admin

Issue 2 (204), article 5

DOI:

Cybernetics and Computer Engineering, 2021, 2(204)

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

KUTSIAK О.А., PhD (Engineering),
Senior Researcher of Bioelectrical Control & Medical Cybernetics Department
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. Glushkov av. Kyiv, 03187, Ukraine

AI-TECHNOLOGY OF MOTOR FUNCTIONS DIAGNOSTICS AFTER A STROKE

Introduction. Diagnostics of motor functions plays an important role in the motor functions restoration after stroke. Synthesis of effective technologies for personalized assessment of motor functions disorders at different rehabilitation stages is an urgent scientific and applied task.

The purpose of the paper is to develop information technology for diagnostics of motor functions deficit after stroke, that uses artificial intelligence tools to increase the effectiveness of the diagnostic process.

Results. The theoretical and practical foundations to synthesize AI-technology for personal diagnostics of motor functions deficit, and the assessment of their restoration as a result of rehabilitation measures after stroke have been developed. For informational assistance to the physician in the diagnostic process, artificial intelligence is used. A new class of mobile digital medicine tools – the specialized software modules for motor functions diagnostics “MovementTestStroke 1.1 (PC)” installed in the PC-structure, and “MovementTestStroke 1.1 (MD)” installed in mobile platforms running under Android operation system have been developed. Software implementation — Visual Studio 2019, C# programming language. Structural and functional models of interaction of physician with software modules, algorithms for motor function deficit diagnostics, and UML-diagrams of these modules are presented.

Functional features of the technology: an expanded range of evidence criteria for personalized quantitative assessment of limb movement deficit, storage in the Database and display on the interface the results of deficit assessment, as well as the deficit dynamics during the rehabilitation course in a convenient form (tables, graphs) make it possible to reduce the physician error, prevent complications, identify the disorders specifics, compare the rehabilitation effectiveness of the upper and lower limbs, their distal and proximal parts, including fine motor skills of the hand, restoration of which helps to restore speech in motor or motor and sensory aphasia.

Conclusions. The use of artificial intelligence tools to diagnose motor deficit will increase the diagnostic effectiveness, and, as a consequence, rehabilitation services for patients after stroke.

Keywords: diagnostics, motor functions, stroke, personal quantitative assessment, criteria, technology, artificial intelligence, software module, structural-functional model, algorithm, activity diagram

Download full text!

REFERENCES

  1. Norrving Bo. Action Plan for Stroke in Europe 2018–2030. European Stroke Journal. 2018, Vol. 3(4), pp. 309–336.
  2. 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)
  3. Gritsenko V.I., Vovk M.I. Trenar – Innovative Technology of Restoration of Movements. Science and Business – the basis of economic development: materials of the International Scientific and Practical Forum. Ukraine, Dnipropetrovsk, 2012, pp. 204–206. (in Russian)
  4. 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: 05.05.2017) (in Russian).
  5. Vovk М.І., Kutsyak О.А. Software module for personal diagnostics of motor functions after stroke. Kibernetika i vyčislitel`naâ tehnika. 2019, № 4 (198), рр. 62–77.
  6. Belova A., Shchepetova O. Scales, tests and questionnaires in medical rehabilitation. Moscow: Antidor, 2002. 440 p. (in Russian)
  7. Smychek V., Ponomareva E. Craniocerebral trauma (clinic, treatment, examination, rehabilitation). Minsk: Research Institute of ME and R, 2010. 430 p. (in Russian)
  8. Vovk М.І., Kutsiak О.А. Information technology for forming a personal movement rehabilitation plan after stroke. Kibernetika i vyčislitel`naâ tehnika. 2020, № 3 (201), рр. 87–99.
  9. Certificate of registration the copyright “Computer program «Diagnostics of deficit of general limb movement, fine motor skills of the hand, walking form by the technique for quantitative assessment of movements deficit in patients after stroke “MovementTestStroke 1.0 (PC)”»” / M.I. Vovk, O.A. Kutsiak (Ukraine); No. 98161; published dated 16.06.2020 [in Ukrainian].
  10. Buch G., Rambo D., Yakobson I. The language of the UML. User’s Guide: transl. from English by N. Muhin. Moscow: DMK Press, 2006. 496 p. (in Russian)
  11. Theory and practice of UML. Activity diagram. URL: http://it-gost.ru/articles/ view_articles/96 (in Russian)

Received 01.04.2021

admin

Issue 2 (204), article 4

DOI:

Cybernetics and Computer Engineering, 2021, 2(204)

FAINZILBERG L.S.1, DSc. (Engineering), Professor,
Chief Researcher of the Department of Automatic Systems
ORCID: 0000-0002-3092-0794
e-mail: fainzilberg@gmail.com

SOLOVEY S.R.2, Student Faculty of Biomedical Engineering,
e-mail: maximum.lenovo.ml@gmail.com

1International 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. Glushkova av., Kyiv, 03187, Ukraine,

2The National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»
37, Peremohy av., Kyiv, 03056, Ukraine

SELF-LEARNING INFORMATION TECHNOLOGY FOR DETECTING RESPIRATORY DISORDERS IN HOME CONDITION

Introduction. In connection with the COVID-19 pandemic, it is important to start treatment promptly in case of a threat of developing viral pneumonia in a patient. The solution to this problem requires the creation of new means for detecting respiratory disorders with a minimum probability of “missing the target”. At the same time, it is equally important to minimize visits to medical institutions by healthy patients because of the danger of their contact with possible carriers of coronavirus infections, that is, to minimize the likelihood of a «false alarm».

Purpose of the article is to develop a method that allows a patient to signal at home about the advisability of contacting a medical institution for an in-depth examination of the respiratory system, and to assess the possibility of implementing this method on a smartphone using a built-in microphone.

Methods. A distinctive feature of the proposed approach lies in the construction of a personalized standard of normal respiratory respiration for a particular patient based on self-learning from a finite sample of observations at home and in comparison, based on original computational algorithms of phonospirograms of sound signals of the following observations with the standard.

Results. A prototype of information technology has been developed that will provide home alarms about possible respiratory disorders, requiring consultation with a doctor and the need for an in-depth medical examination.

It is shown that the construction of a personalized standard of normal breathing can be carried out based on the use of a set of original computational procedures for a finite sample of realizations, independently registered by the user using a microphone built into a smartphone. The algorithm for constructing a standard is based on digital processing of a matrix of paired distances between phonospirograms of the final training sample of observations.

Findings. A software application that provides the implementation of the proposed computational procedures can be implemented on a smartphone of average performance running the Android operating system.

Keywords: respiratory noises, intelligent IT, computational procedures, smartphone.

Download full text!

REFERENCES

  1. Piirila P., Sovijarvi A.R. Crackles: recording, analysis and clinical significance. European Respiratory Journal. 1995, no. 8(12), pp. 2139–2148. DOI: 10.1183/09031936.95.08122139.
  2. Forgacs P. The functional basis of pulmonary sounds. Chest Journal. 1978, vol. 73,
    no 3, pp. 399-405. DOI: 10.1378/chest.73.3.399.
  3. Kosovets LI Experience of electronic registration and classification of breathing sounds of children with bronchopulmonary diseases. Collection of works of acoustic symposium «Consonance-2011». 2011: Institute of Hydromechanics of the National Academy of Sciences of Ukraine, pp. 154–159. (In Russian).
  4. Pasterkamp H., Carson C., Daien D., Oh Y. Digital respirosonography. New images of lung sounds. Chest Journal. 1989, vol. 96, no 6, pp. 1405–1412. DOI: 10.1378/chest.96.6.1405.
  5. Pasterkamp H., Patel S., Wodicka G.R. Asymmetry of respiratory sounds and thoracic transmission. Medical and Biological Engineering and Computing. 1997, vol. 35,
    pp. 103–106. https://doi.org/10.1007/BF02534138.
  6. Wodichka G.R., Kraman S.S., Zenk G.M., Pasterkamp H. Measurement of respiratory acoustic signals. Chest Journal. 1994. vol. 106, no. 4. pp. 1140–1144.
  7. Murphy R.L.H., Vyshedskiy A. et all. Automated Lung Sound Analysis in Patients With Pneumonia. Respiratory Care. 2005, vol. 49, no. 12, pp. 1490–1497. DOI: 10.1378/chest.124.4_MeetingAbstracts.190S-b
  8. Vovk I.V., Goncharova I.Yu. An analytical method for assessing the acoustic properties of stethoscopes. Acoustic bulletin. 2000, vol. 3, no. 2, pp. 10–16. (In Russian).
  9. Goncharova Yu.O. Prospects for storing phonospirographic computer diagnostics in children with bronchogenic dysplasia. Bulletin of VDNZU «Ukrainian Medical Stomatological Academy». 2013, vol. 13, issue 2 (42), pp. 85–88. (In Russian).
  10. Gritsenko V.I., Fainzilberg L.S. Intelligent information technologies in digital medicine on the example of phase-graphy. Kyiv: Naukova Dumka, 2019. 423 p. (In Russian).
  11. Cugell D.W. Lung sound nomenclature. The American Review of Respiratory Disease. 1987, vol. 136, no. 4, pp. 1016. DOI: 10.1164/ajrccm/136.4.1016.
  12. Earis J. Lung sounds. Thorax. 1992, no, 47, pp. 671–672.
  13. Loudon R.G., Murphy R.L. 1984. Lung sounds. The American Review of Respiratory. 1984, Vol. 130, pp. 663–673.
  14. Paciej R., Vyshedskiy A., Bana D. Squawks in pneumonia. Thorax. 2004, vol. 59,
    pp. 177–178.
  15. Wilkins R.L., Dexter J.R., Murphy R.L., Belbono E.A. Lung sound nomenclature survey. Chest Journal. 1990, no. 98, pp. 886-889.  DOI: 10.1378/chest.98.4.88.
  16. Sounds in human lungs download and listen online. URL:https://zvukipro.com/zvukiludei/1392-zvuki-v-legkih-cheloveka.html. (Last accessed: 24.12.2020) (In Russian).
  17. Makarenkova A.A., Ermakova O.V. Preliminary studies of breathing sounds in patients with chronic obstructive pulmonary disease. Abstracts of the reports of the acoustic symposium «Consonance-2009». 2009, Institute of Hydromechanics of the National Academy of Sciences of Ukraine, pp. 40–41. (In Russian).
  18. Fainzilberg L.S. An approach to diagnostic personification decisions on the example of evaluation of cardiac activity. Kibernetika i vyčislitel`naâ tehnika. 2014, no. 178, p. 52–65. (In Russian).
  19. Frigo M., Johnson S.G. FFTW: An adaptive software architecture for the FFT. Proc. of the IEEE Intern. Conf. on Acoustics, Speech, and Signal Processing, Seattle, 1998: WA, vol. 3, pp. 1381–1384.
  20. Sejdić E. Djurović I. JiangJ. Time-frequency feature representation using energy concentration: An overview of recent advances. Digital Signal Processin. 2009, vol. 19,
    no 1, pp. 153–183. DOI:10.1016/j.dsp.2007.12.004.
  21. Bureev A.S. Mathematic model for spectral characteristics of respiratory sounds registered in trachea region. Global Journal of Pure and Applied Mathematics. 2016, vol. 12, no 5. pp. 4569–4578.
  22. Ghafarian P., Jamaati H., Hashemian S.M. A Review on human respiratory modeling. Tanaffos. 2016, vol.15, no. 2, pp. 61–69.
  23. Harper P., Kraman S.S., Pasterkamp H., Wodicka R. An acoustic model of the respiratory tract. IEEE Transactions on Biomedical Engineering. 2001, vol. 48, no. 5, pp. 543–550. DOI: 10.1109/10.918593.
  24. Harper P. Modeling and measurement of flow effects on tracheal sounds. IEEE Transactions on Biomedical Engineering. 2003, vol. 50, no 1, pp. 1–10.
  25. Liu Y., So R.M.C., Zhang C.H. Modeling the bifurcating flow in an asymmetric human lung airway. Journal of Biomechanics. 2003, vol. 36, no. 7, pp. 951–959.
  26. Venegas J.G. Self-organized patchiness in asthma as a prelude to catastrophic shifts. Nature. 2005, vol. 434, pp. 777–782.
  27. Xi J. Numerical study of dynamic glottis and tidal breathing on respiratory sounds in a human upper airway model. Sleep and Breathing. 2017, vol. 22, pp. 463–479. DOI:10.1007/s11325-017-1588-0.
  28. Gurung A. Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: A systematic review and meta-analysis. Respiratory Medicine. 2011, vol. 105, no. 9, pp. 1396–1403. DOI: 10.1016/j.rmed.2011.05.007.
  29. Schmidt A., Zidowitz S., Kriete A., Denhard T., Krass S., Peitgen H.O. A digital reference model of the human bronchial tree. Computerized Medical Imaging and Graphics. 2004,
    vol. 28, no. 4, pp. 203–211. DOI: 10.1016/j.compmedimag.2004.01.001.
  30. Korenbaum V.I. Acoustic diagnostics of the human respiratory system based on an objective analysis of respiratory sounds. Vestnik FEB RAS. 2004, no. 5, pp. 68–79. (In Russian).
  31. Furman E.G., Sokolovsky V.L., Furman G.B. Mathematical model of respiratory noise propagation in the respiratory tract. Russian journal of biomechanics. 2018, vol. 22, no. 2b, pp. 166–177. (In Russian). DOI: 10.15593/RZhBiomeh/2018.2.03.
  32. Dyachenko A.I., Mikhailovskaya A.N. Respiratory acoustics (Review). Proceedings of the Prokhorov General Physics Institute. 2012, vol. 68, pp. 136–181. (In Russian).

Received 02.03.2021

admin

Issue 2 (204), article 3

DOI:

Cybernetics and Computer Engineering, 2021, 2(204)

SHEPETUKHA Y.M., PhD (Engineering), Senior Researcher,
Leading Researcher of the Intelligent Control Department
ORCID: 0000-0002-6256-5248
e-mail: yshep@meta.ua

VOLKOV  O.Ye.,
Head of the Intelligent Control Department
ORCID: 0000-0002-5418-6723
email: alexvolk@ukr.net

KOMAR M.M.,
Senior Researcher of Intelligent Control Department
ORCID: 0000-0002-0119-0964
e-mail: nickkomar08@gmail.com

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

INTELLECTUALIZATION OF DECISION MAKING PROCESSES IN AUTONOMOUS CONTROL SYSTEMS

Introduction. Scientific-technical level of any country in a modern world is mainly determined by a current state and development rate of informational technologies. At the same time, the main avenue of information technologies’ improvement is their intellectualization. Due to intellectualization, it became possible to create advanced systems with principally novel functional capabilities, in particular, high-speed computer systems able to autonomous actions in a complex and dynamic environment. Control means for complex objects and processes play an important role in the operation of autonomous systems. Therefore, the study of theoretical as well as applied issues of such systems’ construction is an important scientific and engineering problem.

The purpose of the paper is to examine both current state and development prospects of a new direction in the area of intelligent information technologies – the elaboration of autonomous control systems for complex objects and processes in a dynamic environment; to formulate a well-grounded approach for the increase in intellectualization level of decision processes in such systems.

Methods. The development of autonomous control systems, as well as the increase in decision making processes’ intellectualization level in such systems, is based on the usage of the following conceptual, theoretical and methodological instruments: the theory of informational technologies’ intellectualization, the methodology of intelligent control, the theoretical fundamentals of artificial intelligence systems’ construction, decision making methods, the methodology of image-based reasoning, methods for simulation of image-based comprehension of environment.

Results. An approach for the consistent usage of methods of artificial intelligence, decision making and intelligent control aimed at the development of autonomous means for the control of complex objects and processes has been examined. Appropriateness of creation of the systems profiled for operations in designated problem domains has been grounded. Both specific features and components of the framework for decision making in intelligent control systems have been determined. Both necessity of the creation of intelligent environment and important role of sensor networks have been stressed. Methodology for the construction of informational images, which represent the most important components of a current situation, has been proposed. Examples of the usage of informational images for performing both dynamic and evolutional re-planning have been considered.

Conclusions. A reasonable way for the development of intelligent control systems is the one that provides a consistent usage of different types of models. Image-based representation of a current situation’s essential interconnections is an efficient instrument for the intellectualization at different stages of decision making processes – alternative generation, understanding of inconsistencies among different data sources, execution of choice procedure, evaluation of results. The application of artificial intelligence elements for decision making in autonomous systems is especially well-grounded in cases of time shortage as well as availability of a great number of existing alternatives.

Keywords: intellectualization of information technologies, intelligent control, decision making, autonomy, artificial intelligence, image, uncertainty, adaptation.

Download full text!

REFERENCES

  1. Mertoguno J.S. Human decision making model for autonomic cyber systems. International Journal on Artificial Intelligence Tools. 2014, Vol. 23, № 6. URL: https://www.worldscientific.com/doi/abs/10.1142/ S0218213014600239. – Title from the screen.
  2. Gonzales D., Harting S. Designing unmanned systems with greater autonomy. RAND Corporation Research Report, Santa Monica, CA, USA, 2014. URL: https://www.rand.org/content/dam/rand/pubs/research_reports/ RR600/RR626/RAND_RR626.pdf. – Title from the screen.
  3. Bradshaw J.M., Hoffman R.R., Johnson M., Woods D.D. The seven deadly myths of “autonomous systems”. IEEE Intelligent Systems. 2013, Vol. 28, № 3, pp. 54–61.
  4. Groumpos P.P. Complex systems and intelligent control: issues and challenges. IFAC Proceedings Volumes. 2001, Vol. 34, №8, pp. 29–36. URL: https://www.sciencedirect.com/science/article/pii/S1474667017407907. – Title from the screen.
  5. Artificial Intelligence (AI): What is it and how does it work? URL: https://www.lexology.com/library/detail.aspx?g=5424a424-c590-45f0-9e2a-ab05daff032d. – Title from the screen.
  6. Schubert J., Brynielsson J., Nilsson M., Svenmarck P. Artificial intelligence for decision support in command and control systems. Proceedings of the 23rd International Command and Control Research & Technology Symposium “Multi-Domain C2”, Pensacola, FL, USA, 2018. URL: https://www.researchgate.net/publication/330638139_Artificial_Intelligence_for_Decision_Support_in_Command_and_Control_Systems. – Title from the screen.
  7. Cunneen M., Mullins M., Murphy F. Autonomous vehicles and embedded artificial intelligence: the challenges of framing machine driving decisions. Applied Artificial Intelligence. 2019, Vol. 33, №8, pp. 706–731.
  8. Phillips-Wren G. AI tools in decision making support systems: a review. International Journal on Artificial Intelligence Tools. 2012, Vol. 21, №2. URL: https://www.researchgate.net/publication/235705583 _Ai_Tools_in_Decision_Making_Support_Systems_a_Review. – Title from the screen.
  9. Petitti A., Di Paola D. A network of stationary sensors and mobile robots for distributed ambient intelligence. IEEE Intelligent Systems. 2016,Vol. 31. №6, pp. 28–34.

Received 04.04.2021

admin

Issue 2 (204), article 2

DOI:

Cybernetics and Computer Engineering, 2021, 2(204)

CHABANIUK V.S.1,2, PhD (Phys.-Math.),
Senior Researcher of the Cartography Department, Institute of Geography,
Director of “Intelligence systems-GEO” LLC,
ORCID: 0000-0002-4731-7895
email: chab3@i.ua, chab@isgeo.kiev.ua

KOLIMASOV I.M.2,
Head of Production of “Intelligence systems-GEO” LLC,
ORCID: 0000-0002-4927-4200
email: kolimasov@ukr.net

KRAKOVSKYI S.P.1,
Junior Researcher of the Cartography Department, Institute of Geography,
ORCID: 0000-0001-5164-6272
email: krakovsp@gmail.com

1Institute of Geography, National Academy of Sciences of Ukraine
44, Volodymyrska str., 01054, Kyiv, Ukraine

2“Intelligence systems-GEO” LLC,
6/44, Mykilsko-Slobidska str., 02002, Kyiv, Ukraine

CRITICAL SYSTEMIC PROPERTIES OF ELECTRONIC ATLASES OF NEW GENERATION. PART 1: PROBLEM AND RESEARCH METHODS

Introduction. The revolutionary changes in information technology of the last two decades allow the construction of electronic atlases (EA), the capabilities of which are fundamentally richer than the capabilities of “classic” EA. This is achieved through the use of the systemic properties of the new generation of EA, which are therefore named systemic. Systemic EA remain the simplest and most effective spatial information models of territorial systems allowing applying them for the decision of many practical problems.

The purpose of the paper is to formulate the need for systemic EA and describe methods for studying their systemic properties. These methods will be used to find and describe critical systemic properties without which EA cannot be systemic. The methods are founded on Relational Cartography and Model-Based Engineering.

Results. The evolution of “classic” EA is considered: from paper atlases and their images to analytical atlases. It is shown that on the imaginary border of classic and nonclassic EA there are already new generation EA — systemic EA. Both the theory and practice of such systemic EA have many unresolved problems. Some of them are described in the article. The authors believe that many problems can be solved by implementing the critical systemic properties of EA. Two methods are used to study the problems and to prove the results: Conceptual frameworks and Solutions frameworks. Both the methods themselves and the possibility of their application to find the critical systemic properties of the new generation of EA are described.

Conclusions. The main problems of electronic atlases of the new generation are described and their solution is offered by a method of Conceptual frameworks and a method of Solutions framework.

Keywords: systemic electronic atlas, Conceptual framework, Solutions framework, critical system property.

Download full text!

REFERENCES

  1. Hurni Lorenz, Shekhar Shashi, Xiong Hui, Zhou Xun. Atlas Information Systems Eds. Encyclopedia Of GIS.- Springer, 2017, 2nd Ed.- 2507, pp.85-92.
  2. Large Encyclopedic Dictionary. Ch. editor Prokhorov A.M. Soviet Encyclopedia, 1993. 1628 p.
  3. Salichtchev K.A. Cartography. Textbook.- M.: MSU Publishing, 3rd Ed. 1990. 400 p. (in Russian)
  4. Vozenilek Vit. Aspects of the Thematic Atlas Compilation, pp. 3-12. In: Brus Jan, Vondrakova Alena, Vozenilek Vit, Eds. Modern Trends in Cartography: Selected Papers of CARTOCON 2014.- Springer, 2015.- 534 p.
  5. URL: https://icaci.org/, (Last accessed: 05.05.2021)
  6. Etymological dictionary of the modern Russian language. Compiled by A.K. Shaposhnikov: in 2 volumes. M.: Flinta, 2016, 2nd ed. stereotyped. V. 1, 584 p., V. 2, 576 p. (in Russian)
  7. Chabaniuk Viktor. Relational сartography: Theory and practice. Kyiv: Institute of Geography of the NAS of Ukraine. 2018, 525 p. (in Ukrainian)
  8. Cauvin Colette, Escobar Francisco, Serradj Aziz. Thematic Cartography. Volume 3: New Approaches in Thematic Cartography.- ISTE-Wiley, 2010 (Adapted and updated from two volumes Cartographie Thématique 3 et 4.-LAVOISIER, 2008), 291 (320) p.
  9. Kraak Menno-Jan, Ormeling Ferjan. Cartography: Visualization of Geospatial Data.- Prentice Hall, 2010, 3rd Ed,198 (249) p.
  10. Chabaniuk V., Dyshlyk O. Atlas Basemaps in Web 2.0 Epoch.- The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B4, 2016 XXIII ISPRS Congress, (12–19 July 2016, Prague, Czech Republic), pp. 611-618.
  11. Sui Daniel Z., Holt James B. Visualizing and Analysing Public-Health Data Using Value-by-Area Cartograms: Toward a New Synthetic Framework. Cartographica. 2008, Vol. 43, Iss. 1, pp. 3–20.
  12. Berlyant, A.M. Geoiconics. M .: Astrea, 1996, 208 p. (in Russian)
  13. Roth Robert E. Interacting with Maps: The science and practice of cartographic interaction.- The Pennsylvania State University, Doctor of Philosophy (Geography) Dissertation. 2011, 215 (225) p. DOI: 10.1179/1743277412Y.0000000019.
  14. Sieber, R. and Losang, E.: Current Challenges in Atlas Cartography, Abstr. Int. Cartogr. Assoc., 2, 32, https://doi.org/10.5194/ica-abs-2-32-2020, 2020.
  15. Vozenilek, Vit. Atlases and Systems Theory within Systematic Cartography, Abstr. Int. Cartogr. Assoc., 1, 386, 2019.
  16. Azócar Fernandez Pablo Iván, Buchroithner Manfred Ferdinand. Paradigms in Cartography: An Epistemological Review of the 20th and 21st Centuries. Springer, 2014, 150 p.
  17. Andreessen Marc. 2007. Analyzing the Facebook Platform, three weeks in [Blog post].- https://web.archive.org/web/20071002070223/http://blog.pmarca.com/2007/06/analyzing_the_f.html. (Last accessed: 03.05.2021)
  18. Pulsifer Peter L., Taylor D.R. Fraser. The cartographer as mediator: Cartographic representation from shared geographic information, pp. 149-180. In Taylor D.R. Fraser (Ed.). Cybercartography: Theory and Practice. (Modern Cartography Series 4).- Elsevier, 2005.- 574 p.
  19. Parush A., Pulsifer P.L., Philps K., Dunn G. Understanding Through Structure: The Challenges of Informational and Navigation Architecture in Taylor D.R.F. and Caquard S. (eds.) Cybercartography. Special Issue of Cartographica on Cybercartography. 2006, Vol. 41 (1), pp. 21-34.
  20. Nunaliit, URL: http://nunaliit.org (Last accessed: 05.05.2021)
  21. Hayes Amos, Pulsifer Peter L., Fiset J.P. The Nunaliit Cybercartographic Atlas Framework, pp. 129-140. In Taylor D.R. Fraser, Editor. Developments in the Theory and Practice of Cybercartography: Applications and Indigenous Mapping (Modern Cartography Series 5). Elsevier, 2014, 364 p.
  22. Reyes Maria del Carmen. Cybercartography from a Modelling Perspective, pp. 63-99. In: Taylor D.R. Fraser (Ed.). Cybercartography: Theory and Practice (Modern Cartography Series 4). Elsevier, 2005, 574 p.
  23. Reyes Carmen, Taylor D.R. Fraser, Martinez Elvia, Lopez Fernando. Geocybernetics: A new Avenue of Research in Geomatics? Cartographica: The International Journal of Geographic Information and Geovisualization. 2006, 41(1), pp. 7-20.
  24. Reyes C., Parás M. Geocybernetics: A pathway from empiricism to cognitive frameworks. GEOcibernética: lnnovating in Geomatics for Society. 2012. URL:http://www.geocibernetica.org/jou.l.f.lal/index.php/diciem_bre2012/resumen-2012-02.
  25. Taylor D.R. Fraser. Some recent developments in the theory and practice of Cybercartography, pp. 55-68. In: Taylor D.R. Fraser, Anonby Erik, Murasugi Kumiko (Eds.). Further Developments in the Theory and Practice of Cybercartography (Modern Cartography Series 9). Elsevier, 2019, 525 p.
  26. Lopez-Caloca F., Sanchez-Sandoval R., Reyes M., Lopez-Caloca A., 2014. From cybercartography to the paradigm of geocybernetics, pp. 17–32. In: Taylor D.R.F. (Ed.), Developments in the Theory and Practice of Cybercartography: Applications and Indigenous Mapping. (Modern Cartography Series 5).Elsevier, 2014, 364 p.
  27. Köbben Barend. Towards a National Atlas of the Netherlands as Part of the National Spatial Data Infrastructure. The Cartographic Journal.2013, Vol. 50, No. 3, pp. 225–231.
  28. Bär H.R., Sieber R. Towards high standard interactive atlases. In: Proceedings of the International Cartographic Conference, Beijing, China, 2001, 7 p.
  29. Sieber Rene, Serebryakova Marianna, Schnurer Raimund, Hurni Lorenz. Atlas of Switzerland Goes Online and 3D – Concept, Architecture and Visualization Methods, pp. 171-184 // Gartner Georg, Jobst Markus, Huang Haosheng, Editors. Progress in Cartography. EuroCarto 2015 (Lecture Notes in Geoinformation and Cartography. Subseries: Publications of the International Cartographic Association (ICA)).Springer, 2016, 480 p.
  30. Sieber Rene, Hollenstein Livia, Odden Benedicte, Hurni Lorenz. From Classic Atlas Design to Collaborative Platforms .The SwissAtlasPlatform Project. 25th International Cartographic Conference, Paris, 2011, 10 p.
  31. URL:https://www.atlasderschweiz.ch/. (Last accessed: 06.02.2021)
  32. Sieber René, Schmid Christoph, Wiesmann Samuel. Smart legend – smart atlas!- XXII International Cartographic Conference (ICC2005), 2005, 9 p.
  33. Lechthaler Mirjanka. Interactive and Multimedia Atlas Information Systems as a Cartographic Geo-Communication Platform, pp. 382-402 / LNG&C2010, Cartography in Central and Eastern Europe. Selected Papers of the 1st ICA Symposium on Cartography for Central and Eastern Europe. Gartner Georg, Ortag Felix (Eds.). Springer, 2010, 570 p.
  34. genderATl. URL:http://genderatlas.at/#projektinfo (Last accessed: 05.05.2021).
  35. genderATlas für die Schule. URL: http://genderatlas.at/schule/ (Last accessed:05.05.2021).
  36. Riegler M., Wenk M.L., Aufhauser E., Ledermann F., Schmidt M., Gartner G.- genderATlas Österreich – Entwicklung eines zielgruppenorientierten Online-Tools. 2015.
  37. Interactive National Atlas of Spain URL: https://interactivo-atlasnacional.ign.es, (Last accessed:05.05.2021).
  38. Interactive Atlas of Belgium URL: https://www.atlas-belgique.be, (Last accessed:05.05.2021).
  39. Geoclip framework URL:https://www.geoclip.fr, (Last accessed:05.05.2021).
  40. Huber S., Schmid C. 2ndatlas of Switzerland: interactive concepts, functionality and techniques. Proceedings of the 21stInternational Cartographic Conference, Durban, ICA, 2003, pp. 1398–1405.
  41. Alexander Christopher. The Timeless Way Of Building. Oxford University Press, 1979. 552 p.
  42. Chabaniuk V.S., Dyshlyk O.P. Conceptual Framework of the Electronic version of the National Atlas of Ukraine. Ukrainian Geographical Journal. 2014, № 2, p. 58-68. (in Ukrainian)
  43. Chabaniuk Viktor, Dyshlyk Oleksandr. GeoSolutions Framework Reinvented: Method, pp. 115-138. Analysis, Modeling and Control. Vol. 3, Collection of Scientific Papers of the Department of Applied Nonlinear Analysis. Edited by prof. Makarenko A.S. Institute for Applied System Analysis at the Igor Sikorsky Kyiv Politechnic Institute, Kyiv, 2018. 250 p.
  44. Favre Jean-Marie. Towards a Basic Theory to Model Model Driven Engineering. Proc. of the 3rd UML Workshop in Software Model Engineering (WiSME’2004), 2004, 8 p.
  45. Bhatt Niraj. MVC vs. MVP vs. MVVM.- July 18, 2009. URL: http://nirajrules.wordpress.com/2009/07/18/mvc-vs-mvp-vs-mvvm/ (Last accessed:05.05.2021).
  46. Implementing the MVVM Pattern Using the Prism Library 5.0 for WPF, 2014-may-05.- URL: https://docs.microsoft.com/en-us/previous-versions/msp-n-p/gg405484(v=pandp.40), (Last accessed:03.05.2021).
  47. Brambilla Marco, Cabot Jordi, Wimmer Manuel. Model-driven Software Engineering in Practice (Synthesis Lectures on Software Engineering). Morgan & Claypool Publishers, 2nd Ed., 2017. 209 p.
  48. Cultural heritage in the Atlas geoinformation system of sustainable development of Ukraine: L.G. Rudenko, К.А. Watering can, V.S. Chabaniuk and others. / for ed. L.G. Rudenko.- Kyiv: Institute of Geography of the National Academy of Sciences of Ukraine, 2018. 172 p. (in Ukrainian)

Received 22.03.2021

admin

Issue 2 (204), article 1

DOI:

Cybernetics and Computer Engineering, 2021, 2(204)

GRITSENKO 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
ORCID: 0000-0003-4813-6153
e-mail:  vig@irtc.org.ua 

BABAK O.V., PhD (Engineering), Senior Researcher,
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: dep175@irtc.org.ua, 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

PECULIARITIES OF INTERCONNECTION 5G, 6G NETWORKS WITH BIG DATA, INTERNET OF THINGS AND ARTIFICIAL INTELLIGENCE

Introduction. The 5G, 6G mobile technologies, which are actively developing in the world, and the Internet of Things (IoT), Big Data (BD), artificial intelligence (AI) are closely intertwined. It is important to understand the features of the relationship to effectively use them in new intelligent information technologies.

The purpose of the article is to highlight the most important features of the relationship, which are viewed on the basis of experience in implementing 5G and 6G technologies.

Results. the Internet of Things, industrial (IIoT), the Internet in total (IoE) use 5G, 6G technologies, as well as cloud, fog and boundary computing for high-speed communication with devices. Machine learning (ML), Date Mining, neural networks and simulation are used to analyze BD. AI algorithms are an integral part of all technologies, they allow you to intelligently connect and control 5G / 6G + IoT + BD + AI.

Conclusions. 5G and 6G high-speed networks, Internet of Things technology, cloud computing, big data analysis and artificial intelligence are necessary conditions for the further development of the digital economy.

Keywords: communication networks, big data, Internet of Things, artificial intelligence.

Download full text!

REFERENCES

  1. 5G and the impact it will have on our global economy. URL: https://bazisgroup.com/
    5g-and-the-impact-it-will-have-on-our-global-economy
  2. Gritsenko V.I., Surovtsev I.V., Babak O.V. 5G wireless communication system. Cybernetics and computer engineering. 2019, № 3 (197), pp. 5-19. DOI: 10.15407/kvt197.03.005
    (in Ukrainian).
  3. 6G. The Next Hyper-Connected. Experience for All. URL: https://cdn.codeground.org/nsr/ downloads/researchareas/6G%20Vision.pdf
  4. IoT technology stack — from IoT devices, sensors, actuators and gateways to IoT platforms IoT. URL: https://www.i-scoop.eu/internet-of-things-guide/iot-technology-stack-devices-gateways-platforms/
  5. Business guide to Industrial IoT (Industrial Internet of Things). URL: https://www.i-scoop.eu/internet-of-things-guide/industrial-internet-things-iiot-saving-costs-innovation/
  6. Cloud, Fog and Edge Computing: Differences and Prospects for Technology Development. URL: https://news.rambler.ru/other/42893517-oblachnye-tumannye-i-granichnye-vychisleniya-otlichiya-i-perspektivy-razvitiya-tehnologiy/ (in Russian).
  7. Development of 5G networks in the world. URL: https://www.tadviser.ru/index.php/ Статья:Развитие_сетей_5G_в_мире (in Russian).
  8. 6G — the sixth generation of mobile communications. URL: https://www.tadviser.ru/index.php/ Статья:6G_(шестое_поколение_мобильной_связи) (in Russian).
  9. Big Date. URL: https://www.it.ua/ru/knowledge-base/technology-innovation/big-data-bolshie-dannye
  10. W. Saad, M. Bennis and M. Chen, A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems.  IEEE Network. Vol. 34, no. 3, pp. 134–142, May/June 2020, DOI: https://doi.org/10.1109/MNET.001.1900287
  11. Y. Zhao, G. Yu, H. Xu. 6G Mobile Communication Network: Vision, Challenges and Key Technologies SCIENTIA SINICA Information. 2019, vol. 49, issue 8, pp. 963–987 (in Chinese), DOI: https://doi.org/10.1360/N112019-00033
  12. Artificial Intelligence. URL: https://www.it.ua/ru/knowledge-base/technology-innovation/artificial-intelligence (in Russian).
  13. Benjamin Jokela. Merging Artificial Intelligence and the Internet of Things. Control Engineering Russia. 2019, №2 (80), pp. 70–72. URL: https://controleng.ru/wp-content/uploads/8070.pdf (in Russian).
  14. Accelerating city transformation using frontier technologies. A U4SSC deliverable. URL: https://www.itu.int/en/publications/Documents/tsb/2020-U4SSC-Deliverable-Accelerating-city-transformation/index.html
  15. 6G will be 8000 times faster than 5G. iXBT.com. URL: https://www.ixbt.com/news/ 2020/02/03/6g-8000-5g.html (in Russian).
  16. Rozenberg I.N. Intelligent control. Modern management technology. ISSN 2226-9339. 2017, №4 (76). URL: https://sovman.ru/article/7608/(in Russian).
  17. http://www.idev40.eu/
  18. https://productive40.eu/
  19. http://www.afarcloud.eu/

Received 20.03.2021

admin

Issue 2 (204)

DOI:

Download Issue 2 (204) as PDF
View web version

TABLE OF CONTENTS:

Informatics and Information Technologies:

Gritsenko V.I., Babak O.V., Surovtsev I.V.
Peculiarities of Interconnection 5G, 6G Networks with Big Data, Internet of Things and Artificial Intelligence

Chabaniuk V.S., Kolimasov I.M., Krakovskyi S.P.
Critical Systemic Properties of Electronic Atlases of New Generation. Part 1: Problem and Research Methods

Intelligent Control and Systems:

Shepetukha Yu.M., Volkov O.Ye., Komar M.M.
Intellectualization of Decision Making Processes in Autonomous Control Systems

Medical and Biological Cybernetics:

Fainzilberg L.S., Solovey S.R.
Self-learning Information Technology for Detecting Respiratory Disorders in Home Conditions

Vovk М.І., Kutsiak О.А.
AI-Technology of Motor Functions Diagnostics after a Stroke

admin

Issue 1 (203), article 5

DOI:https://doi.org/10.15407/kvt203.01.077

Cybernetics and Computer Engineering, 2021, 1(203)

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

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

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

INFORMATION TECHNOLOGY FOR CLASSIFICATION OF DONOSOLOGICAL AND PATHOLOGICAL STATES USING THE ENSEMBLE OF DATA MINING METHODS

Introduction. The digital technologies implementation provides registration of large amounts of bio-medical data (ECG, EEG, electronic medical records) as a basis for assessing and predicting the patients` condition. Data Mining methods allow to identify the most informative indicators and typological groups, to classify the person` functional state and the patients` disease stages to predict their changes.

The purpose of the paper is to develop information technology for the classification of human health states using a set of Data Mining methods and to carry out its validation on examples of an operators` functional state and patient’s disease severity.

Results. The developed IT unites several stages: I — data pre-processing; II — clustering, selecting the homogeneous groups (data segmentation); III — predictors` identification; IV — classifying the studied states, development of predictive models using machine learning algorithms (Decision trees, Support vector machines, neural networks) and the method cross-validation. The proposed IT was used to classify the operators` functional statе and the patients` severity in case of disease progression.

Conclusions. The IT use to assess the operators` activity successes made it possible to identify the most informative HRV indicators, changes in which can predict the operators` reliability, taking into account the type of vegetative regulation. Assessing the disease activity of children with dysplasia with IT use made it possible to identify diagnostic markers of CCC and develop diagnostic rules for determining the stages of the disease by ECG parameters (T wave symmetry, an integral indicator of the ST_T segment shape).

Keywords: information technology, Data Mining, machine learning models, severity of the patient.

Download full text!

REFERENCES

1. Ian H. Data Mining Practical Machine Learning Tools and Techniques Witten, Eibe Frank and Mark A. Hall Data Mining: Practical Machine Learning Tools and Techniques. 3rd Edition. Morgan Kaufmann, 2011, 665 p.

2. Yoo I., Alafaireet P., Marinov M., Pena-Hernandez K., Gopidi R., Chang J. F. Data Mining in Healthcare and Biomedicine: A Survey of the Literature. Journal of medical systems. 2012, no 36(4), pp. 2431-2448.
https://doi.org/10.1007/s10916-011-9710-5

3. Chen M., Hao Y. , Hwang K., Wang L., Wang L. Disease Prediction by Machine Learning Over Big Data From Healthcare Communities. IEEE Access 2017;5:8869-8879.
https://doi.org/10.1109/ACCESS.2017.2694446

4. Safdar S., Zafar S., Zafar N., Khan N.F. Machine learning based decision supportsystems (DSS) for heart disease diagnosis: a review. Artificial Intelligence Review. 2018, 50 (4), pp. 597-623.
https://doi.org/10.1007/s10462-017-9552-8

5. Roopa C. K., Harish B. S. Survey on various Machine Learning Approaches for ECG Analysis. International Journal of Computer Applications. 2017, no 9, vol. 163, pp.25-33.
https://doi.org/10.5120/ijca2017913737

6. Mohan S., Thirumalai C., Srivastava G. Effective heart disease prediction using hybrid machine learning techniques. IEEE Access, 2019. 7:81542-81554.
https://doi.org/10.1109/ACCESS.2019.2923707

7. Goldstein B.A., Navar A.M., Pencina M.J., Ioannidis J.P. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc. 2017, Jan; 24(1):198-208.
https://doi.org/10.1093/jamia/ocw042

8. Antomonov M.Yu. Algorithmization of the choice of adequate mathematical methods in the analysis of medical and biological data. Kibernetika i vycislitel’naa tehnika. 2007, Iss. 153, pp. 12-23. (In Russian)

9. Georga E.I., Tachos N.S., Sakellarios A.I., Kigka V.I., Exarchos T.P., Pelosi G. Artificial intelligence and data mining methods for cardiovascular risk prediction Cardiovascular Computing. Methodologies and Clinical Applications. 2019, pp. 279-301
https://doi.org/10.1007/978-981-10-5092-3_14

10. Amin M., Chiam Y. Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics. 2019, Vol. 36, pp. 82-93.
https://doi.org/10.1016/j.tele.2018.11.007

11. Kaieski N., da Costa C.A., da Rosa Righi R., Lora P.S. Application of artificial intelligence methods in vital signs analysis of hospitalized patients: A systematic literature review. Applied Soft Computing. 2020, Vol. 96,
https://doi.org/10.1016/j.asoc.2020.106612

12. Owens W.D., Felts J.A., et al. A physical status classifications: A study of consistency of ratings. Anesthesiology. 1978, Vol. 49, pp. 239-243.
https://doi.org/10.1097/00000542-197810000-00003

13. Lemeshow S., Le Gall J.R: Modeling the severity of illness of ICU patients. JAMA. 1994, Vol 272, pp.1049-1055.
https://doi.org/10.1001/jama.272.13.1049

14. Le Gall J.R., Lemeshow S., Saulnier F: A new simplified acute physiology score(SAPS II) based on a European/North American multicenter study. JAMA. 1993, 270 (24), pp. 2957-2963.
https://doi.org/10.1001/jama.270.24.2957

15. Knaus W.A., Draper E.A., Wagner D.P., Zimmerman J.E: APACHE II: A severity of disease classification system. Cri.t Care Med .1985, 13:818-829.
https://doi.org/10.1097/00003246-198510000-00009

16. Lemeshow S., Teres D., Klar J., Avrunin J.S., Gehlbach S.H., Rapoport J. Mortality probability models (MPM II) based on an international cohort of intensive care unit patients. JAMA 1993, 270, pp. 2478-86
https://doi.org/10.1001/jama.270.20.2478

17. Trujillano J., Badia M, Servia L. Stratification of the severity of critically ill patients with classification trees. BMC medical research methodology. 2009, V 9, no 7, pp. 83-95.
https://doi.org/10.1186/1471-2288-9-83

18. Kim S., Kim W., Park R.W. A Comparison of intensive care unit mortality prediction models through the use of Data Mining Techniques. Health Inform Res 2011,17, pp.232-43.
https://doi.org/10.4258/hir.2011.17.4.232

19. Allyn J. et all. A comparison of a machine learning model with EuroSCORE II in predicting mortality after elective cardiac surgery: a decision curve analysis. PLoS one 2017, 12(1), pp. 1-12.
https://doi.org/10.1371/journal.pone.0169772

20. Amosov N.M. Thinking about health. Moskow: 1978, 178 p. (In Russian)

21. Baevsky R.M., Berseneva A.P. Introduction to prenosological diagnostics. Moskow: Slovo, 2008, 174 p. (In Russian)

22. HRV analysis software URL: http://www.nevrokard.eu/maini/hrv.html (last access 20.10.2020)

23. Fainzilberg L.S. Computer diagnostics based on the phase portrait of an electrocardiogram. Kyiv: Osvita Ukrainy. 2013, 191 p. (In Russian)

24. Gritsenko V.I., Fainzilberg L.S. Intelligent information technologies in digital medicine on the example of phasagraphy. Kyiv: Naukova Dumka. 2019, 423 p. (In Russian)

25. Fainzilberg L.S., Dykach Ju.R. Linguistic approach for estimation of electrocardiograms’s subtle changes based on the Levenstein distance. Cybernetics and Computer Engineering. 2019, no. 2 (196), pp. 3-26.
https://doi.org/10.15407/kvt196.02.003

26. Gritsenko V.I., Fainzilberg L.S. Current state and prospects for the development of digital medicine. Cybernetics and Computer Engineering. 2020, no. 1 (199), pp. 59-84.
https://doi.org/10.15407/kvt199.01.059

27. Richman J.S. Randall M.J. Physiological time-series analysis using approximate entropy and sample entropy. Am J. Physiol. Heart Circ. Physiol. 2000, Vol. 278, N 6, pp. H22039-H2049.
https://doi.org/10.1152/ajpheart.2000.278.6.H2039

28. Isler Y., Kuntalp M. Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure. Computers in Biology and Medicine. 2007, Vol. 37, no. 10, pp. 1502-1510.
https://doi.org/10.1016/j.compbiomed.2007.01.012

29. Valupadasu R., Chunduri B. R., Chanagoni V. Identification of Cardiac Ischemia using bispectral analysis of ECG. Biomedical Engineering and Sciences (IECBES). 2012: IEEE EMBS Conference on, Langkawi. 2012, pp. 999-1003.
https://doi.org/10.1109/IECBES.2012.6498112

30. Romanyuk O.A., Kozak L.M., Kovalenko A.S., Kryvova O.A. Digital transformation in medicine: from formalized medical documents to information technologies of digital medicine. Cybernetics and Computer Engineering. 2018, no. 4(194), pp. 61-78.
https://doi.org/10.15407/kvt194.04.061

31. Krivova O.A., Kozak L.M. Comprehensive assessment of regional demographic development. Kibernetika i vycislitel’naa tehnika. 2015, Iss 182, pp. 70-84 (In Russian)
https://doi.org/10.15407/kvt182.02.084

32. Wolf L., Shashua A. Features Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach. J. Machine Learning Res. 2005, V. 6, pp. 1855-1887.

33. Guyon I., Elisseeff A. An Introduction to Variable and Feature Selection. Journal of Machine Learning Research. 2003, V 3, pp. 1157-1182.

34. Mandel I.D. Cluster analysis. Moscow: Finance and Statistics. 1988. 128 p. (In Russian)

35. Tzortzis G., Likas A. The MinMax k-Means clustering algorithm. Pattern Recognition. 2014, no 47 (7), pp. 2505-2516.
https://doi.org/10.1016/j.patcog.2014.01.015

36. McLachlan G. Krishnan T. The EM algorithm and extensions. New York, United States: Wiley. 1997, 274 p.

37. Wang K., Wang B., Peng L. CVAP: Validation for cluster analyses. Data Science Journal. 2009, no 8, pp. 88-93.
https://doi.org/10.2481/dsj.007-020

38. Fayn J. A classification tree approach for cardiac ischemia detection using spatiotemporal information from three standard ECG leads. IEEE Trans. Biomed. Eng. 2011, V. 58, no 1, pp. 95-102.
https://doi.org/10.1109/TBME.2010.2071872

39. Pecchia L., Melillo P. Bracale M. Remote health monitoring of heart failure with data mining via CART method on HRV features. IEEE Transactions Biomedical Engineering. 2011, V. 58(3), pp. 800-804.
https://doi.org/10.1109/TBME.2010.2092776

40. Sokolova M., Lapalme G. A systematic analysis of performance measures for classification tasks. Information processing & management. 2009, V. 45, N 4, pp. 427-437.
https://doi.org/10.1016/j.ipm.2009.03.002

41. Kalnish V.V., Shvets A.V. Information technology for psychophysiological support of high reliability of operator activities. Kibernetika i vycislitel’naa tehnika. 2014, Iss. 177, pp. 54-67. (In Russian)

42. Shvets A.V., Kalnysh V.V. Features of influence of various psychophysiological states on reliability of operator’ activity. Military medicine of Ukraine. 2009, no 1, pp. 84-91. (In Ukrainian)

43. Consolaro A., Ruperto N, Bazso A. Development and validation of a composite disease activity score for juvenile idiopathic arthritis. Arthritis & Rheumatism, 2009, vol. 61, pp. 658-666.
https://doi.org/10.1002/art.24516

44. Ansari S., Farzaneh N, Duda M, Horan K. A review of automated methods for detection of myocardial ischemia and infarction using electrocardiogram and electronic health records. IEEE reviews in biomedical engineering. 2017, Vol. 10, pp. 264-298.
https://doi.org/10.1109/RBME.2017.2757953

Received 31.11.2020

admin

Issue 1 (203), article 4

DOI:https://doi.org/10.15407/kvt203.01.060

Cybernetics and Computer Engineering, 2021, 1(203)

ARALOVA N.I.1, DSc (Engineering), Senior Researcher,
Senior Researcher of Optimization of Controlled Processes Department,
ORCID: 0000-0002-7246-2736,
e-mail: aralova@ukr.net

KLYUCHKO O.M.2, PhD (Biology), Associate Professor,
Associate Professor, Faculty of Air Navigation,
ORCID: 0000-0003-4982 7490
e-mail: kelenaXX@nau.edu.ua

MASHKIN V.I.1, PhD (Engineering), Senior Researcher,
Senior Researcher of Optimization of Controlled Processes Department,
ORCID: 0000-0002-4479-6498,
e-mail: mashkin_v@ukr.net

MASHKINA I.V.3, PhD (Engineering), Associate Professor
Associate Professor, Faculty of Information Technology and Management
ORCID: 0000-0002-0667-5749,
e-mail: mashkina@kubg.edu.ua

1V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine.
40, Acad.Glushkov av., Kyiv, 03680, Ukraine
2Electronics and Telecommunications National Aviation University,
1, Lubomyr Huzar av., Kyiv, 03058, Ukraine
3Borys Grinchenko Kyiv University,
18/2, Bulvarno-Kudriavska str., Kyiv, 04053 Ukraine, 04053

MATHEMATICAL MODEL OF FUNCTIONAL RESPIRATORY SYSTEM FOR THE INVESTIGATION OF HARMFUL ORGANIC COMPOUNDS INFLUENCES IN INDUSTRIAL REGIONS

Introduction. The areas around industrial objects, and now in regions of military actions are characterized by a high content of pollutants. Qualitative spectrum of these pollutants is extremely broad and contains both inorganic and organic elements and compounds. In particular, environmental pollution is caused by hydrocarbons with wide range of chemical structures, the study of which is very important due to their harmful and toxic influences on living organisms. The methods, currently used in medicine, give only a “thin slice” of current pathological state of organism, but they cannot predict the long-term consequences of such lesions. That is why it seems appropriate to use mathematical models that simulate the movement of organic compounds in the respiratory and circulatory systems and thus to predict possible pathologies in organs and tissues caused by hypoxic states that occur when these organs and tissues are affected.

Purpose of the paper is to create a mathematical model of functional respiratory system, which simulates the influence of external environment on the parameters of self-organization of human respiratory system in the dynamics of respiratory cycle; and thus to predict hypoxic conditions during tissue damage by hydrocarbons.

Results. The mathematical model for respiratory gases transport and mass transfer in human organism is represented as a system of differential equations, which is a controlled dynamic system, and the states of which are determined by oxygen and carbon dioxide stresses in each structural link of the respiratory system (alveoli, blood, and tissues) at each moment of time. The model is supplemented by the equations of transport of the substances in each structural link as well as by the mathematical model of organism oxygen regimes regulation. The model includes seven groups of tissues – brain, heart, liver and gastrointestinal tissues, kidneys, muscle tissue etc. The algorithm of the work and iterative procedure of research with application of suggested complex are given.

Conclusion. The proposed mathematical model for studying of the transport of organic substances in human organism which consists of differential equations of respiratory gases transport and mass transfer in it, and for the transport of organic compounds is theoretical only for today. However, in the presence of appropriate array of experimental data, it will be able to monitor the state of functional respiratory system after the pathogenic organic compounds inquiry, which may be useful in choosing of strategies and tactics for the treatment of particular lesion.

Keywords: functional respiratory system, regulation of organism oxygen regimes, harmful organic substances, hypoxic state, mathematical model of respiratory system, transport of gases by blood, self-regulation of respiratory system.

Download full text!

REFERENCES

1. Isayenko V.M., Lisichenko G.V., Dudar T.V., Franchuk G.M. et al. Monitoring and methods of measuring the parameters of the environment. Kyiv: NAU-druck. 2009. 312 p. (In Ukrainian)

2. Franchuk G.M., Zaporozhets O.A., Arkhipova G.I. Urban ecology and technoecology. Kyiv: NAU-druck. 2011. 496 p. (In Ukrainian)

3. Franchuk G.M. Isayenko V.M. Ecology, aviation and space. Kyiv. NAU-druck. 2005. 456 p. (In Ukrainian)

4. Klyuchko O.M., Biletsky A.Ya. Computer recognition of chemical substances based on their electrophysiological characteristics. Biotechnologia Acta, 2019 (12). N 5. P. 5-28.
https://doi.org/10.15407/biotech12.05.005

5. Klyuchko O.M. Information and computer technologies in biology and medicine. Kyiv: NAU-druck. 2008. 252 p. (In Ukrainian)

6. Onopchuk Yu.N. Homeostasis of functional respiratory system as a result of intersystem and system-medium informational interaction. Bioecomedicine. Uniform information space /Ed. by V.I. Gritsenko. Kiev. 2001. P. 59-84 (In Russian)

7. Onopchuk Yu.N. Homeostasis of the functional circulatory system as a result of intersystem and system-medium informational interaction. Bioecomedicine. Uniform information space / Ed. by V.I. Gritsenko. Kiev. 2001. P. 85-104 (In Russian)

8. Aralova N.I. Mathematical models of functional respiratory system for solving the applied problems in occupational medicine and sports. Saarbrucken: LAP LAMBERT Academic Publishing GmbH&Co, KG. 2019. 368 p.

9. Lyashko N.I., Onopchuk G.Yu. Pharmacological correction of organism states. Mathematical model and its analysis. Computer mathematics. 2005. N 1. P. 127-134 (In Russian)

10. Aralova N.I. Information Technologies of Decision Making Support for Rehabilitation of Sportsmen Engaged in Combat Sports DOI: 10.1615/J Automat Inf Scien.v48.i6.70. pages 68-78
https://doi.org/10.1615/JAutomatInfScien.v48.i6.70

11. Maslenikova L.D., Ivanov S.V., Fabulyak F.G. et al. Physical chemistry of polymers. Kyiv: NAU-druck. 2009. 312 p. (In Ukrainian)

12. Polynkievich K.B., Onopchuk Yu.N. Conflict situations during the regulation of the main function of organism respiratory system and mathematical models of their resolution.Cybernetics.1986. No. 3. P. 100-104 (In Russian)

13. Aralova A.A., Aralova N.I., Klyuchko O.M., Mashkin V.I., Mashkina I.V. Information system for the examination of organism adaptation characteristics of flight crews’ personnel. Electronics and control systems. 2018. 2. P. 106-113. DOI:
https://doi.org/10.18372/1990-5548.52.11882

14. Klyuchko O.M., Aralova N.I., Aralova A.A. Electronic automated work places for biological investigations Biotechnologia Acta. V. 12. N 2. P. 5-26

15. Onopchuk Yu.N., Aralova N.I., Beloshitsky P.V., Podlivaev B. A., Mastucash Yu. I. Forecasting of wrestler’ state in the combat on the base of mathematic model of functional respiratory system. Computer mathematics. 2005. N 2. P. 69-79 (In Russian)

16. Aralova N.I., Shakhlina L.Ya.-G., Futornyi S.M., Kalytka S.V. Information Technologies for Substantiation of the Optimal Course of Interval Hypoxic Training in Practice of Sports Training of Highly Qualified Sportswomen. Journal of Automation and Information Sciences, V. 52. Iss 1, pp. 41-55 DOI:
https://doi.org/10.1615/JAutomatInfScien.v52.i1.50

Received 04.11.2020

admin

Issue 1 (203), article 3

DOI:https://doi.org/10.15407/kvt203.01.039

Cybernetics and Computer Engineering, 2021, 1(203)

MISHCHENKO M.D.1, Student
e-mail: mishenkomihailo@gmail.com

GUBAREV V.F.2, DSc. (Engineering), Corresponding Member of NAS of Ukraine,
Head of the Dynamic Systems Control Department
e-mail: v.f.gubarev@gmail.com

1National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” 37, Peremohy av., 03056, Kyiv, Ukraine
2Space Research Institute of the National Academy of Sciences of Ukraine and the State Space Agency of Ukraine
40, Acad. Glushkova, 03187, Kyiv, Ukraine

HORIZON LENGTH TUNING FOR MODEL PREDICTIVE CONTROL IN LINEAR MULTI INPUT MULTI VARIABLE SYSTEMS

Introduction. There is a wide range of systems describable as multi input multi variable systems evolving in discrete time. This mathematical model is often used in engineering, but it can also be applied in many other fields. The problem of stabilization of this kind of system frequently arises. In this paper we consider the model predictive control approach to this problem. Its main principle is to generate control signals by optimizing consequent system’s future dynamics on limited prediction horizon. While it demonstrates some good results, in practice we are always limited in terms of computational resources. Thus, we can optimize outcomes of our future control sequence only for limited horizon lengths. That is why it is valuable to understand how this limit affects control quality.

The purpose of the paper is to propose a way to appraise drawbacks of limiting of the prediction horizon to certain length for a particular system, so that we can make informed choice of such limit and therefore choose controller’s microprocessor with sufficient computing power.

Methods. Several indexes which characterize the stabilization process are defined. Their heatmaps built against system’s initial state are used as a convenient visualization of how system’s stabilization dynamics changes depending on its initial state and of drawbacks induced by prediction horizon length limiting. Such heatmaps were built for several prominent example systems with different structures by performing corresponding series of computational experiments.

Results. Drawbacks of prediction horizon length limiting vary from severe to completely nonexistent depending on the system’s structure and representation. These drawbacks relax with increase of this limit. Simple future state’s norm minimizing objective function gives best results with systems whose natural response matrix is not defective and is represented in real Jordan form. Otherwise results worsen dramatically.

Conclusions. The stabilization dynamics depends largely on the system’s structure. Therefore, it is advised to take it into account and build heatmaps of aforementioned indexes to decide on prediction horizon length limit. A good system’s representation can improve stabilization time with limited prediction horizon length. Also, the function of minimum required stabilization time for initial state can be treated as an ideal objective function, but finding this function for a particular system is problematic.

Keywords: MPC, MIMV, heatmap, control synthesis, discrete controllable system, linear system.

Download full text!

REFERENCES

1. Roberts F. Discrete Mathematical Models with Applications to Social, Biological, and Environmental Problems. Englewood Cliffs, Prentice-Hall, 1976. 559 p.

2. Romanenko V. D., Milyavskiy Yu. L. Ensuring the sustainability of pulse processes in cognitive maps on the basis of the models in the states space. System research and information technologies. 2014, N 1, pp. 26-42. (In Russian)

3. Romanenko V.D., Milyavskiy Y.L. Impulse Processes Stabilization in Cognitive Maps of Complex Systems Based on Modal State Regulators. Kibernetika i vycislitelnaa tehnika. 2015, Iss. 179, pp. 43-55. (In Russian)
https://doi.org/10.15407/kvt179.01.043

4. Kailath, T. Linear systems. Englewood Cliffs, NJ: Prentice-Hall, 1980.

5. Chen, C.-T. Linear system theory and design. NY: Oxford University Press. 1999.

6. Gubarev V.F., Mishchenko M.D., Snizhko B.M. Model Predictive Control for Discrete MIMO Linear Systems. In: Kondratenko Y., Chikrii A., Gubarev V., Kacprzyk J. (eds) Advanced Control Techniques in Complex Engineering Theory and Applications. Studies in Systems, Decision and Control. 2019, vol. 203, pp. 63-81 Springer, Cham. https://doi.org/10.1007/978-3-030-21927-7_4

7. Mishchenko M.D., Gubarev V.F. Methods of Model Predictive Control for Discrete Multi-Variable Systems with Input. Cybernetics and Computer Engineering. 2020, 1(199), pp. 39-58.
https://doi.org/10.15407/kvt199.01.039

8. Vandenberghe, L. The cvxopt linear and quadratic cone program solvers. March 2010 http://www.ee.ucla.edu/~vandenbe/publications/coneprog.pdf, (Last accessed: 20.12.2020)

Received 24.12.2020

admin

Issue 1 (203), article 2

DOI:https://doi.org/10.15407/kvt203.01.026

Cybernetics and Computer Engineering, 2021, 1(203)

Anisimov A.V., DSc (Phys & Math), Corresponding Member
of National Academy of Sciences of Ukraine,
Dean of the Faculty of Computer Science and Cybernetics
ORCID: 0000-0002-1467-2006
e-mail: anatoly.v.anisimov@gmail.com

Bevza M.V., PhD student
ORCID: 0000-0002-2697-4968
e-mail: maksymbevza@gmail.com

Bobyl B.V., PhD student
ORCID: 0000-0002-9612-1071
e-mail: bobylbohdan@gmail.com

Taras Shevchenko National University of Kyiv
60, Volodymyrska st., Kiyv, 01033, Ukraine

PREDICTION OF AUDIENCE REACTION ON TEXT-VISUAL CONTENT USING NEURAL NETWORKS

Introduction. Social networks create highly-personalized experiences for their users, giving them an opportunity to follow pages of other users that publicize relevant and interesting content for them. Authors of the content create visual and text content that later receive feedback from their followers in the form of likes, shares and comments.

The purpose of the paper is to build a system that can predict the reaction of the audience on the post and account for all the specialties of the page itself, its audience, the author and variety of possible reactions. In our work we explain the process of the neural network training, that gives the ability to train the neural network for each particular page and audience to get better quality of the algorithms work.

Results. We have created a system that processes both visual and textual part of the content and gives the program the full context of the publication that algorithm will process. The features of the text and image part of the content has been received via processing the data with state-of-the-art neural networks such as BERT and VGG-16.

Conclusions. The result of the work is a state-of-the-art algorithm that can predict reactions of the audience on each publication of the personal page of a user of social media.

Keywords: artificial intelligence, natural language processing, computer vision, social networks.

Download full text!

REFERENCES
1. De Fina, A. Storytelling and audience reactions in social media. Language in Society, 2016, 45, 473-498.
https://doi.org/10.1017/S0047404516000051

2. Gaspar, R., Pedro, C., Panagiotopoulos, P., Seibt, B. Beyond positive or negative: Qualitative sentiment analysis of social media reactions to unexpected stressful events. Comput. Human Behav. 2016, 56, 179-191.
https://doi.org/10.1016/j.chb.2015.11.040

3. Cliche, M. BB_twtr at SemEval-2017 task 4: Twitter sentiment analysis with CNNs and LSTMs. Proceedings of the 11th international workshop on semantic evaluations (SemEval-2017), pp. 573-580.
https://doi.org/10.18653/v1/S17-2094

4. Vaswani A., Shazeer N., Parmar N., Uszkoreit Ja., Jones L., Gomez A.N., Kaiser K., Polosukhin I. Attention is all you need. In Advances in Neural Information Processing Systems, 2017, pp. 6000-6010.

5. Devlin J, Chang M-W., Lee K., Toutanova K.. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.

6. Simonyan K., Zisserman A., Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv e-prints, 2014

7. Russakovsky O. ImageNet Large Scale Visual Recognition Challenge, arXiv e-prints, 2014.

8. He K., Zhang X., Ren S., Sun S. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. IEEE International Conference on Computer Vision (ICCV), 2015. pp. 1026-1034.
https://doi.org/10.1109/ICCV.2015.123

9. Glorot X., Bengio Y. Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:249-256, 2010.

10. Bishop C. M. Neural networks and machine learning. Berlin: Springer, 1998. 353 p.

11. He K., Zhang X., Ren S., Sun S. Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV:IEEE, 2016. pp. 770-778.
https://doi.org/10.1109/CVPR.2016.90

Received 30.11.2020