NEUROBIOLOGICAL PROPERTIES OF THE STRUCTURE OF THE PARALLEL-HIERARCHICAL NETWORK AND ITS USAGE FOR PATTERN RECOGNITION
Leonid Timchenko
tumchenko_li@gsuite.duit.edu.uaState University of Infrastructure and Technology (Ukraine)
https://orcid.org/0000-0001-5056-5913
Natalia Kokriatskaia
State University of Infrastructure and Technology (Ukraine)
https://orcid.org/0000-0001-8025-8172
Volodymyr Tverdomed
State University of Infrastructure and Technology (Ukraine)
Anatolii Horban
State University of Infrastructure and Technology (Ukraine)
Oleksandr Sobovyi
State University of Infrastructure and Technology (Ukraine)
https://orcid.org/0000-0002-6287-6193
Liudmyla Pogrebniak
Kruty Heroes Military Institute of Telecommunications and Information Technologies (Ukraine)
Nelia Burlaka
Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University (Ukraine)
Yurii Didenko
State University of Infrastructure and Technology (Ukraine)
Maksym Kozyr
State University of Infrastructure and Technology (Ukraine)
https://orcid.org/0009-0007-2564-6552
Ainur Kozbakova
Almaty Technological University, The Institute of Institute of Information and Computational Technologies CS MHES (Ukraine)
Abstract
The paper presents the analysis of neurobiological data on the existence of the structure of a parallel-hierarchical network. Discussed method of parallel-hierarchical transformation based on population coding and its application for the pattern recognition task. Based on the analysis, we can conclude that using the methods proposed, it is possible to measure the geometric parameters and properties of images, which can significantly increase the efficiency of processing, in particular estimating the center of mass based on moment characteristics. Experimental results demonstrate that due to various destabilizing factors, accurately measuring the energy center coordinates of laser beam spot images is challenging. However, training the PI network and classifying the fragments into "good" and "bad" can considerably enhance the accuracy of these measurements.
Keywords:
parallel-hierarchical network, population coding, pattern recognition task, laser, center of images, proceeding of imagesReferences
[1] Avrunin O. G. et al.: Research Active Posterior Rhinomanometry Tomography Method for Nasal Breathing Determining Violations. Sensors 21, 2021, 8508 [https://doi.org/10.3390/s21248508].
DOI: https://doi.org/10.3390/s21248508
Google Scholar
[2] Avrunin O. G. et al.: Features of image segmentation of the upper respiratory tract for planning of rhinosurgical surgery. IEEE 39th International Conference on Electronics and Nanotechnology – ELNANO 2019, 485–488.
DOI: https://doi.org/10.1109/ELNANO.2019.8783739
Google Scholar
[3] Chapelle O., Manavoglu E., Rosales R.: Simple and scalable response prediction for display advertising. Transactions on Intelligent Systems and Technology – TIST 5(4), 2015, 2015, 1–34.
DOI: https://doi.org/10.1145/2532128
Google Scholar
[4] Comaniciu D., Ramesh V., Meer P.: Real-Time Tracking of Non-Rigid Objects Using Mean Shift. Conference on CVPR, 2, 2000, 1–8.
DOI: https://doi.org/10.1109/CVPR.2000.854761
Google Scholar
[5] Kukharchuk V. V. et al.: Features of the angular speed dynamic measurements with the use of an encoder. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Srodowiska – IAPGOS 12(3), 2022, 20–26.
DOI: https://doi.org/10.35784/iapgos.3035
Google Scholar
[6] Kuusilinna K. et al.: Configurable parallel memory architecture for multimedia computers. Journal of Systems Architecture 47(14–15), 2002, 1089–1115.
DOI: https://doi.org/10.1016/S1383-7621(02)00059-0
Google Scholar
[7] Kvуetnyy R. et al.: Inverse correlation filters of objects features with optimized regularization for image processing. Proc. SPIE 12476, 2022, 124760Q.
DOI: https://doi.org/10.1117/12.2664497
Google Scholar
[8] Lanitis A., Taylor C. J., Cootes T. F.: Automatic Face Identification System Using Flexible Appearance Models. Image and Vision Computing 13(5), 1995, 393–401.
DOI: https://doi.org/10.1016/0262-8856(95)99726-H
Google Scholar
[9] Orazayeva A. et al.: Biomedical image segmentation method based on contour preparation. Proc. SPIE 12476, 2022, 1247605.
Google Scholar
[10] Rabinovich Z. L., Voronkov G. S.: Representation and processing of knowledge in the interaction of human sensory and linguistic neurosystems. Cybernetics and System Analysis 2, 1998, 3–11.
Google Scholar
[11] Romanyuk O. et al.: A function-based approach to real-time visualization using graphics processing units. Proc. SPIE 11581, 2020, 115810E.
Google Scholar
[12] Sree Vani M.: Prediction of Mobile Ad Click Using Supervised Classification Algorithms. International Journal of Computer Science and Information Technologies 7(2), 2016, 623–625.
Google Scholar
[13] Timchenko L. I. et al.: Multi-stage parallel-hierarchical network as a model of a neural-like computing scheme. Cybernetics and system analysis 2, 2000, 114–134.
DOI: https://doi.org/10.1007/BF02678673
Google Scholar
[14] Timchenko L. et al.: New methods of network modelling using parallel-hierarchical networks for processing data and reducing erroneous calculation risk. CEUR Workshop Proceedingsthis link is disabled 2805, 2020, 201–212.
Google Scholar
[15] Tymchenko L. et al.: Development of a method of processing images of laser beam bands with the use of parallelhierarchic networks. Eastern-European Journal of Enterprise Technologiesthis link is disabled 6(9-102), 2019, 21–27.
DOI: https://doi.org/10.15587/1729-4061.2019.188568
Google Scholar
[16] Vasilevskyi O. M.: Assessing the level of confidence for expressing extended uncertainty: a model based on control errors in the measurement of ion activity. Acta IMEKO 10(2), 2021, 199–203.
DOI: https://doi.org/10.21014/acta_imeko.v10i2.810
Google Scholar
[17] Vasilevskyi O. et al.: A new approach to assessing the dynamic uncertainty of measuring devices. Proc. SPIE 2018, 10808, 108082E.
Google Scholar
[18] WójcikW. et al.: Information Technology in Medical Diagnostics II. Taylor & Francis Group.CRC Press, Balkema Book, London 2019.
Google Scholar
Authors
Leonid Timchenkotumchenko_li@gsuite.duit.edu.ua
State University of Infrastructure and Technology Ukraine
https://orcid.org/0000-0001-5056-5913
Authors
Natalia KokriatskaiaState University of Infrastructure and Technology Ukraine
https://orcid.org/0000-0001-8025-8172
Authors
Volodymyr TverdomedState University of Infrastructure and Technology Ukraine
Authors
Anatolii HorbanState University of Infrastructure and Technology Ukraine
Authors
Oleksandr SobovyiState University of Infrastructure and Technology Ukraine
https://orcid.org/0000-0002-6287-6193
Authors
Liudmyla PogrebniakKruty Heroes Military Institute of Telecommunications and Information Technologies Ukraine
Authors
Nelia BurlakaVinnytsia Mykhailo Kotsiubynskyi State Pedagogical University Ukraine
Authors
Yurii DidenkoState University of Infrastructure and Technology Ukraine
Authors
Maksym KozyrState University of Infrastructure and Technology Ukraine
https://orcid.org/0009-0007-2564-6552
Authors
Ainur KozbakovaAlmaty Technological University, The Institute of Institute of Information and Computational Technologies CS MHES Ukraine
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