optimization (PSO) and the group method of data handling (GMDH). Proc. of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann Publishers, 2002. (9 – 13th of July, 2002, New-York). New-York, 2002. P. 1227–1232.

44. Kondo T., Ueno J. Feedback GMDH-Type Neural Network Self-Selecting Optimum Neural Network Architecture and Its Application to 3-Dimensional Medical Image Recognition of the Lungs. Proc. of the II Intern. Workshop on Inductive Modelling IWIM-2007 (Prague, 19-23rd of Sept. 2007) Prague, 2007. P. 63–70.

45. Jirina M., Jirina M. jr. Genetic Selection and Cloning in GMDH MIA Method. Proc. of the II Intern. Workshop on Inductive Modelling IWIM 2007 (Prague, 23–26th of Sept., 2007) Prague, 2007. P. 165–171.

46. Lytvynenko V., Bidyuk P., Myrgorod V. Application of the Method and Combined Algorithm on the Basis of Immune Network and Negative Selection for Identification of Turbine Engine Surging. Proc. of the II Intern. Conf. on Inductive Modelling ICIM-2008 (Kyiv, 15–19th of Sept. 2008). Kyiv, 2008. P. 116–123.

47. Kordik P. Fully automated knowledge extraction using group of adaptive model evolution: PhD thesis. Prague: CTU, 2006. 150 p.

48. Oh S.K., Pedrycz W., Park H.S. Multi-layer hybrid fuzzy polynomial neural networks: a design in the framework of computational intelligence. Neurocomputing. 2005. 64. P. 397–431. https://doi.org/10.1016/j.neucom.2004.08.001

49. Zaychenko Yu. The Investigations of Fuzzy Group Method of Data Handling with Fuzzy Inputs in the Problem of Forecasting in Financial Sphere. Proc. of the II Intern. Conf. on Inductive Modelling ICIM-2008. (Kyiv, 15–19th of Sept., 2008). Kyiv, 2008. P. 129–133.

50. Bodyanskiy Ye., Vynokurova O., Teslenko N. Cascade GMDH-Wavelet-Neuro-Fuzzy Network. Proc. of the IV Intern. Workshop on Inductive Modelling IWIM-2011 (Kyiv-Zhukyn, 4–11th of July, 2011). Kyiv-Zhukyn, 2011. P. 16–21.

51. Voytyuk I., Dyvak M., Spilchuk V. The Method of Structure Identification of Macromodels as Difference Operators Based on the Analysis of Interval Data and Genetic Algorithm. Proc. of the IV Intern. Workshop on Inductive Modelling IWIM-2011 (Kyiv-Zhukyn 4–11th of July, 2011). Kyiv-Zhukyn, 2011. P. 114–118.

52. Lemke F. Parallel Self-Organizing Modeling. Proc. of the II Int. Conf. on Inductive Modelling ICIM-2008 (Kyiv, 15-19th of Sept. 2008). Kyiv, 2008. P. 176–183.

53. Koshulko O.A., Koshulko A.I. Multistage combinatorial GMDH algorithm for parallel processing of high-dimensional data. Proc. of III Int. Workshop on Inductive Modelling IWIM-2009. (15-19th of Sept., 2009, Krynica). Krynica, Poland, 2009. Prague: CTU, 2009. P. 114–116.

54. Kordik P., Cerny J. Advanced Ensemble Strategies for Polynomial Models. Proc. of the III Intern. Conf. on Inductive Modelling ICIM-2010 (Yevpatoria, 16–22nd of May, 2010). Yevpatoria, 2010. Kherson: KNTU, 2010. P. 77–82.

55. Cepek M., Kordik P., Snorek M. The Effect of Modelling Method to the Inductive Preprocessing Algorithm. Proc. of the III Intern. Conf. on Inductive Modelling ICIM-2010. (Yevpatoria, 16–22nd of May 2010) Yevpatoria, 2010. Kherson: KNTU, 2010. P. 131–138.

56. Sarychev A.P. System Regularity Criterion of Group Method of Data Handling. Journal of Automation and Information Sciences. 2006. 38(11). P. 25–37. https://doi.org/10.1615/J
Automat Inf Scien.v38.i11.30

57. Sarycheva L. Quality Criteria for GMDH-based Clustering. Proc. of the II International Conference on Inductive Modelling ICIM-2008 (Kyiv, 15–19th of Sept., 2008), Kyiv, 2008.

58. Lemke, F. Insights v.2.0, Self-organizing knowledge mining and forecasting tool, 2013.URL: http://www.knowledgeminer.eu. (Last accessed: 01.11.2018)

59. URL: https://www.gmdhshell.com. (Last accessed: 15.11.2018)

60. URL: www.mgua.irtc.org.ua (Last accessed: 01.12.2018)

61. Stepashko V.S. Method of Critical Variances as Analytical Tool of Theory of Inductive Modeling. Journal of Automation and Information Sciences. 2008. 40(2). P. 4–22. https://doi.org/10.1615/JAutomatInfScien.v40.i3.20

62. Ivakhnenko A.G., Savchenko E.A. Investigation of Efficiency of Additional Determination Method of the Model Selection in the Modeling Problems by Application of the GMDH Algorithm. Journal of Automation and Information sciences. 2008. 40(3). P. 47–58. https://doi.org/10.1615/JAutomatInfScien.v40.i3.50

63. Stepashko V.S., Efimenko S.M. Sequential Estimation of the Parameters of Regression Models. Cybernetics and Systems Analysis. 2005. 41(4). P.631–634. https://doi.org/10.1007/s10559-005-0099-3

64. Stepashko V., Yefimenko S. Parallel algorithms for solving combinatorial macromodelling problems. Przeglad Elektrotechniczny (Electrical Review). 2009. 85(4). P 98–99.

65. Samoilenko O., Stepashko V. Method of Successive Elimination of Spurious Arguments for Effective Solution the Search-Based Modelling Tasks. Proc. of the II Intern. Conf. on Inductive Modelling ICIM-2008 (Kyiv,15–19th of Sept. 2008), Kyiv, 2008. P. 36–39.

66. Moroz O., Stepashko V. Hybrid sorting-out algorithm COMBI-GA with evolutionary growth of model complexity. In: Advances in Intelligent Systems and Computing II: Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2017, Lviv, Ukraine / N. Shakhovska, V. Stepashko, Ed. AISC, Vol. 689. Cham: Springer, 2018. P. 346–360. https://doi.org/10.1007/978-3-319-70581-1_25

67. Stepashko V., Bulgakova O., Zosimov V. Construction and Research of the Generalized Iterative GMDH Algorithm with Active Neurons. In: Advances in Intelligent Systems and Computing II: Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2017, Lviv, Ukraine / N. Shakhovska, V. Stepashko, Editors. AISC book series, Vol. 689. Cham: Springer, 2018. P. 492–510. https://doi.org/10.1007/978-3-319-70581-1_35

68. Pavlov A.V. Generalized relaxational iterative algorithm of GMDH. Inductive Modeling of Complex Systems. Collected papers. Issue 3. Kyiv: IRTC ITS NASU, 2011. P. 121–134. (In Ukrainian)

69. Stepashko, V.S.: Conceptual fundamentals of intelligent modeling. Control Systems and Machines (USiM). 2016. 4, pp. 3–15. (In Russian)

70. Yefimenko S.N., Stepashko V.S. Fundamentals of recurrent-and-parallel computing in the combinatorial algorithm COMBI GMDH. USiM. 2014. 6. P. 27–33. (In Russian)

71. Yefimenko S.N., Stepashko V.S. Simulation experiment as a means of effectiveness research of modeling methods from observation data. USiM. 2009. 1. P. 69–78. (In Russian)

72. Bulgakova O., Zosimov V., Stepashko V. Software package for modeling of complex systems based on iterative GMDH algorithms with the network access capability. System Research and Information Technologies. 1. 2014. P. 43–55. (In Ukrainian)

73. Pavlov A. Designing an automated structural-parametric identification system. Inductive Modeling of Complex Systems. Collected papers. Issue 7. Kyiv: IRTC ITS NASU, 2015. P. 202–219. (In Ukrainian)

74. Yefimenko S. Building Vector Autoregressive Models Using COMBI GMDH with Recurrent-and-Parallel Computations. In: Advances in Intelligent Systems and Computing II: Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2017, Lviv, Ukraine / N. Shakhovska, V. Stepashko, Editors. AISC book series, Vol. 689. Cham: Springer, 2018. P. 601–613. https://doi.org/10.1007/978-3-319-70581-1_42

75. Stepashko V., Samoilenko O., Voloschuk R. Informational Support of Managerial Decisions as a New Kind of Business Intelligence Systems. In: Computational Models for Business and Engineering Domains. G. Setlak, K. Markov (Eds.). Rzeszow, Poland; Sofia, Bulgaria: ITHEA. 2014. P. 269–279.

76. Moroz O., Stepashko V. Data reconstruction of seasonal changes of amylolytic microorganisms amount in copper polluted soils. Proc. of the 13th IEEE Intern. Conf. CSIT-2018 & International Workshop on Inductive Modeling. (Lviv, 11–14th of Sept., 2018), Lviv, 2018. P. 479–482. https://doi.org/10.1109/STC-CSIT.2018.8526587

77. Stepashko V.S., Yefimenko S.M., Savchenko Ye.A. Computerized experiment in inductive modeling. Kyiv: Naukova Dumka, 2014. 222 p. (In Ukrainian)

78. Pavlov A.V., Stepashko V.S., Kondrashova N.V. Effective methods of models self-organization. Kyiv: Akademperiodika, 2014. 200 p. (In Russian)

79. Stepashko V.S., Bulgakova O., Zosimov V. Iterational algorithms of inductive modeling. Kyiv: Naukova Dumka, 2014. 190 p. (In Ukrainian)

80. Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks. 2015. 61, pp. 85–117. https://doi.org/10.1016/j.neunet.2014.09.003

Received 20.09.2018

admin

Issue 4 (194), article 2

DOI:https://doi.org/10.15407/kvt194.04.029

Kibern. vyčisl. teh., 2018, Issue 4 (194), pp.

Yermakova I.I., Professor, DSc (Biology),
Leading Researcher Dept. of Complex Research
of Information Technologies
e-mail: : irena.yermakova@gmail.com

Nikolaienko A.Y., Researcher,
Dept. of Complex Research of Information Technologies
e-mail: n_nastja@ukr.net

Solopchuk Y.M., Researcher,
Dept. of Complex Research of Information Technologies

Hrytsaiuk O.V., 1st category software engineer,
Dept. of Complex Research of Information Technologies

Tadeieva J.P., Ph.D. (Engineering), Senior Researcher,
Dept. of Complex Research of Information Technologies

International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
Acad. Glushkova av., 40, Kiev, 03187, Ukraine

INFORMATION SMARTPHONE TECHNOLOGY FOR PREDICTION OF HUMAN HEALTH STATE UNDER EXTREME ENVIRONMENTAL CONDITIONS

Introduction. Nowadays it can’t be imagined the development of personalized e-medicine without smartphone use. It involves the integration of information platforms, mobile applications and portable medical devices via cloud technologies into a single system. Predictive systems that can assess and prevent risk factors of human health in extreme environmental conditions is still absent in contemporary mobile personalized medicine. Combining the unique features of the service platform with the recent development of m-health allows to develop a unique information smartphone system for assessing the risk factors of human health in various environmental conditions. The system allows to collect personal data, integrate the data with another mobile app and gadget data and thus provide predictions of the human state.

The purpose of the article is to develop an intelligent information system
using smartphone technologies based on a multi-functional service platform for predicting a human functional state under extreme environmental conditions.

Results. A client-server architecture was used to build the intelligent information smartphone system, which allows a user to access the service platform (a key feature of the system) and a centralized database via the smartphone application.
The “client” is the smartphone application that uses network protocols to exchange data with the server. Data input, primary control and data transfer to the server, as well as receiv-ing and displaying the prediction results on the smartphone screen are the main functions of the app. The server software provides data management (receiving, processing, transferring and storing data in the databases), automatically controls the integrity and consistency of the information received and stored, manages multi-user access and confidentiality of databases of different users, logs system events, etc.
A unique distinctive feature of the developed system is the service platform for process-ing the entered conditions data and giving the prediction of human functional state. The pre-diction results are analyzed and, based on the results of the analysis, the system identifies probable health risk factors. The automatic analysis and decision making allow to classify the developed system as an intelligent information technology.

Conclusions. The smartphone-health system has been developed. The system has a client-server architecture that provides multi-user access to its resources and features.
The “client” is a smartphone application that allows a user to input, control and transfer the data to the server, and then receive and display the results on the screen. The server con-sists of a data flow manager, the service platform, prediction result database. The multifunc-tional service platform provides a user with the prediction of his functional state under chosen environmental conditions and physical activity.

Keywords: smartphone, e-health, human state prediction, mobile health, extreme environmental conditions.

Download full text (ru)!

REFERENCES

1. How Smartphone Technology Is Changing Healthcare In Developing Countries The Journal of Global Health. URL: https://www.ghjournal.org/how-smartphone-technology-is-changing-healthcare-in-developing-countries/How Smartphone Technology Is Changing Healthcare In Developing Countries (Last accessed: 29.08.2018).

2. Garge G. K., Balakrishna C., Datta S. K. Consumer health care: Current trends in consumer health monitoring. IEEE Consumer Electronics Magazine. 2018. Vol. 7, No1. P. 38–46. https://doi.org/10.1109/MCE.2017.2743238

3. Dorosh N.V., Boyko O.V., Ilkanych K.I., Zayachkivska O.S., Basalkevych O.Y., Yermakova I.I., Dorosh O.I. M-health technology for personalized medicine. Development and modernization of medical science and practice: experience of Poland and prospects of Ukraine: Collective monograph. Vol. 1. Lublin: Izdevnieciba “Baltija Publishing”, 2017. P. 66–85.

4. Gritsenko V.I., Yermakova I.I., Bogationkova A.I., Dorosh O.I. Information Technologies For Personalized m-Health. Visnyk of the National Academy of Sciences of Ukraine. 2016. No 2. P. 87–90. (in Ukrainian)

5. Dorosh N., Ilkanych K., Kuchmiy H., Boyko I., Yermakova I., Dorosh O., Voloshyn D. Measurement modules of digital biometrie medical systems based on sensory electronics and mobile-health applications. Proceedings of the 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET). IEEE (Slavske, 20–24th of Feb. 2018). Slavske, 2018. P. 687–691. https://doi.org/10.1109/TCSET.2018.8336294

6. Masciantonio M. G., Surmanski A. A. Medical smartphone applications. University of Western Ontario Medical Journal. 2017. Vol. 86(2). P. 51–53. https://doi.org/10.5206/uwomj.v86i2.2030

7. Yermakova I. Information platform of multicompartmental models of human thermoregulatory system. Kibernetika i vycislitel`naa tehnika. 2013. Vol. 174. P. 81–91. (in Russian)

8. Gritsenko V., Yermakova I., Dukchnovskaya K., Tadejeva J. Dynamic models and information technologies for prediction of human vital functions. Control Systems and Computers, 2004, vol. 2, P. 56-60. (in Russian)

9. Kumar S., Nandury S. V., Raj S. An Extended Client Server Architecture in Mobile Environment. International Journal of Computer Engineering and Applications. 2014. Vol. 5(2). P. 97–107.

10. Armstrong L.E., Ganio M.S., Casa D.J., Lee E.C., McDermott B.P., Klau J.F., Jimenez L., Le Bellego L., Chevillotte E., Lieberman H. R. Mild Dehydration Affects Mood in Healthy Young Women. The Journal of nutrition. 2011. Vol. 142(2). P. 382–388. https://doi.org/10.3945/jn.111.142000

Received 31.08.2018

admin

Issue 4 (194), article 1

DOI:https://doi.org/10.15407/kvt194.04.007

Kibern. vyčisl. teh., 2018, Issue 4 (194), pp.

Grytsenko V.I., Corresponding Member of NAS of Ukraine,
Director of International Research and Training
Center for Information Technologies and Systems
of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine
e-mail: vig@irtc.org.ua

Rachkovskij D.A., DSc (Engineering), Leading Researcher
Dept. of Neural Information Processing Technologies
e-mail: dar@infrm.kiev.ua

Revunova E.G., PhD (Engineering), Senior Researcher
Dept. of Neural Information Processing Technologies
e-mail: egrevunova@gmail.com

International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
Acad. Glushkov av., 40, Kiev, 03187, Ukraine

NEURAL DISTRIBUTED REPRESENTATIONS OF VECTOR DATA IN INTELLIGENT INFORMATION TECHNOLOGIES

Introduction. Distributed representation (DR) of data is a form of a vector representation, where each object is represented by a set of vector components, and each vector component can belong to representations of many objects. In ordinary vector representations, the meaning of each component is defined, which cannot be said about DR. However, the similarity of RP vectors reflects the similarity of the objects they represent.
DR is a neural network approach based on modeling the representation of information in the brain, resulted from ideas about a “distributed” or “holographic” representations. DRs have a large information capacity, allow the use of a rich arsenal of methods developed for vector data, scale well for processing large amounts of data, and have a number of other advantages. Methods for data transformation to DRs have been developed for data of vari-ous types – from scalar and vector to graphs.

The purpose of the article is to provide an overview of part of the work of the Department of Neural Information Processing Technologies (International Center) in the field of neural network distributed representations. The approach is a development of the ideas of Nikolai Amosov and his scientific school of modeling the structure and functions of the brain.

Scope. The formation of distributed representations from the original vector representations of objects using random projection is considered. With the help of the DR, it is possible to efficiently estimate the similarity of the original objects represented by numerical vectors. The use of DR allows developing regularization methods for obtaining a stable solution of discrete ill-posed inverse problems, increasing the computational efficiency and accuracy of their solution, analyzing analytically the accuracy of the solution. Thus DRs allow for in-creasing the efficiency of information technologies applying them.

Conclusions. DRs of various data types can be used to improve the efficiency and intelligence level of information technologies. DRs have been developed for both weakly structured data, such as vectors, and for complex structured representations of objects, such as sequences, graphs of knowledge-base situations (episodes), etc. Transformation of different types of data into the DR vector format allows unifying the basic information technologies of their processing and achieving good scalability with an increase in the amount of data processed.
In future, distributed representations will naturally combine information on structure and semantics to create computationally efficient and qualitatively new information technologies in which the processing of relational structures from knowledge bases is performed by the similarity of their DRs. The neurobiological relevance of distributed representations opens up the possibility of creating intelligent information technologies based on them that func-tion similarly to the human brain.

Keywords: distributed data representation, random projection, vector similarity estimation, discrete ill-posed problem, regularization.

Download full text!

REFERENCES

1. Amosov N. M. Modelling of thinking and the mind. New York: Spartan Books, 1967. 192 p. https://doi.org/10.1007/978-1-349-00640-3

2. Amosov N.M., Baidyk T.N., Goltsev A.D., Kasatkin A.M., Kasatkina L.M., Rachkovskij D.A. Neurocomputers and Intelligent Robots. Kyiv: Nauk. Dumka. 1991. 269 p.(in Russian)

3. Gritsenko V.I., Rachkovskij D.A., Goltsev A.D., Lukovych V.V., Misuno I.S., Revunova E.G., Slipchenko S.V., Sokolov A.M., Talayev S.A. Neural distributed representation for intelligent information technologies and modeling of thinking. Kibernetika i vycislitelnaa tehnika. 2013. Vol. 173. P. 7–24. (in Russian)

4. Goltsev A.D., Gritsenko V.I. Neural network technologies in the problem of handwriting recognition. Control Systems and Machines. 2018. N 4. P. 3–20. (in Russian).

5. Kussul E.M. Associative neuron-like structures. Kyiv: Nauk. Dumka. 1992. 144 p. (in Russian)

6. Kussul E.M., Rachkovskij D.A., Baidyk T.N. Associative-Projective Neural Networks: Architecture, Implementation, Applications. Proc. Neuro-Nimes’91. (Nimes, 25–29th of Oct. 25–29, 1991). Nimes, 1991. P. 463–476.

7. Gayler R. Multiplicative binding, representation operators, and analogy. Advances in Analogy Research: Integration of Theory and Data from the Cognitive, Computational, and Neural Sciences. Edited by K. Holyoak, D. Gentner, and B. Kokinov. Sofia, Bulgaria: New Bulgarian University, 1998. P. 405.

8. Kanerva P. Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors. Cognitive Computation. 2009. Vol. 1, N 2. P. 139–159. https://doi.org/10.1007/s12559-009-9009-8

9. Goltsev A., Husek D. Some properties of the assembly neural networks. Neural Network World. 2002. Vol. 12, N. 1. P. 15–32.

10. Goltsev A.D. Neural networks with assembly organization. Kyiv: Nauk. Dumka. 2005. 200 p. (in Russian)

11. Goltsev A., Gritsenko V. Modular neural networks with radial neural columnar architecture. Biologically Inspired Cognitive Architectures. 2015. Vol. 13. P. 63–74. https://doi.org/10.1016/j.bica.2015.06.001

12. Frolov A.A., Rachkovskij D.A., Husek D. On information characteristics of Willshaw-like auto-associative memory. Neural Network World. 2002. Vol. 12, No 2. P. 141–158.

13. Frolov A.A., Husek D., Rachkovskij D.A. Time of searching for similar binary vectors in associative memory. Cybernetics and Systems Analysis. 2006. Vol. 42, N 5. P. 615–623. https://doi.org/10.1007/s10559-006-0098-z

14. Gritsenko V.I., Rachkovskij D.A., Frolov A.A., Gayler R., Kleyko D., Osipov E. Neural distributed autoassociative memories: A survey. Kibernetika i vycislitel`naa tehnika. 2017. N 2 (188). P. 5–35.

15. Li P., Hastie T.J., Church K.W. Very sparse random projections. Proc. KDD’06. (Philadelphia, 20 – 23th of Aug.). Philadelphia, 2006. P. 287–296. https://doi.org/10.1145/1150402.1150436

16. Rachkovskij D.A. Vector data transformation using random binary matrices. Cybernetics and Systems Analysis. 2014. Vol. 50, N 6. P. 960–968. https://doi.org/10.1007/s10559-014-9687-4

17. Rachkovskij D.A. Formation of similarity-reflecting binary vectors with random binary projections. Cybernetics and Systems Analysis. 2015. Vol. 51, N 2. P. 313–323. https://doi.org/10.1007/s10559-015-9723-z

18. Rachkovskij D.A. Estimation of vectors similarity by their randomized binary projections. Cybernetics and Systems Analysis. 2015. Vol. 51, N 5. P. 808–818. https://doi.org/10.1007/s10559-015-9774-1

19. Revunova E.G., Rachkovskij D.A. Using randomized algorithms for solving discrete ill-posed problems. Intern. Journal Information Theories and Applications. 2009. Vol. 16, N 2. P. 176–192.

20. Durrant R.J., Kaban A. Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions. Machine Learning. 2015. Vol. 99, N 2. P. 257–286. https://doi.org/10.1007/s10994-014-5466-8

21. Xiang H., Zou J. Randomized algorithms for large-scale inverse problems with general Tikhonov regularizations. Inverse Problems. 2015. Vol. 31, N 8: 085008. P. 1–24.

22. Revunova E.G. Study of error components for solution of the inverse problem using random projections. Mathematical Machines and Systems. 2010. N 4. P. 33–42 (in Russian).

23. Rachkovskij D.A., Revunova E.G. Randomized method for solving discrete ill-posed problems. Cybernetics and Systems Analysis. 2012. Vol. 48, N. 4. P. 621–635. https://doi.org/10.1007/s10559-012-9443-6

24. Revunova E.G. Randomization approach to the reconstruction of signals resulted from indirect measurements. Proc. ICIM’13 (Kyiv 16–20th of Sept., 2013). Kyiv, 2013. P. 203–208.

25. Revunova E.G. Analytical study of the error components for the solution of discreteill-posed problems using random projections. Cybernetics and Systems Analysis. 2015. Vol. 51, N. 6. P. 978–991. https://doi.org/10.1007/s10559-015-9791-0

26. Revunova E.G. Model selection criteria for a linear model to solve discrete ill-posed problems on the basis of singular decomposition and random projection. Cybernetics and Systems Analysis. 2016. Vol. 52, N.4. P. 647–664. https://doi.org/10.1007/s10559-016-9868-4

27. Revunova E.G. Averaging over matrices in solving discrete ill-posed problems on the basis of random projection. Proc. CSIT’17 (Lviv 05–08th of Sept., 2017). Lviv, 2017. Vol. 1. P. 473–478. https://doi.org/10.1109/STC-CSIT.2017.8098831

28. Revunova E.G. Solution of the discrete ill-posed problem on the basis of singular value decomposition and random projection. Advances in Intelligent Systems and Computing II. Cham: Springer. 2018. P. 434–449.

29. Hansen P. Rank-deficient and discrete ill-posed problems. Numerical aspects of linear inversion. Philadelphia: SIAM, 1998. 247 p. https://doi.org/10.1137/1.9780898719697

30. Nowicki D., Verga P., Siegelmann H. Modeling reconsolidation in kernel associative memory. PLoS ONE. 2013. Vol. 8(8): e68189. doi:10.1371/journal.pone.0068189. https://doi.org/10.1371/journal.pone.0068189

31. Nowicki D, Siegelmann H. Flexible kernel memory. PLoS ONE. 2010. Vol. 5(6): e10955. doi:10.1371/journal.pone.0010955. https://doi.org/10.1371/journal.pone.0010955

32. Revunova E.G., Tyshchuk A.V. A model selection criterion for solution of discrete ill-posed problems based on the singular value decomposition. Proc. IWIM’2015 (20–24th of July, 2015, Kyiv-Zhukin). Kyiv-Zhukin, 2015. P.43–47.

33. Revunova E.G. Improving the accuracy of the solution of discrete ill-posed problem by random projection. Cybernetics and Systems Analysis. 2018. Vol. 54, N 5. P. 842–852. https://doi.org/10.1007/s10559-018-0086-0

34. Marzetta T., Tucci G., Simon S. A random matrix-theoretic approach to handling singular covariance estimates. IEEE Trans. Information Theory. 2011. Vol. 57, N 9. P. 6256–6271. https://doi.org/10.1109/TIT.2011.2162175

35. Stepashko V. Theoretical aspects of GMDH as a method of inductive modeling. Control systems and machines. 2003. N 2. P. 31–38. (in Russian)

36. Stepashko V. Method of critical variances as analytical tool of theory of inductive modeling. Journal of Automation and Information Sciences. 2008. Vol. 40, N 3. P. 4–22. https://doi.org/10.1615/JAutomatInfScien.v40.i3.20

37. Kussul E.M., Baidyk T.N., Lukovich V.V., Rachkovskij D.A. Adaptive neural network classifier with multifloat input coding. Proc. Neuro-Nimes’93 (25–29th of Oct., 1993, Nimes). Nimes, France, 1993 P. 209–216.

38. Lukovich V.V., Goltsev A.D., Rachkovskij D.A. Neural network classifiers for micromechanical equipment diagnostics and micromechanical product quality inspection. Proc. EUFIT’97 (8–11th of Sept, 1997, Aachen). Aachen, Germany, 1997. P. 534–536.

39. Kussul E.M., Kasatkina L.M., Rachkovskij D.A., Wunsch D.C. Application of random threshold neural networks for diagnostics of micro machine tool condition. Proc. IJCNN’01 (4–9th of May, 1998, Anchorage). Anchorage, Alaska, USA, 1998 P. 241–244. https://doi.org/10.1109/IJCNN.1998.682270

40. Gol’tsev A.D. Structured neural networks with learning for texture segmentation in images. Cybernetics and Systems Analysis. 1991. Vol. 27, N 6. P. 927–936. https://doi.org/10.1007/BF01246527

41. Rachkovskij D.A., Revunova E.G. Intelligent gamma-ray data processing for environmental monitoring. In: Intelligent Data Processing in Global Monitoring for Environment and Security. Kyiv-Sofia: ITHEA. 2011. P. 136–157.

42. Revunova E.G., Rachkovskij D.A. Random projection and truncated SVD for estimating direction of arrival in antenna array. Kibernetika i vycislitel`naa tehnika. 2018. N 3(193). P. 5–26.

43. Ferdowsi S., Voloshynovskiy S., Kostadinov D., Holotyak T. Fast content identification in highdimensional feature spaces using sparse ternary codes. Proc. WIFS’16 (4–7th of Dec., 2016, Abu Dhabi) Abu Dhabi, UAE, 2016. P. 1–6.

44. Dasgupta S., Stevens C.F., Navlakha S. A neural algorithm for a fundamental computing problem. Science. 2017. Vol. 358(6364). P. 793–796. https://doi.org/10.1126/science.aam9868

45. Iclanzan D., Szilagyi S.M., Szilagyi L.. Evolving computationally efficient hashing for similarity search. Proc. ICONIP’18. 2. (Siem Reap, 15-18th of Dec., 2018). Siem Reap, Cambodia, 2018. 2018. https://doi.org/10.1007/978-3-030-04179-3_49

46. Rachkovskij D.A., Slipchenko S.V., Kussul E.M., Baidyk T.N. Properties of numeric codes for the scheme of random subspaces RSC. Cybernetics and Systems Analysis. 2005. Vol. 41, N. 4. P. 509–520. https://doi.org/10.1007/s10559-005-0086-8

47. Rachkovskij D.A., Slipchenko S.V., Kussul E.M., Baidyk T.N. Sparse binary distributed encoding of scalars. 2005. Journal of Automation and Information Sciences. Vol. 37, N 6. P. 12–23. https://doi.org/10.1615/J
Automat Inf Scien.v37.i6.20

48. Rachkovskij D.A., Slipchenko S.V., Misuno I.S., Kussul E.M., Baidyk T. N. Sparse binary distributed encoding of numeric vectors. Journal of Automation and Information Sciences. 2005. Vol. 37, N 11. P. 47–61. https://doi.org/10.1615/J
Automat Inf Scien.v37.i11.60

49. Kleyko D., Osipov E., Rachkovskij D.A. Modification of holographic graph neuron using sparse distributed representations. Procedia Computer Science. 2016. Vol. 88. P. 39–45. https://doi.org/10.1016/j.procs.2016.07.404

50. Kleyko D., Rahimi A., Rachkovskij D., Osipov E., Rabaey J. Classification and recall with binary hyperdimensional computing: Tradeoffs in choice of density and mapping characteristics. IEEE Trans. Neural Netw. Learn. Syst. 2018.

51. Kussul E., Baidyk T., Kasatkina L. Lukovich V. Rosenblatt perceptrons for handwritten digit recognition. Proc. IJCNN’01. (Washington, 15-19 July, 2001). Washington, USA. 2001. P. 1516–1521. https://doi.org/10.1109/IJCNN.2001.939589

52. Baidyk T, Kussul E., Makeyev O., Vega A., Limited receptive area neural classifier based image recognition in micromechanics and agriculture. International Journal of Applied Mathematics and Informatics. 2008.Vol. 2, N 3. P. 96–103.

53. Baydyk T., Kussul E., Hernandez Acosta M. LIRA neural network application for microcomponent measurement. International Journal of Applied Mathematics and Informatics. Vol.6, N 4. 2012. P.173–180.

54. Goltsev A.D., Gritsenko V.I. Algorithm of sequential finding the textural features characterizing homogeneous texture segments for the image segmentation task. Kibernetika i vycislitel`naa tehnika. 2013. N 173. P. 25–34 (in Russian).

55. Goltsev A., Gritsenko V., Kussul E., Baidyk T. Finding the texture features characterizing the most homogeneous texture segment in the image. Proc. IWANN’15. (Palma de Mallorca, Spain, June 10-12, 2015). Palma de Mallorca, 2015. 2015. P. 287–300. https://doi.org/10.1007/978-3-319-19258-1_25

56. Goltsev A., Gritsenko V., Husek D. Extraction of homogeneous fine-grained texture segments in visual images. Neural Network World. 2017. Vol. 27, N 5. P. 447– 477. https://doi.org/10.14311/NNW.2017.27.024

57. Kussul N.N., Sokolov B.V., Zyelyk Y.I., Zelentsov V.A., Skakun S.V., Shelestov A.Y. Disaster risk assessment based on heterogeneous geospatial information. J. of Automation and Information Sci. 2010. Vol. 42, N 12. P. 32–45. https://doi.org/10.1615/JAutomatInfScien.v42.i12.40

58. Kussul N., Lemoine G., Gallego F. J., Skakun S. V, Lavreniuk M., Shelestov A. Y. Parcel-based crop classification in Ukraine using Landsat-8 data and Sentinel-1A data. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 2016. Vol. 9, N 6. P. 2500–2508. https://doi.org/10.1109/JSTARS.2016.2560141

59. Kussul N., Lavreniuk M., Shelestov A., Skakun S. Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery. European Journal of Remote Sensing. 2018. Vol. 51, N 1. P. 627–636. https://doi.org/10.1080/22797254.2018.1454265

60. Sokolov A., Rachkovskij D. Approaches to sequence similarity representation. Information Theories and Applications. 2005. Vol.13, N 3. P. 272–278.

61. Recchia G., Sahlgren M., Kanerva P., Jones M. Encoding sequential information in semantic space models: Comparing holographic reduced representation and random permutation. Comput. Intell. Neurosci. 2015. Vol. 2015. Art. 986574. P. 1–18.

62. Rasanen O.J., Saarinen J.P. Sequence prediction with sparse distributed hyperdimensional coding applied to the analysis of mobile phone use patterns. IEEE Trans. Neural Netw. Learn. Syst. 2016. Vol. 27, N 9. P. 1878–1889. https://doi.org/10.1109/TNNLS.2015.2462721

63. Gallant S.I., Culliton P. Positional binding with distributed representations. Proc. ICIVC’16. (Portsmouth, UK 3–5 Aug., 2016). Portsmouth, 2016. 2016. P. 108–113. https://doi.org/10.1109/ICIVC.2016.7571282

64. Frady E. P., Kleyko D., Sommer F. T. A theory of sequence indexing and working memory in recurrent neural networks. Neural Comput. 2018. Vol. 30, N. 6. P. 1449–1513. https://doi.org/10.1162/neco_a_01084

65. Rachkovskij D.A. Some approaches to analogical mapping with structure sensitive distributed representations. Journal of Experimental and Theoretical Artificial Intelligence. 2004. Vol. 16, N 3. P. 125–145. https://doi.org/10.1080/09528130410001712862

66. Slipchenko S.V., Rachkovskij D.A. Analogical mapping using similarity of binary distributed representations. Int. J. Information Theories and Applications. 2009. Vol. 16, N 3. P. 269–290.

Received 22.08.2018