Adhesive interaction of flexographic inks with the photopolymer printing plate surface. Printing and Publishing. Ukrainian Academy of Printing

Author(s)	Collection number	Pages	Download abstract	Download full text
Кукура Т. Ю., Вархоляк В. І.	№ 1 (87)	133-139

Summary
References

A mathematical model for analysis of influencing factors of the software complexes support automation is developed and represented in this article. The model makes it possible to calculate the probability of the belonging of the multi-layer perceptron hidden layer neurons to the influencing factors of the subjective perception of the supported programming complex by the appropriate relevant subject(s) of interaction with it. The methodological base of the research consists of: methods of research of artificial neural networks, in particular multilayer perceptron models (used to interpret the subjective perception of the object of software complexes support), methods of mathematical modeling (used to develop a mathematical model of the analysis of factors influencing the support of software complexes), as well as methods of computer design, computer modeling and computer programming for modeling the developed mathematical model, and obtaining and analyzing the relevant results of its work. Among the main theoretical results obtained, a mathematical model is developed, which enables the analysis and calculation of probabilities of belonging of the multilayer perceptron models hidden layers neurons (which interpret the processes of subjective perception of an object) to the influencing factors that affects the results of subjective perception of the object by the subjects of interaction with this object. Among the main scientific and practical results – all results obtained at the output of the developed mathematical model are important for further research into the problems of software complexes support automation. The developed mathematical model is unique and has no known analogues. The main scientific and applied problem solved by the developed mathematical model is the problem of restoring the boundaries of the influencing factors lost (blurred) when introducing multilayer perceptron models into the models of software complexes support. The main practical value is that the developed mathematical model, in fact, makes it possible to determine and establish (restore) these lost (blurred) boundaries of influencing factors. The results obtained with the help of the developed mathematical model provide an opportunity to research further scientific and applied issues of the more complex and global scientific and applied problem of software complexes support automation.

Keywords: support, program complex, influencing factors, model.

doi: 10.32403/0554-4866-2024-1-87-75-85

1. Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence: AI & SOCIETY, 35 (3), 611–623. Doi: https://doi.org/10.1007/s00146-019-00931-w (in English).
2. BEUC, 2018: Automated Decision Making And Artificial Intelligence - A Consumer Perspective. Report, 20 June 2018, Brussels. Retrieved from https://www.beuc.eu/publications/beuc-x-2018-058_automated_decision_making_and_artificial_intelligence.pdf (in English).
3. Humble, Jez, & Farley, David. (2010). Continuous Delivery: Reliable Software Releases Through Build, Test, and Deployment Automation (3rd ed.). Addison Wesley (in English).
4. Jaihar, J., Lingayat, N., Vijaybhai, P. S., Venkatesh, G., & Upla, K. (2020). Smart home automation using machine learning algorithms, in: 2020 International Conference for Emerging Technology (INCET), IEEE, 1–4. doi: 10.1109/INCET49848.2020.9154007 (in English).
5. Kurtanović, Z., & Maalej, W. (2017). Automatically classifying functional and non-functional requirements using supervised machine learning. In 2017 IEEE 25th International Requirements Engineering Conference (RE), 490−495 (in English).
6. Landau, I., & Landau, V. (2017). From data driven decision making (DDDM) to automated datadriven model based decision making (MBDM). hal-01527766. Retrieved from https://hal.archives-ouvertes.fr/hal-01527766 (in English).
7. Larus, J., Hankin, C., Carson, S. G., Christen, M., Crafa, S., Grau, O., … & Werthner, H. (2018). When Computers Decide: European Recommendations on Machine-Learned Automated Decision Making. New York, NY, USA: ACM, Doi: https://doi.org/10.1145/3185595 (in English).
8. Lu, M., & Liang, P. (2017 Jun 15). Automatic classification of non-functional requirements from augmented app user reviews. In Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering, 344−353 (in English).
9. Mohanty, L., & Tajammul, M. (2022). DevOps CI Automation Continuous Integration: International Research Journal of Engineering and Technology (IRJET), 9, 3, 1221–1226 (in English).
10. Salameh, H. (2019). The impact of devops automation, controls, and visibility practices on software continuous deployment and delivery, in: Proceedings of the 2nd International Conference on Research in Management and Economics, 22–46 (in English).
11. Sushma, T. N. Automation of software development using devops and its benefits. IJERT. 2020. 9 (6). IJERTV9IS060369. DOI: 10.17577/IJERTV9IS060369 (in English).
12. Trudova, A., Dolezel, M., & Buchalcevova, A. (2020). Artificial intelligence in software test automation: A systematic literature review, in Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering, 181–192. doi: 10.5220/0009417801810192 (in English).
13. Vadlamudi, Satya Gautam et al. (2016). Proactive Decision Support using Automated Planning, arXiv preprint arXiv:1606.07841 (in English).
14. Vang-Mata, R. (2020). Multilayer Perceptrons: Theory and Applications, New York, Nova Science Publishers (in English).
15. Wettinger, J., Andrikopoulos, V., & Leymann, F. (2015). Automated capturing and systematic usage of devops knowledge for cloud applications, in Cloud Engineering (IC2E), 2015 IEEE International Conference on, 60–65 (in English).