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Başlık: Sustainable information technology for auditable financial anomaly prediction aligned with the EU AI Act, ESG and CSRD standards
Yazarlar: Zlobin, Mykola
Affiliation: Chernihiv Polytechnic National University
Bibliographic description (Ukraine): Zlobin M. Sustainable information technology for auditable financial anomaly prediction aligned with the EU AI Act, ESG and CSRD standards / Mykola Zlobin // ITSEC, 27-29 May 2026. — Tern. : TNTU, 2026. — P. 74–76.
Bibliographic reference (2015): Zlobin M. Sustainable information technology for auditable financial anomaly prediction aligned with the EU AI Act, ESG and CSRD standards // ITSEC, Ternopil, 27-29 May 2026. 2026. P. 74–76.
Bibliographic citation (APA): Zlobin, M. (2026). Sustainable information technology for auditable financial anomaly prediction aligned with the EU AI Act, ESG and CSRD standards. Proceedings of the 15th International Scientific and Technical Conference ITSec: Information Technology Security, 27-29 May 2026, Ternopil, 74-76. TNTU..
Bibliographic citation (CHICAGO): Zlobin M. (2026) Sustainable information technology for auditable financial anomaly prediction aligned with the EU AI Act, ESG and CSRD standards. Proceedings of the 15th International Scientific and Technical Conference ITSec: Information Technology Security (Tern., 27-29 May 2026), pp. 74-76.
Is part of: Матеріали ⅩⅤ Міжнародної науково-технічної конференції ITSec: Безпека інформаційних технологій, 2026
Proceedings of the 15th International Scientific and Technical Conference ITSec: Information Technology Security, 2026
Conference/Event: ⅩⅤ Міжнародна науково-технічна конференція ITSec: Безпека інформаційних технологій
Journal/Collection: Матеріали ⅩⅤ Міжнародної науково-технічної конференції ITSec: Безпека інформаційних технологій
Yayın Tarihi: 27-May-2026
Date of entry: 27-Ağu-2026
Yayıncı: ТНТУ
TNTU
Place of the edition/event: Тернопіль
Ternopil
Temporal Coverage: 27-29 травня 2026 року
27-29 May 2026
UDC: 004.8
502.131.1]
657.6
[341.171
061.1EU]
Number of pages: 3
Page range: 74-76
Start page: 74
End page: 76
URI: http://elartu.tntu.edu.ua/handle/lib/53887
Copyright owner: © Кафедра кібербезпеки Тернопільського національного технічного університету імені Івана Пулюя, 2026
References (International): 1. European Parliament and Council. Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence. Official Journal of the European Union. – 2024.
2. Bailey D.H., Borwein J.M., López de Prado M., Zhu Q.J. The probability of backtest overfitting. Journal of Computational Finance. – 2016. – Vol. 20, №4. – P. 39–69.
3. Dal Pozzolo A., Caelen O., Johnson R.A., Bontempi G. Calibrating probability with undersampling for unbalanced classification. 2015 IEEE Symposium Series on Computational Intelligence. – 2015. – P. 159–166.
4. Strubell E., Ganesh A., McCallum A. Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. – 2019. – P. 3645–3650.
Content type: Conference Abstract
Koleksiyonlarda Görünür:ⅩⅤ Міжнародна науково-технічна конференція ITSec: Безпека інформаційних технологій (2026)



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