The use of copyrighted databases for the development of machine learning in the context of restrictive measures in the Russian Federation

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Рұқсат ақылы немесе тек жазылушылар үшін

Аннотация

The article analyzes possible ways of accounting for copyrights and intellectual property of copyright holders of databases. The focus is on the formation and development of machine learning in the context of restrictive measures in the Russian Federation. Theoretical and practical approaches to the possible differentiation of goals and objectives for using information from databases and taking into account the interests of their copyright holders are analyzed. Proposals have been prepared to improve the legal regulation of intellectual property copyrights in the Russian Federation in the context of sanctions in order to stimulate the development of machine learning in order to gain additional competitive advantages for students in the national or international labor market.

Толық мәтін

Рұқсат жабық

Авторлар туралы

Mikhail Afanasyev

Financial University under the Government of the Russian Federation

Хат алмасуға жауапты Автор.
Email: maafanasev@fa.ru
SPIN-код: 9327-4536

Cand. Sci. (Law), associate professor, Department of Legal Regulation of Economic Activities

Ресей, Moscow

Әдебиет тізімі

  1. Shaimieva E.Sh., Gumerova G.I. Intellectual property objects of educational organizations on digital platforms. Problems of Economics and Legal Practice. 2023. No. 3. Pp. 267–273. (In Rus.)
  2. Sviridova E.A. The legal regime of the database as an object of copyright and related rights. Problems of Economics and Legal Practice. 2021. No. 1. Pp. 140–145. (In Rus.)
  3. Korchemkina O.A. Conceptual foundations of the draft law in the field of legal regulation of databases. Gaps in Russian Legislation. 2015. No. 3. Pp. 195–198. (In Rus.)
  4. Boyarintseva O.A. Current issues of legal regulation of databases generated and used in the field of public and municipal administration. Gaps in Russian Legislation. 2019. No. 4. Pp. 240–244. (In Rus.)
  5. Astapov R.L., Mukhamadeeva R.M. Automation of machine learning parameter selection and machine learning model training. Current Scientific Research in the Modern World. 2021. No. 5-2 (73). Pp. 34–37. (In Rus.)
  6. Girfanov A.I., Papaev R.M., Zagidullin L.R. et al. Using machine learning to study animal behaviors. In: International Forum Kazan Digital Week-2022. Collection of materials of the International Forum. R.N. Minnikhanov (gen. ed.). Kazan, 2022. Pp. 751–755.
  7. Polenok M.V., Bondarenko S.V., Kozlova I.R., Yurkova O.N. On machine learning methods in making managerial decisions in the field of healthcare. In: Challenges of the digital economy: Development trends in the aftermath of the COVID-19 pandemic. Collection of articles of the IV All-Russian Scientific and Practical Conference dedicated to the Year of Science and Technology in Russia. Bryansk, 2021. Pp. 225–229.
  8. Akhatkulov S.A., Omonov A.A. Application of machine learning algorithms for adaptive programming learning. In: Innovative technologies of teaching physics-mathematical and vocational disciplines. Proceedings of the XVI International Scientific and Practical Conference. Mozyr, 2024. Pp. 142–143.
  9. Gorbunov P.M., Matskevich Yu.A., Chubar A.V. Machine learning. Automating the selection of a machine learning model. In: Robotics and artificial intelligence. Materials of the XIII All-Russian Scientific and Technical Conference with international participation. Moscow, 2021. Pp. 155–160.
  10. Gorodnichev D.Yu. Machine learning and deep learning. Modern Problems of Linguistics and Methods of Teaching Russian in Higher Education Institutions and Schools. 2022. No. 38. Pp. 278–281. (In Rus.)
  11. Saltanaeva E.A., Shakirov A.A., Gimaeva A.R. Comparison of traditional machine learning and deep learning methods. Scientific and Technical Bulletin of the Volga Region. 2023. No. 12. Pp. 379–381. (In Rus.)
  12. Akinin A.A. Preparation of databases for machine learning. In: Engineering technologies: Traditions, innovations, development vectors. Materials of the X All-Russian Scientific and Practical Conference with International Participation. Abakan, 2024. Pp. 6–7.
  13. Konovalov G.G. Application of machine learning for query optimization in database management systems. International Journal of Humanities and Natural Sciences. 2023. No. 10-2 (85). Pp. 58–61. (In Rus.)

Қосымша файлдар

Қосымша файлдар
Әрекет
1. JATS XML