Design of the architecture of an intelligent system for distributing commercial content in the internet space based on SEO-technologies, neural networks, and Machine Learning

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.164441

Keywords:

commercial content, personalization, Machine Learning, SEO-technology, search metrics, e-commerce, NLP

Abstract

We have considered a task on designing an intelligent system of commercial distribution of informational products using a personalized approach to visitors based on the categories and tags of content that interests visitors. A general standard architecture of appropriate system has been developed using methods and personalization tools in the Internet environment with a core of automated recommendation of tags (categories) in the form of a neural network with controlled training. A personalized approach to the web site user results in a higher rate of sales. The system that was developed on the basis of modern SEO technologies considering the metrics for assessing the operation of an information and search module in the system makes it possible to select relevant content according to the user's personalized interests. The system has classes and subclasses that include real commercial informational products, interrelated by the built logical links, whose application promotes the intelligent supply of content based on the personalization of needs and interests of the user. In addition, based on modern methods of Machine Learning, the designed system learns to refine the results from searching the content in demand according to the personalized user's preferences. Personalization algorithms make it possible to associate each user with a list of products that are most likely to be of interest, and can predict what customers may want to see even if they are not aware of it yet. The aim of the intelligent system of e-commerce is to represent unique content based on the personalization approach and the use of tags. In addition to a standard text introduction of categories and tags based on images and product descriptions, the designed automation process defines tags and product categories. Recognition of context using deep neural networks now provides a technology for automated addition of tags to the description of goods at e-commerce web sites. The methods can be used to categorize facial expressions and recognize emotions

Author Biographies

Vasyl Lytvyn, Lviv Polytechnic National University S. Bandery str., 12, Lvіv, Ukraine, 79013

Doctor of Technical Sciences, Professor

Department of Information Systems and Networks

Victoria Vysotska, Lviv Polytechnic National University S. Bandery str., 12, Lvіv, Ukraine, 79013

PhD, Associate Professor

Department of Information Systems and Networks

Andrii Demchuk, Ukraine National Paralympic Commitee Esplanadna str., 42/813, Kyiv, Ukraine, 01023

PhD

Ihor Demkiv, Lviv Polytechnic National University S. Bandery str., 12, Lvіv, Ukraine, 79013

Doctor of Physical and Mathematical Sciences, Associate Professor

Department of Computational Mathematics and Programming

Oksana Ukhanska, Lviv Polytechnic National University S. Bandery str., 12, Lvіv, Ukraine, 79013

PhD, Associate Professor

Department of Applied Mathematics

Volodymyr Hladun, IT Academy “STEP” Zamarstynivska str., 83a, Lvіv, Ukraine, 79019

PhD, Associate Professor

Roman Kovalchuk, Hetman Petro Sahaidachnyi National Army Academy Heroiv Maidanu str., 32, Lviv, Ukraine, 79012

PhD

Department of Engineering Mechanics (Weapons and Equipment of Military Engineering Forces)

Oksana Petruchenko, Hetman Petro Sahaidachnyi National Army Academy Heroiv Maidanu str., 32, Lviv, Ukraine, 79012

PhD

Department of Engineering Mechanics (Weapons and Equipment of Military Engineering Forces)

Lyudmyla Dzyubyk, Hetman Petro Sahaidachnyi National Army Academy Heroiv Maidanu str., 32, Lviv, Ukraine, 79012

PhD

Department of Engineering Mechanics (Weapons and Equipment of Military Engineering Forces)

Nataliia Sokulska, Hetman Petro Sahaidachnyi National Army Academy Heroiv Maidanu str., 32, Lviv, Ukraine, 79012

Hetman Petro Sahaidachnyi National Army Academy

Heroiv Maidanu str., 32, Lviv, Ukraine, 79012

References

  1. Vysotska, V., Fernandes, V. B., Emmerich, M. (2018). Web content support method in electronic business systems. Proceedings of the 2nd International Conference on Computational Linguistics and Intelligent Systems. Vol. I: Main Conference. Lviv, 20–41. Available at: http://ceur-ws.org/Vol-2136/10000020.pdf
  2. Nakamoto, S. Bitcoin: A Peer-to-Peer Electronic Cash System. Available at: https://bitcoin.org/bitcoin.pdf
  3. Mobasher, B. (2007). Data Mining for Web Personalization. Lecture Notes in Computer Science, 90–135. doi: https://doi.org/10.1007/978-3-540-72079-9_3
  4. Berko, A., Alieksieiev, V. (2018). A Method to Solve Uncertainty Problem for Big Data Sources. 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). doi: https://doi.org/10.1109/dsmp.2018.8478460
  5. Xu, G., Zhang, Y., Li, L. (2011). Web Content Mining. Web Mining and Social Networking, 71–87. doi: https://doi.org/10.1007/978-1-4419-7735-9_4
  6. Lytvyn, V., Kuchkovskiy, V., Vysotska, V., Markiv, O., Pabyrivskyy, V. (2018). Architecture of System for Content Integration and Formation Based on Cryptographic Consumer Needs. 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). doi: https://doi.org/10.1109/stc-csit.2018.8526669
  7. Lytvyn, V., Vysotska, V., Pukach, P., Nytrebych, Z., Demkiv, I., Senyk, A. et. al. (2018). Analysis of the developed quantitative method for automatic attribution of scientific and technical text content written in Ukrainian. Eastern-European Journal of Enterprise Technologies, 6 (2 (96)), 19–31. doi: https://doi.org/10.15587/1729-4061.2018.149596
  8. Gozhyj, A., Kalinina, I., Vysotska, V., Gozhyj, V. (2018). The Method of Web-Resources Management Under Conditions of Uncertainty Based on Fuzzy Logic. 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). doi: https://doi.org/10.1109/stc-csit.2018.8526761
  9. Lytvyn, V., Vysotska, V., Uhryn, D., Hrendus, M., Naum, O. (2018). Analysis of statistical methods for stable combinations determination of keywords identification. Eastern-European Journal of Enterprise Technologies, 2 (2 (92)), 23–37. doi: https://doi.org/10.15587/1729-4061.2018.126009
  10. Khomytska, I., Teslyuk, V. (2016). Specifics of phonostatistical structure of the scientific style in English style system. 2016 XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT). doi: https://doi.org/10.1109/stc-csit.2016.7589887
  11. Khomytska, I., Teslyuk, V. (2017). The Method of Statistical Analysis of the Scientific, Colloquial, Belles-Lettres and Newspaper Styles on the Phonological Level. Advances in Intelligent Systems and Computing, 149–163. doi: https://doi.org/10.1007/978-3-319-45991-2_10
  12. Dosyn, D. G., Ivantyshyn, O. L., Koshovyy, V. V., Romanyshyn, I. M., Soroka S. O. (2003). To a question on the mechanism of formation of ionospheric disturbances at groundbased artificial acoustic excitation. Proceedings of 8th International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory DIPED 2003, 211–214.
  13. Lavrenyuk, S. P., Pukach, P. Y. (2007). Mixed problem for a nonlinear hyperbolic equation in a domain unbounded with respect to space variables. Ukrainian Mathematical Journal, 59 (11), 1708–1718. doi: https://doi.org/10.1007/s11253-008-0020-0
  14. Pukach, P. Ya., Kuzio, I. V. (2013). Nonlinear transverse vibrations of semiinfinite cable with consediration paid to resistance. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 3, 82–87.
  15. Pukach, P. Ya., Kuzio, I. V. (2015). Resonance phenomena in quasi-zero stiffness vibration isolation systems. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 3, 62–67.
  16. Pukach, P. Y. (2016). Investigation of Bending Vibrations in Voigt–Kelvin Bars with Regard for Nonlinear Resistance Forces. Journal of Mathematical Sciences, 215 (1), 71–78. doi: https://doi.org/10.1007/s10958-016-2823-0
  17. Pukach, P., Il’kiv, V., Nytrebych, Z., Vovk, M. (2017). On nonexistence of global in time solution for a mixed problem for a nonlinear evolution equation with memory generalizing the Voigt-Kelvin rheological model. Opuscula Mathematica, 37 (45), 735. doi: https://doi.org/10.7494/opmath.2017.37.5.735
  18. Pukach, P. Y. (2012). On the unboundedness of a solution of the mixed problem for a nonlinear evolution equation at a finite time. Nonlinear Oscillations, 14 (3), 369–378. doi: https://doi.org/10.1007/s11072-012-0164-6
  19. Pukach, P. Y. (2014). Qualitative Methods for the Investigation of a Mathematical Model of Nonlinear Vibrations of a Conveyer Belt. Journal of Mathematical Sciences, 198 (1), 31–38. doi: https://doi.org/10.1007/s10958-014-1770-x
  20. Fedushko, S. (2014). Development of a software for computer-linguistic verification of socio-demographic profile of web-community member. Webology, 11 (2). Available at: http://www.webology.org/2014/v11n2/a126.pdf
  21. Korzh, R., Fedushko, S., Peleschyshyn, A. (2015). Methods for forming an informational image of a higher education institution. Webology, 12 (2). Available at: http://www.webology.org/2015/v12n2/a140.pdf
  22. Korzh, R., Peleschyshyn, A., Syerov, Yu., Fedushko, S. (2014). The cataloging of virtual communities of educational thematic. Webology, 11 (1). Available at: http://www.webology.org/2014/v11n1/a117.pdf
  23. Bezobrazov, S., Sachenko, A., Komar, M., Rubanau, V. (2016). The Methods of Artificial Intelligence for Malicious Applications Detection in Android OS. International Journal of Computing, 15 (3), 184–190.
  24. Dunets, O., Wolff, C., Sachenko, A., Hladiy, G., Dobrotvor, I. (2017). Multi-agent system of IT project planning. 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). doi: https://doi.org/10.1109/idaacs.2017.8095141
  25. Lytvyn, V., Vysotska, V., Pukach, P., Nytrebych, Z., Demkiv, I., Kovalchuk, R., Huzyk, N. (2018). Development of the linguometric method for automatic identification of the author of text content based on statistical analysis of language diversity coefficients. Eastern-European Journal of Enterprise Technologies, 5 (2 (95)), 16–28. doi: https://doi.org/10.15587/1729-4061.2018.142451
  26. Lytvyn, V., Vysotska, V., Pukach, P., Vovk, M., Ugryn, D. (2017). Method of functioning of intelligent agents, designed to solve action planning problems based on ontological approach. Eastern-European Journal of Enterprise Technologies, 3 (2 (87)), 11–17. doi: https://doi.org/10.15587/1729-4061.2017.103630
  27. Kanishcheva, O., Vysotska, V., Chyrun, L., Gozhyj, A. (2018). Method of Integration and Content Management of the Information Resources Network. Advances in Intelligent Systems and Computing II, 204–216. doi: https://doi.org/10.1007/978-3-319-70581-1_14
  28. Vysotska, V., Lytvyn, V., Burov, Y., Gozhyj, A., Makara, S. (2018). The consolidated information web-resource about pharmacy networks in city. CEUR Workshop Proceedings (Computational linguistics and intelligent systems), 2255, 239–255. Available at: http://ceur-ws.org/Vol-2255/paper22.pdf
  29. Lytvyn, V., Vysotska, V., Veres, O., Rishnyak, I., Rishnyak, H. (2017). The risk management modelling in multi project environment. 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). doi: https://doi.org/10.1109/stc-csit.2017.8098730
  30. Korobchinsky, M., Chyrun, L., Chyrun, L., Vysotska, V. (2017). Peculiarities of content forming and analysis in internet newspaper covering music news. 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). doi: https://doi.org/10.1109/stc-csit.2017.8098735
  31. Naum, O., Chyrun, L., Vysotska, V., Kanishcheva, O. (2017). Intellectual system design for content formation. 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). doi: https://doi.org/10.1109/stc-csit.2017.8098753
  32. Lytvyn, V., Vysotska, V., Burov, Y., Veres, O., Rishnyak, I. (2018). The Contextual Search Method Based on Domain Thesaurus. Advances in Intelligent Systems and Computing II, 310–319. doi: https://doi.org/10.1007/978-3-319-70581-1_22
  33. Veres, O., Rusyn, B., Sachenko, A., Rishnyak, I. (2018). Choosing the method of finding similar images in the reverse search system. CEUR Workshop Proceedings (Computational linguistics and intelligent systems), 2136, 99–107.
  34. Tkachenko, R., Izonin, I. (2019). Model and Principles for the Implementation of Neural-Like Structures Based on Geometric Data Transformations. Advances in Intelligent Systems and Computing, 578–587. doi: https://doi.org/10.1007/978-3-319-91008-6_58
  35. Bodyanskiy, Y., Perova, I., Vynokurova, O., Izonin, I. (2018). Adaptive wavelet diagnostic neuro-fuzzy network for biomedical tasks. 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET). doi: https://doi.org/10.1109/tcset.2018.8336299
  36. Peleshko, D., Peleshko, M., Kustra, N., Izonin, I. (2011). Analysis of invariant moments in tasks image processing. 2011 11th International Conference The Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), 263–264.
  37. Peleshko, D., Ivanov, Y., Sharov, B., Izonin, I., Borzov, Y. (2016). Design and implementation of visitors queue density analysis and registration method for retail videosurveillance purposes. 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP). doi: https://doi.org/10.1109/dsmp.2016.7583531
  38. Vitynskyi, P., Tkachenko, R., Izonin, I., Kutucu, H. (2018). Hybridization of the SGTM Neural-Like Structure Through Inputs Polynomial Extension. 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). doi: https://doi.org/10.1109/dsmp.2018.8478456
  39. Izonin, I., Trostianchyn, A., Duriagina, Z., Tkachenko, R., Tepla, T., Lotoshynska, N. (2018). The Combined Use of the Wiener Polynomial and SVM for Material Classification Task in Medical Implants Production. International Journal of Intelligent Systems and Applications, 10 (9), 40–47. doi: https://doi.org/10.5815/ijisa.2018.09.05
  40. Rashkevych, Y., Peleshko, D., Vynokurova, O., Izonin, I., Lotoshynska, N. (2017). Single-frame image super-resolution based on singular square matrix operator. 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON). doi: https://doi.org/10.1109/ukrcon.2017.8100390
  41. Tkachenko, R., Tkachenko, P., Izonin, I., Tsymbal, Y. (2017). Learning-Based Image Scaling Using Neural-Like Structure of Geometric Transformation Paradigm. Studies in Computational Intelligence, 537–565. doi: https://doi.org/10.1007/978-3-319-63754-9_25
  42. Lytvyn, V., Vysotska, V., Pukach, P., Bobyk, І., Pakholok, B. (2016). A method for constructing recruitment rules based on the analysis of a specialist's competences. Eastern-European Journal of Enterprise Technologies, 6 (2 (84)), 4–14. doi: https://doi.org/10.15587/1729-4061.2016.85454
  43. Zhezhnych, P., Markiv, O. (2018). Recognition of tourism documentation fragments from web-page posts. 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET). doi: https://doi.org/10.1109/tcset.2018.8336350
  44. Gozhyj, A., Chyrun, L., Kowalska-Styczen, A., Lozynska, O. (2018). Uniform Method of Operative Content Management in Web Systems. CEUR Workshop Proceedings (Computational linguistics and intelligent systems), 2136, 62–77. Available at: http://ceur-ws.org/Vol-2136/10000062.pdf
  45. Vysotska, V., Hasko, R., Kuchkovskiy, V. (2015). Process analysis in electronic content commerce system. 2015 Xth International Scientific and Technical Conference “Computer Sciences and Information Technologies” (CSIT). doi: https://doi.org/10.1109/stc-csit.2015.7325447
  46. Chyrun, L., Vysotska, V., Kis, I., Chyrun, L. (2018). Content Analysis Method for Cut Formation of Human Psychological State. 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). doi: https://doi.org/10.1109/dsmp.2018.8478619
  47. Gozhyj, A., Vysotska, V., Yevseyeva, I., Kalinina, I., Gozhyj, V. (2019). Web Resources Management Method Based on Intelligent Technologies. Advances in Intelligent Systems and Computing, 206–221. doi: https://doi.org/10.1007/978-3-030-01069-0_15
  48. Lytvyn, V., Vysotska, V., Veres, O., Rishnyak, I., Rishnyak, H. (2016). Content linguistic analysis methods for textual documents classification. 2016 XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT). doi: https://doi.org/10.1109/stc-csit.2016.7589903
  49. Lytvyn, V., Vysotska, V. (2015). Designing architecture of electronic content commerce system. 2015 Xth International Scientific and Technical Conference “Computer Sciences and Information Technologies” (CSIT). doi: https://doi.org/10.1109/stc-csit.2015.7325446
  50. Lytvyn, V., Vysotska, V., Kuchkovskiy, V., Bobyk, I., Malanchuk, O., Ryshkovets, Y. et. al. (2019). Development of the system to integrate and generate content considering the cryptocurrent needs of users. Eastern-European Journal of Enterprise Technologies, 1 (2 (97)), 18–39. doi: https://doi.org/10.15587/1729-4061.2019.154709
  51. Vasyl, L., Victoria, V., Dmytro, D., Roman, H., Zoriana, R. (2017). Application of sentence parsing for determining keywords in Ukrainian texts. 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). doi: https://doi.org/10.1109/stc-csit.2017.8098797
  52. Maksymiv, O., Rak, T., Peleshko, D. (2017). Video-based Flame Detection using LBP-based Descriptor: Influences of Classifiers Variety on Detection Efficiency. International Journal of Intelligent Systems and Applications, 9 (2), 42–48. doi: https://doi.org/10.5815/ijisa.2017.02.06
  53. Peleshko, D., Rak, T., Izonin, I. (2016). Image Superresolution via Divergence Matrix and Automatic Detection of Crossover. International Journal of Intelligent Systems and Applications, 8 (12), 1–8. doi: https://doi.org/10.5815/ijisa.2016.12.01
  54. Bazylyk, O., Taradaha, P., Nadobko, O., Chyrun, L., Shestakevych, T. (2012). The results of software complex OPTAN use for modeling and optimization of standard engineering processes of printed circuit boards manufacturing. 2012 11th International Conference on "Modern Problems of Radio Engineering, Telecommunications and Computer Science" (TCSET), 107–108.
  55. Bondariev, A., Kiselychnyk, M., Nadobko, O., Nedostup, L., Chyrun, L., Shestakevych, T. (2012). The software complex development for modeling and optimizing of processes of radio-engineering equipment quality providing at the stage of manufacture. TCSET’2012, 159.
  56. Riznyk, V. (2016). Multi-modular Optimum Coding Systems Based on Remarkable Geometric Properties of Space. Advances in Intelligent Systems and Computing, 129–148. doi: https://doi.org/10.1007/978-3-319-45991-2_9
  57. Teslyuk, V., Beregovskyi, V., Denysyuk, P., Teslyuk, T., Lozynskyi, A. (2018). Development and Implementation of the Technical Accident Prevention Subsystem for the Smart Home System. International Journal of Intelligent Systems and Applications, 10 (1), 1–8. doi: https://doi.org/10.5815/ijisa.2018.01.01
  58. Basyuk, T. (2015). The main reasons of attendance falling of internet resource. 2015 Xth International Scientific and Technical Conference “Computer Sciences and Information Technologies” (CSIT). doi: https://doi.org/10.1109/stc-csit.2015.7325440
  59. Pasichnyk, V., Shestakevych, T. (2016). The Model of Data Analysis of the Psychophysiological Survey Results. Advances in Intelligent Systems and Computing, 271–281. doi: https://doi.org/10.1007/978-3-319-45991-2_18
  60. Zhezhnych, P., Markiv, O. (2017). Linguistic Comparison Quality Evaluation of Web-Site Content with Tourism Documentation Objects. Advances in Intelligent Systems and Computing, 656–667. doi: https://doi.org/10.1007/978-3-319-70581-1_45
  61. Chernukha, O., Bilushchak, Y. (2016). Mathematical modeling of random concentration field and its second moments in a semispace with erlangian disrtibution of layered inclusions. Task Quarterly, 20 (3), 295–334.
  62. Davydov, M., Lozynska, O. (2017). Information system for translation into ukrainian sign language on mobile devices. 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). doi: https://doi.org/10.1109/stc-csit.2017.8098734
  63. Davydov, M., Lozynska, O. (2017). Mathematical Method of Translation into Ukrainian Sign Language Based on Ontologies. Advances in Intelligent Systems and Computing, 89–100. doi: https://doi.org/10.1007/978-3-319-70581-1_7
  64. Davydov, M., Lozynska, O. (2016). Linguistic models of assistive computer technologies for cognition and communication. 2016 XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT). doi: https://doi.org/10.1109/stc-csit.2016.7589898
  65. Mykich, K., Burov, Y. (2016). Uncertainty in situational awareness systems. 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET). doi: https://doi.org/10.1109/tcset.2016.7452165
  66. Mykich, K., Burov, Y. (2016). Algebraic Framework for Knowledge Processing in Systems with Situational Awareness. Advances in Intelligent Systems and Computing, 217–227. doi: https://doi.org/10.1007/978-3-319-45991-2_14
  67. Mykich, K., Burov, Y. (2016). Research of uncertainties in situational awareness systems and methods of their processing. Eastern-European Journal of Enterprise Technologies, 1 (4 (79)), 19–27. doi: https://doi.org/10.15587/1729-4061.2016.60828
  68. Mykich, K., Burov, Y. (2016). Algebraic model for knowledge representation in situational awareness systems. 2016 XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT). doi: https://doi.org/10.1109/stc-csit.2016.7589896
  69. Kravets, P. (2010). The control agent with fuzzy logic. Perspective Technologies and Methods in MEMS Design, 40–41.
  70. Pukach, P., Il’kiv, V., Nytrebych, Z., Vovk, M., Pukach, P. (2017). On the Asymptotic Methods of the Mathematical Models of Strongly Nonlinear Physical Systems. Advances in Intelligent Systems and Computing, 421–433. doi: https://doi.org/10.1007/978-3-319-70581-1_30
  71. Kravets, P. (2007). The Game Method for Orthonormal Systems Construction. 2007 9th International Conference – The Experience of Designing and Applications of CAD Systems in Microelectronics. doi: https://doi.org/10.1109/cadsm.2007.4297555
  72. Kravets, P. (2016). Game Model of Dragonfly Animat Self-Learning. Perspective Technologies and Methods in MEMS Design, 195–201.

Downloads

Published

2019-04-16

How to Cite

Lytvyn, V., Vysotska, V., Demchuk, A., Demkiv, I., Ukhanska, O., Hladun, V., Kovalchuk, R., Petruchenko, O., Dzyubyk, L., & Sokulska, N. (2019). Design of the architecture of an intelligent system for distributing commercial content in the internet space based on SEO-technologies, neural networks, and Machine Learning. Eastern-European Journal of Enterprise Technologies, 2(2 (98), 15–34. https://doi.org/10.15587/1729-4061.2019.164441

Issue

Vol. 2 No. 2 (98) (2019): Information technology. Industry control systems

Section

Information technology

License

Copyright (c) 2019 Vasyl Lytvyn, Victoria Vysotska, Andrii Demchuk, Ihor Demkiv, Oksana Ukhanska, Volodymyr Hladun, Roman Kovalchuk, Oksana Petruchenko, Lyudmyla Dzyubyk, Nataliia Sokulska

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.