1. William P., Badholia A. Analysis of personality traits from text-based answers using HEXACO model // 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). - IEEE, 2021. - S. 1-10.
2. William P., Badholia A. Evaluating Efficacy of Classification Algorithms on Personality Prediction Dataset // Elementary Education Online. - 2020. - T. 19. - № 4. - S. 3400-3413.
3. Bibave R. et al. A Comparative Analysis of Single Phase to Three Phase Power Converter for Input Current THD Reduction //2022 International Conference on Electronics and Renewable Systems (ICEARS). - IEEE, 2022. - S. 325-330.
4. Agamirov K. V. Problemy yuridicheskogo prognozirovaniya: metodologiya, teoriya, praktika : monografiya / pod nauch. red. R. V. Shagiyevoy. - Moskva: Yurkompani, 2015. - 406 s. - (Seriya «Aktual’nyye yuridicheskiye issledovaniya»).
5. Go A. et al. Twitter sentiment analysis // Entropy. - 2009. - T. 17. - 252 s.
6. Lima A. C. E. S., de Castro L. N. Automatic sentiment analysis of Twitter messages / 2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN). - IEEE, 2012. - S. 52-57.
7. Vedaldi A., Fulkerson B. VLFeat: An open and portable library // Computer Vision Algorithms. ACMMM. - 2010. - S. 1469-1472. https://doi.org/10.1145/1873951.1874249.
8. Mavrinskaya T. V., Loshkarov A. V., Churakova Ye. N. Obezlichivaniye personal’nykh dannykh i tekhnologii «bol’shikh dannykh» (BigData) // Interaktivnaya nauka. - 2017. - № 16. - S. 78-80.
9. Pathan M. et al. Artificial cognition for applications in smart agriculture: A comprehensive review // Artificial Intelligence in Agriculture. - 2020. - T. 4. - S. 81-95.
10. Pandya R. et al. Buildout of methodology for meticulous diagnosis of K-complex in EEG for aiding the detection of Alzheimer’s by artificial intelligence // Augmented Human Research. - 2020. - T. 5. - S. 1-8. DOI:https://doi.org/10.1007/s41133-019-0021-6.
11. Musumeci F. et al. An overview on application of machine learning techniques in optical networks // IEEE Communications Surveys & Tutorials. - 2018. - T. 21. - № 2. - S. 1383-1408.
12. Judd J. S. Learning in networks is hard // Proc. of 1st International Conference on Neural Networks, San Diego, California, June 1987. - IEEE, 1987.
13. Panchiwala S., Shah M. A comprehensive study on critical security issues and challenges of the IoT world //Journal of Data, Information and Management. - 2020. - T. 2. - S. 257-278.
14. Simon A. et al. An overview of machine learning and its applications //International Journal of Electrical Sciences & Engineering. - 2016. - T. 1. - № 1. - S. 22-24.
15. Patel D., Shah D., Shah M. The intertwine of brain and body: a quantitative analysis on how big data influences the system of sports //Annals of Data Science. - 2020. - T. 7. - S. 1-16.
16. Batoyev V. B. «Bol’shiye dannyye (Big Data)» i prediktivnaya analitika v operativno-razysknoy deyatel’nosti: problemy ispol’zovaniya i puti resheniya // Vestnik Volgogradskoy akademii MVD Rossii. - 2020. - № 1 (52). - S. 11-17.
17. Sukhodolov A. P. i dr. Big data kak sovremennyy kriminologicheskiy metod izucheniya i izmereniya organizovannoy prestupnosti // Vserossiyskiy kriminologicheskiy zhurnal. - 2019. - T. 13. - № 5. - S. 718-726.
18. Dremlyuga R. I., Reshetnikov V. V. Pravovyye aspekty primeneniya prediktivnoy analitiki v pravookhranitel’noy deyatel’nosti // Aziatsko-Tikhookeanskiy region: ekonomika, politika, pravo. - 2018. - T. 20. - № 3. - S. 133-144.
19. Nikitin Ye. V. O novykh vozmozhnostyakh primeneniya sovremennykh tsifrovykh tekhnologiy v pravookhranitel’noy deyatel’nosti // Pravoporyadok: istoriya, teoriya, praktika. - 2018. - № 4 (19). - S. 55-59.
20. Pavlichenko N. V., Tambovtsev A. I. Budushcheye professii operupolnomochennyy - Big Data i analitika // Trudy akademii upravleniya MVD Rossii. - 2020. - № 2 (54). - S. 62-68.
21. Saini M., Kapoor A. K. Biometrics in forensic identification: applications and challenges // J Forensic Med. - 2016. - T. 1. - № 108. - S. 2. DOI:https://doi.org/10.4172/2472-1026.1000108.
22. Kindt E. J. Having yes, using no? About the new legal regime for biometric data // Computer law & security review. - 2017. - T. 34. - № 3. - S. 523-538. DOIhttps://doi.org/10.1016/j.clsr.2017.11.004.
23. Tistarelli M., Grosso E., Meuwly D. Biometrics in forensic science: challenges, lessons and new technologies //Biometric Authentication: First International Workshop, BIOMET 2014, Sofia, Bulgaria, June 23-24, 2014. Revised Selected Papers 1. - Springer International Publishing, 2014. - S. 153-164. DOIhttps://doi.org/10.1007/978-3-319-13386-7_12.
24. Zeinstra C. G. et al. Forensic face recognition as a means to determine strength of evidence: a survey // Forensic Sci Rev. - 2018. - T. 30. - № 1. - S. 21-32.
25. Bouchrika I. Evidence evaluation of gait biometrics for forensic investigation //Multimedia Forensics and Security: Foundations, Innovations, and Applications. - 2017. - S. 307-326. DOI:https://doi.org/10.1007/978-3-319-44270-9_13.
26. Brandon J. Terrifying high-tech porn: creepy’deepfake’videos are on the rise // Fox news. - 2018. - T. 20 [Electronic resource] // FoxNews : site - URL: https://www.foxnews.com/tech/terrifying-high-techporn-creepy-deepfake-videos-are-on-the-rise (date of treatment: 03.05.2023).
27. Liu M., Zhang X. Deepfake Technology and Current Legal Status of It //2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022). - Atlantis Press, 2022. - S. 1308-1314. DOI:https://doi.org/10.2991/978-94-6463-040-4_194.
28. Voigt P., Von dem Bussche A. The eu general data protection regulation (gdpr) // A Practical Guide, 1st Ed., Cham: Springer International Publishing. - 2017. - T. 10. - № 3152676. - 383 s. https://doi.org/10.1007/978-3-319-57959-7.
29. Minyasheva G. I. Vyyavleniye i raskrytiye moshennichestv, sovershayemykh s ispol’zovaniyem informatsionno-telekommunikatsionnykh tekhnologiy / Sovremennyye problemy ugolovnogo protsessa: puti resheniya sbornik materialov 3-y mezhdunarodnoy konferentsii / pod obshchey red. A. Yu. Terekhova. - Ufa: Izd-vo Ufimskogo yuridicheskogo instituta MVD Rossii 2022. - S. 191-197.
30. Zheludkov M. A. Izucheniye vliyaniya novykh tsifrovykh tekhnologiy na determinatsiyu moshennicheskikh deystviy (tekhnologiya deepfake) / Razvitiye nauk antikriminal’nogo tsikla v svete global’nykh vyzovov obshchestvu : sbornik trudov po materialam vserossiyskoy zaochnoy nauchnoprakticheskoy konferentsii s mezhdunarodnym uchastiyem. - Saratov, 2021. - S. 262-270.
31. Yugay L. Yu. Moshennichestvo s ispol’zovaniyem biometricheskikh tekhnologiy: sushchnost’, riski i mery protivodeystviya // Pravovyye voprosy protivodeystviya moshennichestvu i kiberprestupleniyam. - 2021. - T. 1. - № 1. - S. 59-63.
32. Rastoropova O. V. Protivodeystviye ispol’zovaniyu iskusstvennogo intellekta v prestupnykh tselyakh // Vestnik Universiteta prokuratury Rossiyskoy Federatsii. - 2021. - T. 4. - № 84. - S. 52-58.
33. Lemaykina S. V. Aktual’nyye voprosy protivodeystviya ispol’zovaniyu tekhnologii dipfeykov // Yurist»-Pravoved». - 2022. - № 3 (102). - S. 175-178.
34. Karpika A. G. Aktual’nyye voprosy sovershenstvovaniya pravovogo i tekhnicheskogo obespecheniya protivodeystviya prestupleniyam, sovershayemym s ispol’zovaniyem tekhnologiy iskusstvennogo intellekta // Filosofiya prava. - 2021. - № 3 (98). - S. 109-113.
35. Yastrebov O. A. Pravosub»yektnost’ elektronnogo litsa: teoretiko-metodologicheskiye podkhody // Trudy Instituta gosudarstva i prava Rossiyskoy akademii nauk. - 2018. - T. 13. - № 2. - S. 36-55.