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Decision-Making Supporting Models Concerning the Internal Security of the State

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the digital society, states’ information security has become one of the key elements of ensuring the competitiveness and sustainable development of the state, guaranteeing its integrity and security in general. An important component of state security is the internal security of the state, which must ensure the personal and public safety of its citizens. Modern Ukraine is building a new system of criminal justice, which requires a new information system for risk assessment and support for optimal decision-making. Today, applied research and the development of information and analytical software for the internal security of the state have acquired a special meaning. In the paper, there is built a set of models for providing operational information for decision-making in criminal justice. This is a cluster model for creating criminal profiles of convicts, and a scoring model for identifying individual characteristics of criminals that have the greatest impact on their propensity to reoffend. The obtained models can provide reliable support for decision-making in the field of criminal justice and become part of the information support system for the internal security of Ukraine in general.
Rocznik
Strony
301--307
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wykr.
Twórcy
  • West Ukrainian National University
  • West Ukrainian National University
  • University of Bielsko-Biala, Poland
  • University of Bielsko-Biala, Poland, West Ukrainian National University
Bibliografia
  • [1] “World Prison Brief,” Institute for Crime & Justice Policy Research, 2021. Retrieved from https://www.prisonstudies.org
  • [2] World Population Review, “Incarceration Rates by Country,” 2022. Retrieved from https://worldpopulationreview.com.
  • [3] F. Itulua-Abumere, “Criminal Profiling,” London: Roehampton University, 2012. Retrieved from https://www.researchgate.net
  • [4] V. Tretynyk, and A. Chernyavskyj, “Creating a psychological portrait of a criminal offender based on methods of fuzzy logic,” 2018. Retrieved from http://pmk.fpm.kpi.ua. [in Ukrainian].
  • [5] K. Berezka, O. Kovalchuk, S. Banakh, S. Zlyvko, and R. Hrechaniuk, “A Binary Logistic Regression Model for Support Decision Making in Criminal Justice,” Folia Oeconomica Stetinensia, vol. 22(1), pp. 1-17, 2022. https://doi.org/10.2478/foli-2022-0001
  • [6] A. Babuta, M. Oswald, and C. Rinik, “Machine Learning Algorithms and Police Decision-Making,” University of Winchester, 2018.
  • [7] M. Ghasemi, D. Anvari, M. Atapour. J. S. Wormith K. C. Stockdale, and R. J. Spiteri, “The Application of Machine Learning to a General Risk-Need Assessment Instrument,” Prediction of Criminal Recidivism. Criminal Justice and Behavior, vol. 48, Issue 4, pp. 518-538, 2022. https://doi.org/10.1177/0093854820969753
  • [8] Yu. Rongqin, N. Långström, M. Forsman, A. Sjölander S Fazel., and Ya. Molero, “Associations between prisons and recidivism: A nationwide longitudinal study,” National Center for Biotechnology Information, 2022. https://www.ncbi.nlm.nih.gov
  • [9] F. J. C. E. van Ginneken, and P. Nieuwbeerta, “Climate consensus: A multilevel study testing assumptions about prison climate,” Journal of Criminal Justice, vol. 69, 2020. https://doi.org/10.1016/j.jcrimjus.2020.101693
  • [10] O. Kovalchuk, S. Banakh, M. Masonkova, V. Burdin, O. Zaverukha, and R. Ivanytskyy, “A Scoring Model for Support Decision Making in Criminal Justice”, in Proc. 12th International Conference “Advanced Computer Information Technologies,” pp. 116-120, 2022. https://doi.org/10.1109/ACIT54803.2022.9913182
  • [11] R. Berks, “Machine Learning Risk Assessments in Criminal Justice Settings,” Springer; 1st ed., 2019.
  • [12] J. Chirakijja, “The Local Economic Impacts of Prisons,” Department of Econometrics and Business Statistics Monash University, 2018. Retrieved from https://economics.smu.edu.sg
  • [13] A. Grawert, and T.-A. Craigie, “Mass. Incarceration Has Been a Driving Force of Economic Inequality,” Brennar Center for Justice, 2020. Retrieved from https://www.brennancenter.org
  • [14] “Economics of Incarceration,” Prison Policy Initiative, 2021. Retrieved from https://www.prisonpolicy.org
  • [15] M. McLaughlin, C. Pettus-Davis, D. Brown, D., C. Veeh, and T. Renn, “The Economic Burden of Incarceration in the United States,” Institute for justice research and development, 2016. Retrieved from https://ijrd.csw.fsu.edu
  • [16] M. Mumford, D. Whitmore Schanzenbach, and R. Nunn, “The economics of private prison. REPORT,” Brookings, 2020. https://www.brookings.edu.
  • [17] O. Kovalchuk, M. Shynkaryk, and M. Masonkova, (2021). “Econometric models for estimating the financial effect of cybercrimes,” in Proc. 11th International Conference “Advanced Computer Information Technologies“, pp. 381-384, 2021. https://doi.org/10.1109/ACIT52158.2021.9548490
  • [18] W. A. Petherick, and B. E. Turvey, “Criminal Profiling. In Criminal Profiling. An Introduction to Behavioral Evidence Analysis,” Fourth Edition, pp. 1210-140, 2012. https://doi.org/10.1016/C2010-0-66252-3
  • [19] “Cluster Analysis’”, Science Direct, 2015. Retrieved from https://www.sciencedirect.com
  • [20] F. Y. Jin, “Interpretable Machine Learning Credit Scoring Model,” Asia Pacific University, 2019.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f06a1bfa-eea2-4f08-9f4e-7e6813b12a69
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