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Issue 1 (207), article 7

DOI:

Cybernetics and Computer Engineering, 2022, 1(207)

Vovk М.І., PhD (Biology), Senior Researcher,
Head of the Department of Bioelectrical Control & Medical Cybernetics
ORCID: 0000-0003-4584-9553
e-mail: vovk@irtc.org.ua; imvovk3940@gmail.com

Kutsiak О.А., PhD (Engineering),
Senior Researcher of the Department of Bioelectrical Control & Medical Cybernetics
ORCID: 0000-0003-2277-7411
e-mail: spirotech85@ukr.net

International Research and Training Center
for Information Technologies
and Systems
of the NAS of Ukraine and of MES of Ukraine,
40, Acad. Hlushkov av. Kyiv, 03187, Ukraine

INFORMATION TECHNOLOGIES FOR MUSCLE FUNCTIONS CONTROL. RETROSPECTIVE ANALYSIS AND DEVELOPMENT PROSPECTS

Introduction. The research on muscle functions control is determined not only by scientific interest but also by practical necessity. 

The purpose of the paper is to conduct a retrospective analysis of the synthesis of information technologies for the control of human muscle functions for their recovery, correction or training.

Results. The evolution of the synthesis of science-intensive information technologies for muscle functions controlling with the purpose for recovering, correcting or training them on the basis of external control circuits is shown. The informative and energy myostimulation signals play the role of these loops. And the signals come from electronic software devices or information software and hardware complexes. The main classes of the first generation of such devices as open (“MIOTON”), adaptive (“MIOSTIMUL”), and modern — “TRENAR” are considered. The devices contain a set of basic software modules for activating the patient reserves to recover the muscle activity depending on the motor functions state and the patient general state. The new patented technology for oral speech recovery after stroke based on training the fine motor skills of the hand is considered. The new information technology of digital medicine “AI-REABILITOLOG” is presented. This technology for information assistance to user (physician) in making diagnostic and treatment decisions on rehabilitation of motor and speech functions uses artificial intelligence tools — specialized software modules for creating the personalized training plan of extremities, fine motor skill of the hand, in particular for oral speech recovery, and the gait on the results of their disorders quantitative assessment. The results of practical application, the advantages of the developed information technologies are presented. The prospects for their development are considered.

Conclusions. The main principles for synthesis of science-intensive information technologies for muscle functions controlling in order to recover, correct or train them on the basis of external control circuits are a combination of physical and cognitive influences, active participation of the subject in training procedures and their self-control.

Keywords: information technologies, digital medicine, control, myostimulation devices, muscle functions, movements, speech, diagnostics, rehabilitation, stroke, personalized quantitative assessment, criteria, artificial intelligence, software module, structural and functional model

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REFERENCES

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

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

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5. Inventor’s certificate. Multichannel device for adaptive bioelectrical motor control of a person / L. Aleev, M. Vovk, V. Goranev, A. Shevchenko (USSR); No 929 054; 23.05.1982. (in Russian).

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8. Gritsenko V.I. Vovk M.I., Kotova A.B., Kiforenko S.I., Belov V.M. Information technologies in biology and medicine. Course of lectures. Kyiv: Naukova Dumka, 2007. 382p. (In Ukrainian)

9. Vovk М.І., Kutsyak O.A. Software module for personal diagnostics of motor functions after stroke. Cybernetics and Computer Engineering. 2019. № 4 (198). рр. 62–77

10. Patent. A method of treating speech disorders / М.І. Vovk, Ye.B. Halian, О.М. Pidopryhora (Ukraine); № 111388; publshed 25.04.2016, Bulletin no 18 (in Ukrainian)

11. Vovk, M.I., Halian, Ye.B., Kutsiak, O.A. Computer Software & Hardware Complex for Personal Oral Speech Restoration after a Stroke. Sci. innov. 2020. Vol. 16, № 1(91).
pp. 54–68. https://doi.org/10.15407/scine15.05.054 

12. Vovk М.І., Kutsyak O.A. Software module for personal diagnostics of motor functions after stroke. Cybernetics and Computer Engineering. 2019. № 4 (198). рр. 62–77

13. Vovk M.I., Kutsiak O.A., Lauta A.D., Ovcharenko M.А. Information Assistance of Researches on the Dynamics of Movement Restoration After the Stroke. Kibernetika i vyčislitel`naâ tehnika. 2017. № 3 (189), pp. 61–78. (in Ukrainian)

14. Belova A., Shchepetova O. Scales, tests and questionnaires in medical rehabilitation. Moscow: Antidor, 2002. 440 p. (in Russian)

15. Smychek V., Ponomareva E. Craniocerebral trauma (clinic, treatment, examination, rehabilitation). Minsk: Research Institute of ME and R, 2010. 430 p. (in Russian)

16. Vovk М.І., Kutsyak О.А. Information technology for forming a personal movement rehabilitation plan after a stroke. Cybernetics and Computer Engineering. 2020. № 3 (201). pp. 87–99.

17. Vovk М.І., Kutsyak О.А. Mobile AI-technology for forming the personalized movements rehabilitation plan after a stroke. Cybernetics and Computer Engineering. 2021. № 4 (206). pp. 73–88.

18. Vovk М.І., Kutsyak О.А. AI-technology of motor functions diagnostics after a stroke. Cybernetics and Computer Engineering. 2021. № 2 (204). pp. 84–100. 

Received 24.03.2022

Issue 1 (207), article 6

DOI:

Cybernetics and Computer Engineering, 2022, 1(207)

Kiforenko S.I., DSc (Biology), Senior Researcher
Leading Researcher of the Department of Application
Mathematical and Technical Methods in Biology and Medicine
ORCID: 0000-0001-2345-6789
e-mail: skifor@ukr.net

Belov V.M., DSc (Medicine), Professor,
Head of the Department of Application
Mathematical and Technical Methods in Biology and Medicine
ORCID: 0000-0001-8012-9717
e-mail: motj@ukr.net

Hontar T.M., PhD (Biology), Senior Researcher
Senior Researcher of the Department of Application
Mathematical and Technical Methods in Biology and Medicine
ORCID: 0000-0002-9239-0709
e-mail: gtm_kiev@ukr.net

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,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine

THE HIERARCHY PRINCIPLE AS THE BASIS OF BIOLOGICAL SYSTEMS RESEARCH

Introduction. The article illustrates the feasibility of using the methodology of a systematic approach for the rational organization of research in solving biomedical problems at the stages of diagnosis, prognosis and correction of the condition. The effectiveness of using the principle of hierarchy as one of the main organizational principles of systems analysis is illustrated by specific examples of quantitative assessment of Health and its components and in the development of hierarchical modeling technology using mathematical models of varying complexity in a single technological cycle simultaneously.

The purpose of the paper is to show the expediency of using the principle of hierarchy on the examples of developing information-structural model of health category as an integrative structural concept and synthesis of hierarchical modeling technology as a basis for modern preclinical trials.

Results A hierarchical structure of health assessment technology has been developed, which includes conceptual level, management level: synthesis of assessment models and algorithms for calculating health reserves according to the norm index, level of synthesis of technological scaling procedures and diagnostic conclusions.

The technology of mathematical modeling using the hierarchy of models of different complexity for simulation research of different algorithms for glycemic control (analytical, numerical, simulation) to predict the glycemic profile at the stage of preclinical trials.

Conclusions. The hierarchical organization of the structure of the study of the category of health allowed to receive quantitative and verbal conclusions about the state of health reserves in general and all its components, taking into account the norm index, which increased the resolution of estimation algorithms. The proposed technology of hierarchical modeling of glycemic regulation in patients with diabetes allows to assess at the preclinical stage the peculiarities of the use of regulatory algorithms to prevent errors directly in the practice of treatment.

Keywords: the hierarchy principle, information-structural model of the health, hierarchical modeling, glycemic control system, simulation pre-clinical trials.

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17. Belov V.M., Kotova A.B. Human health: challenges, methods, approaches. Kyiv: Naukova Dumka, 2017. 132 p. (In Russian)

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http://simulation.su/uploads/files/default/immod-2003-1-50-55.pdf (In Russian)

22. Kiforenko S.I. Hierarchical modeling as the basis of the technology of preclinical testing of algorithms for the treatment of equal glycaemia. Kibernetika i vyčislitel`naâ tehnika. Iss.187, 2017. P. 80–96. (In Ukrainian)

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Received 21.02.2022

Issue 1 (207), article 5

DOI:

Cybernetics and Computer Engineering, 2022, 1(207)

O.S. Kovalenko, DSc (Medicine), Professor,
 Head of the Medical Information Systems Department
ORCID 0000-0001-6635-0124
e-mail: askov49@gmail.com

L.M. Kozak, DSc (Biology), Senior Researcher,
Leading Researcher of the Medical Information Systems Department
ORCID: 0000-0002-7412-3041
e-mail: lmkozak52@gmail.com

M. Najafian Tumajani,
Junior Researcher of the Medical Information Systems Department,
ORCID:
e-mail: najafian@mail.ru

O.O. Romanyuk,
Junior Researcher of the Medical Information Systems Department
ORCID:0000-0002-6865-1403
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,
40, Glushkov ave., Kyiv, 03187, Ukraine

EXPERIENCE AND PROSPECTS OF CREATING MEDICAL INFORMATION SYSTEMS AND INFORMATION TECHNOLOGIES TO SUPPORT MEDICAL CARE

Introduction. One of the four flagship initiatives identified by the WHO as health priorities for the coming years is the Flagship Initiative to enable citizens for receive quality health care through digital health care. The use of digital medical technologies to provide health care will serve for strengthening the health care system, empowering patients and achieving the principle of “health for all”.

The purpose of the paper is to summarize the experience and latest results of the scientists of the Medical Information Systems Department of the International Center for Research and Development of Medical Information Systems and Information Technologies of Digital Medicine against the background of the general process of digital transformation in medicine.

Results. The main characteristics and principles of building modern medical information systems (MIS) as components of the digital medicine ecosystem are determined. Internal and external information flows of MIS are analyzed. To further differentiate the representative attributes of these documents, three similar but different technologies associated with the patient card were identified: electronic medical records, electronic health records and electronic patient health passport, each of which is differentiated based on the level of patient orientation. Based on one of the principles of “5Ps medicine”, the principle of personalization, the structure of personal medical storage is determined, which according to modern challenges is needed by all participants in digital medicine infrastructure (patients, doctors, laboratories and functional diagnostics departments, etc.). To ensure the interconnection of such repositories, models of business processes of accumulation and exchange of digital medical data have been created and based on them mobile applications, modules for accumulation and exchange of digital medical data between different users in the process of diagnostic data analysis have been developed. The interaction of mobile applications with the local information environment of the health care institution is analyzed and its features are taken into account in the created specialized mobile software modules of accumulation and analysis of personal medical data.

Conclusion. The developed model of digital transformation in medicine, which includes digital methods of obtaining and analyzing biomedical signals, digital medical images, methods of forming electronic medical records and documents, allowed to create methods and tools for building the digital medicine ecosystem using global intellectual resources to provide the necessary level for analysis Big Data and decision support for doctors at all stages of medical care. The use of developed mobile applications of accumulation, analysis and exchange of personal medical data allows to review the accumulated data, assess and predict human health according to the developed Data Mining models and implement medical data exchange of different origins between patient and doctor.

Keywords: medical information systems, digital medicine ecosystems, medical information technologies, mobile applications, classification models Data Mining.

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REFERENCES

1. Draft global strategy on digital health 2020–2025. July 2020 by WHO.  https://www.who.int/ docs/ default-source/documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d.pdf (Last accessed: 29.12.2021)

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6. Kozak L.M., Kovalenko A.S., Kryvova O.A., Romanyuk O.A. Digital Transformation in Medicine: From Formalized Medical Documents to Information Technologies of Digital Medicine. Kibernetika i vyčislitel`naâ tehnika. 2018. 4(194). С. 61–78.

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12. Chén O.Y., Roberts B. R. Personalized Health Care and Public Health in the Digital Age. Front. Digit. Health, 30 March 2021. V. 3. Article 595704. https://doi.org/ 10.3389/fdgth.2021.595704.

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14. Kovalenko A.S., Kozak L.M., Romanyuk O.A. Information technology of digital medicine. Kibernetika i vyčislitel`naâ tehnika. 2017. №1(187). С.67–79. (In Russian)

15. Romaniuk, O. O., Kozak, L. M., and Kovalenko, O. S. Formation of Interoperable Digital Medicine Information Environment: Personal Medical Data. Sci. innov. 2021. V. 17, no. 5. P. 50–62.

16. Kryvova O.A., Kozak L.M. Information Technology for Classification of Donosological and Pathological States Using the Ensemble of Data Mining Methods. Cybernetics and Computer Engineering. 2021, 1(203), pp 77–96.

17. Officials Should Target 20 Key Areas to Transform Health Care System https://www8.nationalacademies.org/onpinews/newsitem.aspx?RecordID=10593

18. Nanotechnology is a key priority for the foreseeable future in medicine http:// www.nanolab.com.ua/publicacii/article4.html

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Received 14.01.2022

Issue 1 (207), article 4

DOI:

Cybernetics and Computer Engineering, 2022, 1(207)

L.S. Zhiteckii, PhD (Engineering),
Acting Head of the Department of
Intelligent Automatic Systems
e-mail: leonid_zhiteckii@i.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,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine

PROBLEMS AND PROSPECTS FOR THE INTELLECTUALIZATION OF AUTOMATIC CONTROL SYSTEMS

Introduction. The improvement of automatic control systems via their intellectualization is the important problem from both theoretical and practical points of view. The presence of adaptation and learning processes intrinsic to the natural intelligence makes it possible to consider the modern adaptive and learning systems as some intelligent control systems of the simplest type.

The purpose of this paper is to outline briefly the world-class results related to the efficient adaptive control and achieved in Intelligent Automatic Systems Department during the last 25 years and also to point out on problems of future research in this scientific area.

Results. A new adaptive control theory which has recently been completed represent the significant achievement to deal with the control systems in the presence of both parameter and nonparameter uncertainties. The main distinguishing feature of this theory is that it requires no information about the constrained membership set of unknown plant parameters and the bounds on arbitrary unmeasurable disturbances. Utilizing its methods, we can ensure the desired performance indices of the control systems with uncertain plants whereas the existing methods become quite unacceptable in the same situation.

Conclusion. Based on recent results concerning the adaptation and learning problems, we propose to take a next step toward to novel intelligent automatic control systems containing complex nonlinear plants. However, new perspective methods guaranteeing a perfect behavior of the closed-loop control systems, in particular, the stability of these control systems should be devised before implementing them in practical applications. This as yet unsolved scientific problem remains the subject of future theoretical research.

Keywords: adaptive and learning control system, automatic intelligent control system, parameter and nonparameter uncertainties, unmeasured disturbance, complex nonlinear plant. 

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40. Zhiteckij L.S., Skurikhin V.I., Tyupa O.V, Sapunova N.A. Adaptive discrete-time PID control algorithm for controlling infinite-dimensional systems. Proc. European Control Conference ECC-2001 (Porto, Portugal, September 4-7, 2001), 2001. P. 184 – 189. 
41. Zhiteckij L.S., Skurikhin V. I., Tyupa O. V Tuning and self-tuning of discrete-time PID controllers based on model reduction approach. Proc. IFAC Workshop on Digital Control: Past, Present and Future of PID Control (Terrassa, Spain, April 5-7, 2000), 2000. P. 167 – 172.
42. Zhiteckii L.S. Robust control of some classes of nonlinear discrete-time plants using linear controllers. Journal of Automation and Information Sciences. 2016. N 48, P. 383 – 407. 
43. Zhiteckij L.S. Singularity-free stable adaptive control of a class of nonlinear discrete-time systems. Proc. 15th IFAC World Congress (Barcelona, Spain, July 21-26, 2002), 2002. P. 475 – 480.
44. Skurikhin V.I., Zhitetskij L.S. Control of thermo– and mass exchange processes by using of adjusted models: practical examples. Upravlyayushie sistemy i mashiny. 2002. N 6, P. 77 – 84. (in Russian).
45. Zhiteckii L.S., Azarskov V.N., Nikolaienko S.A., Solovchuk K.Yu. Some features of neural networks as nonlinearly parameterized models of unknown systems using an online learning algorithm. Journal of Applied Mathematics and Physics. January 2018. N 6. P. 247 – 263.

Received 21.03.2022

Issue 1 (207), article 3

DOI:

Cybernetics and Computer Engineering, 2022, 1(207)

Yermakova I.I., DSc (Biology), Professor,
Leading Researcher of the Department of Complex Research of Information Technologies,
ORCID: 0000-0002-9417-1120
e-mail: irena.yermakova@gmail.com

Nikolaienko A.Yu., PhD (Engineering),
Senior Researcher of the Department of Complex Research of Information Technologies,
ORCID: 0000-0002-2402-2947
e-mail: n_nastja@ukr.net

Bogatonkova A.I., PhD (Engineering),
Senior Researcher of the Department of Complex Research of Information Technologies,
ORCID: 0000-0002-7536-5958
e-mail: bogatonkova@gmail.com

Tadeeva J.P., PhD (Engineering),
Senior Researcher of the Department of Complex Research of Information Technologies,
ORCID: 0000-0001-5418-2848
e-mail: jbest0207@gmail.com

Kravchenko P.M.,
Senior Engineer of the Department of Complex Research of Information Technologies,
ORCID: 0000-0001-8137-5063
e-mail: paul.kravchenko@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,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine

MULTIFUNCTIONAL INFORMATION SYSTEM FOR MODELING THE HUMAN THERMOPHYSIOLOGICAL STATE IN EXTREME ENVIRONMENT

Introduction. Models of human thermoregulation are used for theoretical and experimental findings. Mathematical modeling of human physiological systems is one of the main methods for studying them in parallel with physiological researches in human body. For today, many mathematical models have been developed to predict human physiological responses, but most of them are related to special task.

The purpose of the paper is to develop multifunctional information system for modeling of human thermophysiological state that takes into account comprehensive assessment of impact of extreme conditions in humans. 

Results. The information system allows to research different tasks related to assessing the simultaneous impact on humans of various extreme conditions: cold and heat, wet and dry air, high winds, intense physical activity, immersion in cold water, waterproof protective clothing, long-wave and shortwave solar radiation, room temperature inhomogeneity and exposure to electromagnetic field.

Proposed information system enable us to make quantitative evaluations of impact of protective clothing on human thermophysiological state; human adaptation to intense physical activity at hot environment; overheating and dehydration in athletes during the marathon; safe time of hypothermia in cold water; human temperature comfort indoors during professional activity; electromagnetic radiation in the radio frequency ranges. 

Conclusions. The information system provides preliminary recommendations to physiological reserves of human, risks of deterioration of functional state (overheating, dehydration or hypothermia), safe stay time in extreme environment, temperature comfort in room during professional activities; effects of general, regional and local electromagnetic hyperthermia in humans of radio frequency ranges.

Keywords: model of human thermoregulation, information technology, physical activity, environmental conditions, protective clothing, temperature comfort, electromagnetic radiation.

Download full text!

REFERENCES

1. Enescu D. Models and Indicators to Assess Thermal Sensation Under Steady-State and Transient Conditions. Energies. 2019, Vol. 12, Iss. 5, № 841, pp. 143.

2. Yermakova I. Information platform of multicompartmental models of human thermoregulatory system. Kibernetika i vyčislitel`naâ tehnika. 2013, Vol. 174, pp. 81–91. (in Russian)

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5. Yermakova I., Nikolaienko A., Grigorian A. Dynamic model for evaluation of risk factors during work in hot environment. Journal of Physical Science and Application. USA: David Publishing Company. 2013, Vol. 3, № 4, pp. 238243. 

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7. Sawka M.N., Cheuvront S.N., Kenefick R. Hypohydration and human performance: impact of environment and physiological mechanisms. Sports Medicine. 2015, Vol. 45, № 1, pp. 5160. 

8. Hrytsenko V., Nikolaienko A., Solopchuk Y., Yermakova I., Regan M. Dynamics of Physiological Responses during Long Distance Run: Modelling. 2018 IEEE 38^th International Conference on Electronics and Nanotechnology (ELNANO). Institute of Electrical and Electronics Engineers. Kiev, Ukraine. April 2426, 2018, pp. 439442.

9. ISO 7730:2005. Ergonomics of the thermal environment – Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria. Geneva: ISO Standard. 2005, pp. 152.

Received 21.02.2022

Issue 1 (207), article 2

DOI:

Cybernetics and Computer Engineering, 2022, 1(207)

Babak O.V., PhD (Engineering),
Senior Researcher of the Ecological Digital Systems Department
ORCID: 0000-0002-7451-3314
e-mail: dep175@irtc.org.ua

Surovtsev I.V., DSc (Engineering), Senior Researcher,
Head of the Ecological Digital Systems Department
ORCID: 0000-0003-1133-6207
e-mail: igorsur52@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,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine

DATA REDUCTION AS A METHOD OF INTELLECTUALIZATION OF INFORMATION TECHNOLOGIES

Introduction. The principles of reduction theory in the form of methods for detecting hidden patterns in data, approaches to transforming coordinate systems and reducing the dimensionality of input information are widely used in identifying and classifying objects, finding abnormal trends in financial activities, technical systems for measuring physical and chemical parameters.

The purpose of the paper is to review the theoretical and practical results of the application of the theory of reduction in problems and data processing systems.

Methods. Methods for detecting hidden patterns in data, classification of objects using alpha procedure, cognitive modeling of environmental data, frequency-time digital filtering, conversion and modeling of multicomponent signals, measuring concentrations of chemicals in the environment and the content of greenhouse gases in the earth atmosphere, determining the loads on the axis of the car on the signal of dynamic weighing, estimating the consumption of pulverized coal to save coke and natural gas blast furnace.

Results. Principles of the theory of reduction, highly sensitive analytical system “Analyzer SCP” for measuring concentrations of 20 chemicals in drinking water, food and the environment using three new pulsed chronopotentiometry methods for electrochemical analysis, information technology “Atmospheric gases” for determining concentrations of 38 gases atmosphere, computer systems of dynamic weighing of the car for customs weight control of vehicles.

Conclusions. For a class of problems with monotonic integrated measurement data, it is necessary to transform the coordinate system, which allows us to consider a multicomponent signal as the sum of individual components. The use of reduction theory methods has made it possible to develop intelligent information technologies for environmental monitoring of biosphere objects, effective technical systems for measuring physical parameters, and detecting fraudulent transactions in the banking system.

Keywords: reduction theory, hidden patterns, object classifications, coordinate system transformations, frequency-time filtering, concentration determination.

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REFERENCES

1. Ivin A.A., Medvedeva I.A., Bershtein V.L. Reduction. Humanitarian Quarter: Concepts. Center for Humanitarian Technologies, 2002–2021 (Last accessed: 12/29/2021), URL: https://market.ru/concepts/6892 (in Russian).

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6. Vasilyev V.I. Induction and reduction in extrapolation problems. Kibernetika i vyčislitel`naâ tehnika. 1998, Vol. 116, pp.65–81 (in Russian).

7. Vasilyev V.I., Surovtsev I.V. Inductive methods for pattern detection based on reduction theory. Control System and Computers. 1998, No 5, pp. 3–13 (in Russian).

8. Vapnik V. Statistical learning theory. New York: John-Wiley Sans. Inc., 1998, 286.

9. Babak O.V. On one approach to optimizing the solution of problems of learning pattern recognition based on the support vector machine method. Cybernetics and System Analysis. 2004, No 2, pp. 179–185 (in Russian).

10. Babak O.V. On one principle of self-organization of mathematical models. Problems of control and informatics. 2001, No 2, pp. 98–107 (in Russian).

11. Babak O.V., Vasilyev V.I., Chernov V.A. Peculiarities of measuring the consumption of pilecoal fuel when it is blowed into the blast furnace tuyeres. Steel. 1988, No 1, pp.18–20
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12. Device for combinatorial modeling of physical objects: patent 124909, Ukraine: IPC (2006). G05В 17/00, G06G 7/48 (2006.01). a202003852; claimed 26.06.2020; published 08.12.2021 (in Ukrainian). 

13. Device for adjusting of pulverized coal fuel consumption: patent 82758 Ukraine: IPC С21В 7/24/. a200608430; claimed 27.07.2006; published 12.05.2008 (in Ukrainian).

14. Method for measurement of flow rate of dust-coal fuel: patent 83106 Ukraine: IPC G01F 9/00, G01F 25/00, G05B 17/00. a200608431; claimed 27.07.2006; published 10.06.2008 (in Ukrainian).

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16. Gritsenko V.I., Skurikhin V.I., Tsepkov G.V. Information technologies of digital signal processing: new approaches and prospects of implementation. Visnyk NAS of Ukraine. 2005, No 12, pp. 33–41 (in Ukrainian).

17. Surovtsev I.V., Galimov S.K., Tatarinov O.E. Information technology for determining the concentration of toxic elements in environmental objects. Kibernetika i vyčislitel`naâ tehnika. 2018, No.1(191), pp. 5–33. DOI: https://doi.org/10.15407/kvt191.01.005 (in Ukrainian).

18. System for axle-by-axle weighing on platform scales : patent 106013 Ukraine: IPC G01G 19/02, a201309799; claimed 06.08.2013; published 10.07.2014 (in Ukrainian).

19. Surovtsev I.V. New information technology for axial weighing of cars on platform scales. Control System and Computers. 2015, No 3, pp. 77–81 (in Russian).

20. Device for measuring the concentration of toxic elements: patent 107412, Ukraine: IPC (2006) G01N 27/48. a201306295; claimed 21.05.13; published 25.12.14 (in Ukrainian).

21. Device for measuring the concentration of chemical elements by pulsed chronopotentiometry: patent 123459, Ukraine: IPC G01N 27/48 (2006.01). a201902429; claimed 12.03.2019; published 07.04.2021 (in Ukrainian).

22. Surovtsev I.V., Velykyi P.Y., Galimova V.M., Sarkisova M.V. Ionometric method for determination of concentrations of microelements in research of digital medicine. Cybernetics and Computer Engineering, 2020, No 4 (202), pp. 25–43.

23. Wenger E.F, Babak O.V., Surovtsev I.V. et al. Algorithm for operational indirect measurement of the concentration of gaseous impurities in the atmosphere by Fourier spectrometer spectra. Control System and Computers, 2007, No 2, pp.33–38, 81 (in Russian).

24. Gritsenko V.I., Surovtsev I.V., Babak O.V. 5G wireless communication system. Cybernetics and Computer Engineering. 2019, No 3 (197), pp. 5-19. DOI: 10.15407/kvt197.03.005 (in Ukrainian).

25. Gritsenko V.I., Babak O.V., Surovtsev I.V. Peculiarities of interconnection 5G, 6G networks with big data, internet of things and artificial intelligence. Cybernetics and Computer Engineering. 2021, No 2 (204), pp. 5–19 (in Ukrainian).

26. Device for pre-sowing seed treatment: patent 122085, Ukraine: IPC (2020.01) А01С 1/00, A01G 7/04 (2006.01). a201809456; claimed 19.09.2018; published 10.09.2020 (in Ukrainian).

Received 8.02.2022

Issue 1 (207), article 1

DOI:

Cybernetics and Computer Engineering, 2022, 1(207)

Gritsenko V.I., Corresponding Member of NAS of Ukraine,
Honorary 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
ORCID: 0000-0003-4813-6153
e-mail: vig@irtc.org.ua

Tymofijeva N.K., DSc (Engineering), Senior Researcher,
Acting Head of Department of Complex Research of Information Technologies
ORCID: 0000-0002-0312-1153
e-mail: tymnad@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,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine

FINDING SUBCLASSES OF SOLVABLE PROBLEMS IN COMBINAR OPTIMIZATION AND ARTIFICIAL INTELLIGENCE BY STRUCTURE
OF INPUT INFORMATION

Introduction. The literature for some classes of combinatorial optimization problems describes subclasses that have a certain structure of input data with a clear nature, for which there is a known method of analytical finding of a global solution without searching for options. These subclasses of problems are called solvable. They can be used to reduce unsolvable combinatorial optimization problems to solvable ones. 

The purpose of the paper is to identify the following main approaches for solving combinatorial optimization problems: a) methods and algorithms based on partial search of variants; b) methods and algorithms based on recognizing the structure of input information. The second approach includes work on finding subclasses of solvable problems and development of recognition algorithms according to these subclasses of the structure of input information. The problem is to identify subclasses for different classes of combinatorial optimization problems according to the structure of input data, for which according to the developed rules analytically find a global solution.

Methods. To select subclasses of solvable problems, we use the method of modeling input data by functions of a natural argument. To do this, the input data, which are given by finite sequences, are given by the functions of the natural argument, one of which is combinatorial. For various such functions, which are represented by linear, periodic, convex, the global values of the objective function are determined, both maximum and minimum. 

Results. Subclasses of solvable problems are distinguished for different classes of combinatorial optimization and artificial intelligence problems according to the structure of input data. Found global maximum and minimum for assignment problems, traveling salesman problem, placement of objects on a given surface. 

Conclusions. Using the method of modeling the structure of input data by means of natural argument functions allows to reduce some unsolvable problems of combinatorial optimization to solvable ones. For the latter, it is easy to find an argument (combinatorial configuration) for which the objective function acquires a global minimum and maximum, as well as to formulate the expression behind which is its value. In artificial intelligence problems, the subclasses of solvable problems are distinguished both on the basis of similarity and the structure of the input data. Using them allows to reduce unsolvable problems to solvable ones.

Keywords: subclasses of solvable problems, natural argument function, combinatorial optimization, similarity measures, objective function.

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REFERENCES

1. Tymofijeva N.K. Theoretical-Numerical Methods Used to Solve Combinatorial Optimization Problems.  Manuscript. The dissertation for Doctor’s Degree in Technical Sciences on Speciality 01.05.02 – Mathematical Modelling and Numerical Methods.  V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine, K., 2007, 374 p.

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6. Timofeeva N.K. Subclasses of solvable problems from classes of combinatorial optimization problems. Cybernetics and systems analysis. 2009, № 2, pp. 97–105.

7. Timofeeva N.K. On some properties of partitioning a set into subsets. USiM. 2002, № 5, pp. 6–23. 

8. Schlesinger M., Flach B. Some solvable subclasses of structural recognition problems Czech Pattern Recognition Workshop 2000, Tomas Svoboda (ed.), Czech Pattern Recognition Society, Praha, February. 2000, pp. 55–61.

9. Tymofijeva N.K. On some features of the definition of subclasses of solvable problems in the recognition and synthesis of speech signals. Reports of the XV International Conference on Automatic Control “Automation-2008”, Collection of scientific papers in three volumes. Vol. 2, Odessa, September 23 – 26, 2008, Odessa, ONMA, 2008, pp. 937–940.

10. Tymofijeva N.K., Gritsenko V.I. Argument of the objective function in the problem of clinical diagnosis. USiM. 2012, № 3, pp. 3–14.

11. Vintsyuk T.K. Analysis, recognition and interpretation of speech signals. K.: Nauk. dumka, 1987, 262 p.

Received 16.02.2022

Issue 1 (207)

DOI:

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TABLE OF CONTENTS:

Information technologies and systems: 25 years of experience and development prospects

Informatics and Information Technologies:

Gritsenko V.I., Tymofijeva N.K.
Finding subclasses of solvable problems in combinar optimization and artificial intelligence by structure of input information

Babak O.V., Surovtsev I.V.
Data reduction as a method of intellectualization of information technologies

Yermakova I.I, Nikolaienko A.Yu., Bogatonkova A.I., Tadeeva Ju.P., Kravchenko P.M. Multifunctional information system for modeling the human thermophysiological state in extreme environment

Intelligent Control and Systems:

Zhiteckii L.S.
Problems and prospects for the intellectualization of automatic control systems

Medical and Biological Cybernetics:

Kovalenko O.S., Kozak L.M., Najafian Tumajani M., Romanyuk O.O.
Experience and prospects of creating the medical information systems and information technologies to medical care support

Kiforenko S.I., Belov V.M., Hontar T.M.
The hierarchy principle as the basis of biological systems research

Vovk М.І., Kutsiak О.А.
Information technologies for muscle functions control. Retrospective analysis and development prospects

Issue 4 (206), article 5

DOI:https://10.15407/kvt206.04.073

Cybernetics and Computer Engineering, 2021, 4(206)

VOVK М.І., PhD (Biology), Senior Researcher,
Head of Bioelectrical Control & Medical Cybernetics Department
ORCID: 0000-0003-4584-9553, e-mail: vovk@irtc.org.ua; imvovk3940@gmail.com

KUTSIAK О.А., PhD (Engineering),
Senior Researcher of the Bioelectrical Control & Medical Cybernetics Department
ORCID: 0000-0003-2277-7411, e-mail: spirotech85@ukr.net

International Research and Training Center for Information Technologies
and Systems of the NAS of Ukraine and of MES of Ukraine,
40, Acad. Hlushkov av. Kyiv, 03187, Ukraine

MOBILE AI-TECHNOLOGY FOR FORMING THE PERSONALIZED MOVEMENTS REHABILITATION PLAN AFTER A STROKE

Introduction. The consequences of stroke change seriously the quality of life. Especially motor activity suffers. Speech disorders occupy a significant place. The synthesis of effective technologies for restoration of limb movements, fine motor hand, that plays significant role in restoring the speech motor skills, is the urgent scientific and applied task.

Recently, the use of artificial intelligence in medicine has attracted attention. At the same time, mobile technologies are developing. It is considered that artificial intelligence in a smartphone will make the medicine of the future accessible for everybody.

The purpose of the paper is to develop the technology for movement restoration after a stroke that uses the artificial intelligence tool for increasing the effectiveness of rehabilitation process – specialized software module for mobile platforms to assist the user (physician) in the formation of personalized plans at different rehabilitation stages.

Results. The AI-technology for forming the personalized movement training plan to patient after a stroke is developed. This technology uses artificial intelligence tool – the software module for information assistance in forming the plan  “MovementRehabStroke 1.0 (MD)” that installed in  mobile platforms. This module provides the user with recommended movement training plan based on results of quantitative assessment of movements deficit is determined by software module “MovementTestStroke 1.1 (MD)” and patient general state. This plan may be corrected. The structural and functional model of user (physician) and software module “MovementRehabStroke 1.0 (MD)” interaction, and algorithm for forming the personalized movements rehabilitation plan – recommended and finally user-formed are given.

Conclusions. The usage of artificial intelligence tools reduces the physician error in diagnostic and treatment decisions, prevents complications, reduces the disability risks, improves the quality and widespread usage of medical and rehabilitation services for patients after stroke.

Keywords: stroke, AI-technology, personalized plan, movement training, rehabilitation, diagnostics, software module, structural and functional model, algorithm.

Download full text!

REFERENCES

1 Gorelick P.B. The global burden of stroke: Persistent and disabling. Lancet Neurol. 2019. Vol. 18. No 5. pp. 417-418.
https://doi.org/10.1016/S1474-4422(19)30030-4

2 Delpont B., et.al. Pain after stroke: A review. Revue neurologique. 2018. Vol. 174. No 10. pp. 671-674.
https://doi.org/10.1016/j.neurol.2017.11.011

3 Buono Lo, Corallo F., Bramanti P., Marino S. Coping strategies and health-related quality of life after stroke. 2017. J. Health Psychol. 2017. Vol. 22. No 1. pp. 16-28.
https://doi.org/10.1177/1359105315595117

4 Jorgensen H.S., Nakayama H., Raaschou H.O., Olsen T.S. Recovery of walking function in stroke patients: the Copenhagen Stroke Study. Archives of Physical Medicine and Rehabilitation. 1995. Vol. 76(1). pp. 27-32.
https://doi.org/10.1016/S0003-9993(95)80038-7

5 Holland A., Fridriksson J. Aphasia management during the early phases of recovery following stroke. American Journal of Speech-Language Pathology. 2001. Vol. 10 (1). pp. 19-28.
https://doi.org/10.1044/1058-0360(2001/004)

6 Berthier M.L. Poststroke aphasia: epidemiology, pathophysiology and treatment. Drugs Aging. 2005. Vol. 22(2). pp. 163-182.
https://doi.org/10.2165/00002512-200522020-00006

7 Hatem S.M., Saussez G., Faille M.D., Prist V. Rehabilitation of motor function after stroke: A multiple systematic review focused on techniques to stimulate upper extremity recovery. Frontiers Hum. Neurosci. 2016. Vol. 10. P. 442.
https://doi.org/10.3389/fnhum.2016.00442

8 Upper Limb Rehabilitation System for Stroke Survivors Based on Multi-Modal Sensors and Machine Learning / Sheng Miao, et.al. IEEE Access: Special section on AI and IoT convergence for smart health. 2021. Vol. 9. pp. 30283-30291.
https://doi.org/10.1109/ACCESS.2021.3055960

9 Hossain M.S. Cloud-supported cyber-physical localization framework for patients monitoring. IEEE Syst. J. 2015. Vol. 11. No 1. pp. 118-127.
https://doi.org/10.1109/JSYST.2015.2470644

10 Stinear C.M., Lang C.E., Zeiler S., Byblow W.D. Advances and challenges in stroke rehabilitation. Lancet Neurol. 2020. Vol. 19. No 4. pp. 348-360.
https://doi.org/10.1016/S1474-4422(19)30415-6

11 Vovk M.I., Galyan Ye.B. Restoring of motor component of speech based on muscle movement control. Theoretical grounding. Cybernetics and Computer Engineering. 2012. No 167. pp.51-60. (in Russian).

12 Varun H Buch, Irfan Ahmed, Mahiben Maruthappu. Artificial intelligence inmedicine: current trends and future possibilities. Br J Gen Pract. 2018. No 68(668). pp. 143-144.
https://doi.org/10.3399/bjgp18X695213

13 The Oxford Dictionary of Current English. Oxford: Oxford University Press, 2001. 3rdedition. 1083 p.

14 Ahuja A.S. The impact of artificial intelligence in medicine on the future role of the physician. PeerJ. 2019.
https://doi.org/10.7717/peerj.7702

15 Artificial intelligence in medicine: the main trends in the world. URL:https://medaboutme.ru/zdorove/publikacii/stati/sovety_vracha/iskusstvennyy_intellekt_v_meditsine_glavnye_trendy_v_mire/ (Last accessed: 1.04.2021) (in Russian).

16 Vovk M.I., Kutsiak O.A. AI-Technology of Motor Functions Diagnostics after a Stroke. Cybernetics and Computer Engineering. 2021. No 2 (204). pp. 84-100.
https://doi.org/10.15407/kvt204.02.084

17 Vovk M.I., Kutsyak O.A. Software module for personal diagnostics of motor functions after stroke. Cybernetics and Computer Engineering. 2019. No 4 (198). pp. 62-77.
https://doi.org/10.15407/kvt198.04.062

18 Belova A., Shchepetova O. Scales, tests and questionnaires in medical rehabilitation. Moscow: Antidor, 2002. 440 p. (in Russian)

19 Smychek V., Ponomareva E. Craniocerebral trauma (clinic, treatment, examination, rehabilitation). Minsk: Research Institute of ME and R, 2010. 430 p. (in Russian)

20 Vovk M.I., Kutsyak O.A. Information technology for forming a personal movement rehabilitation plan after a stroke. Cybernetics and Computer Engineering. 2020. No 3 (201). pp. 87-99.
https://doi.org/10.15407/kvt201.03.087

21 Vovk M.I. Information technology of movement control. Evolution of synthesis and development prospects. Cybernetics and Computer Engineering. 2018. No 4 (194). pp. 79-97. (in Ukrainian).
https://doi.org/10.15407/kvt194.04.079

Received 15.09.2021

Issue 4 (206), article 4

DOI:https://10.15407/kvt206.04.054

Cybernetics and Computer Engineering, 2021, 4(206)

KRYVOVA O.A.,
Researcher, Medical Information Systems Department
ORCID: 0000-0002-4407-5990, e-mail: ol.kryvova@gmail.com

KOZAK L.M., DSc (Biology), Senior Researcher,
Leading Researcher, Medical Information Systems Department
ORCID: 0000-0002-7412-3041, e-mail: lmkozak52@gmail.com

KOVALENKO O.S., DSc (Medicine), Professor,
Head of Medical Information Systems Department
ORCID: 0000-0001-6635-0124, e-mail: askov49@gmail.com

NENASHEVA L.V.
Junior Researcher, Medical Information Systems Department
ORCID: 0000-0003-1760-2801, e-mail: larnen@ukr.net

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,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine

PREDICTION OF SURGERY CONTROL PARAMETERS IN CARDIOLOGY TO OPTIMIZE THE EMISSION FRACTION VALUES WITH THE HELP OF NEURAL NETWORKS

Introduction. In the Big Data era, decision tree methods, machine learning, and neural networks, along with other Data Mining methods became an alternative to classical statistical methods as a more useful tool for analyzing large and inhomogeneous data. Neural Networks methods have emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction, treatment. 

The purpose of the paper is to identify the control parameters of the surgical intervention to optimize the EF ejection fraction after the surgery using a Data Mining method (neural network) models.

Results. The analysis of changes in hemodynamic parameters of children with severe heart defects due to surgery — implantation of conduit. Changes in these parameters after surgery were analyzed using analysis of variance for repeated measurements (RepANOVA). It was determined that after the surgery there was a significant, statistically significant decrease in 3 hemodynamic parameters (end diastolic index, aortic pressure gradient, and augmentation index). According to the cluster analysis, three groups of patients were identified, which were differed in all hemodynamic parameters and in the peculiarities of changes in the studied parameters after surgery. A model based on a neural network of the RBF type (with radial-based activation functions) was built using the Data Mining Automated Neural Networksmodule of the STATISTICA package. According to the developed models, the dependence of the emission fraction after the surgery on the control parameters — dopamine dose and conduit diameter was determined.

Conclusions. The use of predictive models of neural networks developed by the type of RBF network with radially symmetric functions in single-layer networks, allowed to analyze the effectiveness of surgical interventions in the case of congenital heart disease in infants and children. Taking into account the results of the developed predictive model of the dependence of the cardiac output fraction on the parameters of surgery (dose, conduit diameter) and factors such as age, weight, hemodynamic status, gives the surgeon essential information to achieve good results of a surgery.

Keywords: Data Mining classification models, predictive models, neural networks, surgical efficiency

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Received 01.09.2021