Article Review Procedure
Academic Areas and Subjects
Applied Aspects of Information Technology
Search by article
Vol. 4 № 1
Vol. 4 № 2
Vol. 3 № 1
Vol. 3 № 2
Vol. 3 № 3
Vol. 3 № 4
Vol. 2 № 1
Vol. 2 № 2
Vol. 2 № 3
Vol. 2 № 4
Vol. 1 № 1
5 Oct 2021
On October 5, 2021, a business meeting was held between representatives of the EPAM Systems IT Company Denis Grinev and Sergey Garashchuk with the Rector of the State University “Odessa Polytechnic” Gennadii Alexandrovich Oborskiy
17 Sept 2021
International Summer School
15 July 2021
Until November 1, 2021, enrollment in the double degree program of Slovakia 2ouble Degree is carried out.
MODELS AND METHODS OF INTELLECTUAL ANALYSIS FOR MEDICAL-SOCIOLOGICAL MONITORING’S DATA BASED ON THE NEURAL NETWORK WITH A COMPETITIVE LAYER
In this scientific publication, we suggest using the system of intellectual analysis of medical and sociological monitoring’s data using a neural network with a competitive Kohonen layer to automate the process of obtaining knowledge (metadata) about the state of public health of the target audience. The following specialized tools have been developed to implement the system: models and a method for presenting detailed and aggregated medical and sociological data in area of primary and secondary features; the method of neural network classification of respondents based on machine learning of a neural network with a competitive layer; the procedure for labeling neurons of the Kohonen layer, taking into account the classification decisions received from the sociologist-analyst (initial markers). At at the first step, a two-dimensional histogram of pairwise coincidences of neuron numbers and existing initial class markers was constructed, and then it was corrected by lines and by columns in accordance with the developed rule. The result of the correction is the correspondence matrix of the numbers of neurons of the Kohonen layer and existing markers of classification decisions. The testing of the developed models and methods is based on a system of intellectual analysis using real medical-sociological monitoring’s data. The research results show that it is possible to increase the relative share of correct classification decisions by an average of 20 % and reduce the share of false decisions by 50% compared with the sociologist-analyst for tasks of intellectual analysis of medical and sociological monitoring’s data. These tasks were related to determining the working conditions of respondents.
, Postgraduate student of Information Systems Department
( email@example.com )
Olena O. Arsirii
, Dr. of Tech. Sciences, Professor
( firstname.lastname@example.org )
data mining; medical and sociological monitoring; neural networks with a competitive layer
1. Efimenko, S. A. (2019). “Mediko - sotsiologicheskiy monitoring kak instrument sovre-mennyih tehnologiy v upravlenii zdorovem patsientov”. [Medical and sociological monitoring as an instrument of modern technologies in managing patient health], Internet conference Health: problems of organization, management and levels of responsibility [Electronic resource]. – Available at: http://ecsocman.hse.ru/text/16207043/. – Active link 01.07.2019 (in Russian).
2. Rudenko, A. I., & Arsirii, E. A. (2018). “Metodika intellektualnogo analiza slabostrukturirovannyih mnogomernyih dannyih sotsiologicheskih oprosov”. [The methodology of intellectual analysis of poorly structured multidimensional data from sociological surveys], Materials of the Eighth International Conference of Students and Young Students of Modern Information Technology 2018 (Modern Information Technology systems 2018) (May 23-25, 2018) / MES of Ukraine; Odessa Nat. polytech. un-t; Int Compute, Odessa, Ukraine, Ecology, pp. 168-169 (in Russian).
3. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). “From Data Mining to Knowledge Discovery in Data bases”. [Текст], AI Magazine, Vol. 17, No. 3, pp. 37-54. DOI: https://doi.org/10.1609/aimag.v17i3.1230.
4. Semenov, V. E. (2009). “Analiz i interpretatsiya dannyih v sotsiologii: uchebnoe posobie”. [Analysis and interpretation of data in sociology: a training manual], Vladimir state. un-t. Vladimir, Russian Federation, Publishing House of VlSU, 131 p. ISBN 978-5-89368-916-7 (in Russian).
5. Kislova, O. N. (2005). “Intellektualnyiy analiz dannyih: vozmozhnosti i perspektivyi primeneniya v sotsiologicheskih issledovaniyah. MetodologIya, teorIya ta praktika sotsIologIchnogo analIzu suchasnogo suspIlstva”. [Data Mining: Possibilities and Perspectives of Application in Sociological. Methodology, theory and practice of sociological analysis of modern society: Collection of scientific works Research], ZbIrnik naukovih prats, Kharkiv, Ukraine, pp. 237-243 (in Russian).
6. Babilunha, O., Arsirii, E. A., Manikaeva, O., & Rudenko, O. (2018). “Automation of the preparation process weakly-structured multi-dimensional data of sociological surveys in the data mining system”. Herald of Advanced Information Technology, Vol. 1, No. 1, pp. 11-20. DOI://10.15276/hait.01.2018.1.
7. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. & Wirth, R. (2000). “CRISP-DM 1.0: Step- by-Step Data Mining Guide”. SPSS, Copenhagen.
8. Wirth, R., & Hipp, J. (2000). “CRISP-DM: Towards a Standard Process Model for Data Mining”, Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, pp. 29-30.
9. Praveen, S., & Chandra, U. (2017). “Influence of Structured, SemiStructured, Unstructured data on various data models”, International Journal of Scientific & Engineering Research, Vol. 8, Issue 12, pp. 67-69. 10. Corbetta, Р. (2011). “Social Research: Theory, Methods and Techniques”, London: Sage, 328 p.
11. Hunter, M. G. (2002). “The Repertory Grid Technique: A Method for the Study of Cognition in Information Systems”, MIS Quarterly, 26(1),
12. Kim, J. O., & Muller, C. W. (1989). “Faktornyiy, diskriminantnyiy i klasternyiy analiz”. [Factor, discriminate and cluster analysis], Moscow, Russian Federation, Finance and Statistics (in Russian).
13. Härdle, W., & Simar, L. (2012). “Applied Multivariate Statistical Analysis Free preview”, Berlin; New York : Springer, 486 p.
14. Witten, I. H.,& Frank, E. (2005). “Data Mining: Practical Machine Learning Tools and Techniques” (Second Edition), Morgan Kaufmann, Germany. ISBN 0-12-088407-0 https://www.cs.waikato.ac.nz/~ml/weka/ book.html.
15. Chapelle, O, Schölkopf, B, & Zien, A. (2006). “Semi-Supervised Learning (Adaptive Computation and Machine Learning series)”, The MIT Press, (September 22, 2006), 528 р.
16. Merkert, J., Mueller, M., & Hubl, M. A (2015). “Survey of the Application of Machine Learning in Decision Support Systems”, Twenty-Third European Conference on Information Systems (ECIS 2015), Münster, Germany, pp. 1-15.
17. Arsiriy, E. A., Manikaeva, O. S., Vasilevskaya, A. P. (2015). “Razrabotka podsistemyi podderzhki prinyatiya resheniy v sistemah neyrosetevogo raspoznavaniya obrazov po statisticheckoy informatsii”. [Development of a decision support subsystem in neural network pattern recognition systems based on statistical information], East European Journal of Advanced Technologies, Vol. 6, No. 4 (78), pp. 4-12.
18. Barsegyan, A. A., Kupriyanov, M. S., Stepanenko, V. V., & Holod, I. I. (2004). “Metodyi i modeli analiza dannyih: OLAP i Data Mining”. [Methods and models of data analysis: OLAP and Data Mining], SPb., Russian Federation, BHV-Petersburg, 336 p.: ill. (in Russian)
19. Arsiri, O., Antoshchuk, S., Babilunha, O., Manikaeva, O., & Nikolenko, A. (2019). “Intellectual Information Technology of Analysis of Weakly-Structured Multi-Dimensional Data of Sociological Research”, International Scientific Conference “Intellectual Systems of Decision Making and Problem of Computational Intelligence” ISDMCI 2019, Lecture Notes in Computational Intelligence and Decision Making, pp. 242-258. [Electronic resource]. – Available at: https://link.springer.com /chapter/10.1007/978-3-030-26474-1_18.
20. Kohonen, T. (1990). “The self-organizing map”, Proceedings of the IEEE, Vol. 78, Issue 9, pp. 1464-1480. Doi: 10.1109/5.58325.
21. (2019). “Dannyie sotsiologicheskogo issledovaniya “Ukraina–stil zhizni” [Data from the sociological study “Ukraine– Lifestyle”]. [Electronic resource]. – Access mode: http://edukacjainauka.pl/limesurvey/ index. php/lang-pl 23. – Active link 01.07.2019 (in Russian).
Vol. 2 № 3, 2019
17 Oct 2021
Search by author
Information Systems and Technologies
1. Models and Methods of Information Technology
2. Design of Information Systems and Technologies
3. Mathematical Issues of Information Technologies
4. Innovative Technologies in Education, Culture and art
5. Game Technologies, Augmented and Virtual Reality
6. Theoretical and Applied Issues of Computer Science
7. Project, Program and Portfolio Management
Digital control of Technical and Social Systems
1. Adaptive and optimal Control Systems
2. Parametric and System Identification
3. Interconnected Systems and Systems with Distributed Parameter
4. Renewable Energy Systems
5. Machine Learning and Artificial Intelligence in General Technical Problems and Electromechanics
6. Management of Production and Power Plants
7. Control Systems for Robotic Systems and Complexes, Electric Vehicles
8. Diagnosis and Evaluation of Complex Systems
9. Simulation of Physical Objects and Processes
Sensor less Control Systems
Software Engineering and Systems Analysis
1. Methods and Means of Intellectual Information Processing
2. Recognition, Decision Making, Forecasting
3. Neural Network Technologies and Machine Learning Methods
4. Semantic Models. Natural Language Processing
5. Theoretical and Applied Issues of Software Engineering
6. Models and Methods of Software Quality Management
Computer Systems and Cybersecurity
1. Parallel and Distributed Information Processing
2. Internet of Things
3. Information Security and Cybersecurity
4. Computer Networks and Systems
5. Components of Robotic Systems
KarelWintersky ] [
[ © Odessa National Polytechnic University, 2018.]