Neural Network Method for Determining Sanctions’ Impact on the Administrative Offence Level

Vladov, Serhii; Vysotska, Victoria; Voloshanivska, Tetiana; Podorozhnii, Yevhen; Hanenko, Ihor; Nazarkevych, Mariia; Hovorov, Valerii; Shopina, Iryna; Zherebtsov, Denys; Pitomets, Artem

doi:10.3390/app16073340

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Open AccessArticle

Neural Network Method for Determining Sanctions’ Impact on the Administrative Offence Level

by

Serhii Vladov

^1,2,*

,

Victoria Vysotska

^2,3

,

Tetiana Voloshanivska

⁴

,

Yevhen Podorozhnii

⁵

,

Ihor Hanenko

⁶

,

Mariia Nazarkevych

^3,*

,

Valerii Hovorov

⁷,

Iryna Shopina

⁸,

Denys Zherebtsov

⁷ and

Artem Pitomets

⁷

¹

Department of Scientific Activity Organisation, Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine

²

Department of Combating Cybercrime, Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine

³

Information Systems and Networks Department, Lviv Polytechnic National University, 12, Bandera Street, 79013 Lviv, Ukraine

⁴

Department of Criminal Law Disciplines, Institute of Law and Security, Odesa State University of Internal Affairs, 1 Uspenska Street, 65014 Odesa, Ukraine

⁵

Department of Civil, Labour and Business Law, Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine

⁶

Department of Criminal Procedure and Criminalistics, Odesa State University of Internal Affairs, 1 Uspenska street, 65014 Odesa, Ukraine

⁷

Department of Law Enforcement Activity and Policeistics, Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine

⁸

Department of Administrative Law Disciplines, Educational and Scientific Institute of Law and Law Enforcement Activity, Lviv State University of Internal Affairs, 26, Horodotska Street, 79007 Lviv, Ukraine

^*

Authors to whom correspondence should be addressed.

Appl. Sci. 2026, 16(7), 3340; https://doi.org/10.3390/app16073340

Submission received: 5 March 2026 / Revised: 24 March 2026 / Accepted: 27 March 2026 / Published: 30 March 2026

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Abstract

A neural network simulation–regression method was developed to assess the impact of sanctions on the level of administrative offences under fragmented, noisy, and short administrative time series. The study addresses the problem of quantifying and predicting changes at the offence level as a sanction size function, using detection probability, prior violation level, compliance costs, and auxiliary contextual factors. The proposed framework combines a hybrid MLP–LSTM neural network, double machine learning-based orthogonal causal estimation, the simulation-based generation of counterfactual scenarios through domain randomization, multiple imputation for missing data, debiasing procedures, and ensemble uncertainty estimation. The contribution to administrative law consists of a quantitative tool creation for substantiating and optimising sanction policy, assessing heterogeneous effects, and supporting evidence-based rulemaking and law enforcement decisions. In comparative experiments, the developed method achieved an RMSE of 8…12%, a prediction accuracy of 93…96%, an overall accuracy of 95%, a precision of 94%, a recall of 93%, and an F1-score of 93.5%, thereby outperforming contemporary econometric, simulation, causal machine learning, and predictive machine learning approaches used for sanction effect modelling. Additional verification through nonparametric statistical testing cponfirmed that the proposed model’s superiority over the compared algorithms is statistically significant across the main quality metrics, which strengthens the evidence for its robustness and practical value in sanction policy analysis under fragmented administrative data conditions.

Keywords: neural network; sanction impact; administrative offences; simulation regression; causal effects; sensitivity analysis; policy optimisation; “what-if” modelling; situation analysis; uncertainty assessment; disturbances dynamics; information security; cyber resilience; threat detection

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MDPI and ACS Style

Vladov, S.; Vysotska, V.; Voloshanivska, T.; Podorozhnii, Y.; Hanenko, I.; Nazarkevych, M.; Hovorov, V.; Shopina, I.; Zherebtsov, D.; Pitomets, A. Neural Network Method for Determining Sanctions’ Impact on the Administrative Offence Level. Appl. Sci. 2026, 16, 3340. https://doi.org/10.3390/app16073340

AMA Style

Vladov S, Vysotska V, Voloshanivska T, Podorozhnii Y, Hanenko I, Nazarkevych M, Hovorov V, Shopina I, Zherebtsov D, Pitomets A. Neural Network Method for Determining Sanctions’ Impact on the Administrative Offence Level. Applied Sciences. 2026; 16(7):3340. https://doi.org/10.3390/app16073340

Chicago/Turabian Style

Vladov, Serhii, Victoria Vysotska, Tetiana Voloshanivska, Yevhen Podorozhnii, Ihor Hanenko, Mariia Nazarkevych, Valerii Hovorov, Iryna Shopina, Denys Zherebtsov, and Artem Pitomets. 2026. "Neural Network Method for Determining Sanctions’ Impact on the Administrative Offence Level" Applied Sciences 16, no. 7: 3340. https://doi.org/10.3390/app16073340

APA Style

Vladov, S., Vysotska, V., Voloshanivska, T., Podorozhnii, Y., Hanenko, I., Nazarkevych, M., Hovorov, V., Shopina, I., Zherebtsov, D., & Pitomets, A. (2026). Neural Network Method for Determining Sanctions’ Impact on the Administrative Offence Level. Applied Sciences, 16(7), 3340. https://doi.org/10.3390/app16073340

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