A.A. Adamova1, A.V. Betskov2, D.I. Denisova3, I.S. Galishnikov4

1,3,4 Bauman Moscow State Technical University (Moscow, Russia)

2 Academy of Management of the Ministry of Internal Affairs of the Russian Federation (Moscow, Russia)

Problem setting. This article is devoted to the study of the DNA matrix in criminalistics using artificial intelligence. The basics of DNA are considered: biometric identification, DNA portrait, DNA matrix, protein and hydrogen bonds and matrix processing methods. In more detail, the article discusses the problems of matrix recognition, their solutions and the forecast of application in the future. A comparative analysis of DNA profiling methods was also carried out to identify the advantages and disadvantages of certain methods. The spheres of application of artificial intelligence in re-al life are revealed and the ways of using neural networks in criminology and methods of processing DNA matrices are given in more detail.

Target. Analysis of intelligent methods and tools in solving the problems of using DNA templates in forensic science.

Results. The authors identified the purpose of studying methods for recognizing DNA templates, relating to the topic "Neural network processing of DNA templates in forensic science." First of all, the basic concepts and problems of DNA, such as biometric identification, DNA portrait, were considered. Based on the information considered, DNA profiling methods were analyzed. In conclusion of the work done, the areas of application of neural network processing of DNA templates in the field of forensics were determined. Variants of directions have also been proposed, where artificial intelligence, a trampled DNA template, can still be used.

Practical significance. The results of the work can be used in a variety of fields. First of all, we will consider the application in the field of forensics, in particular in the techniques of fingerprinting procedures, in addition, the neural network is also used in medicine and other areas. With the help of DNA templates, the following problems can be solved: the answer to the question "who are your parents?," The search for the criminal, having only a small particle containing his DNA. The principle of DNA structure underlies the invention of many technologies. A large range of applications increases the number of problems solved. Using artificial intelligence, the accuracy of data, the speed of investigation due to neural network processing, is increased.

Adamova A.A., Betskov A.V., Denisova D.I., Galishnikov I.S. Neural network processing of DNA matrices in criminology. Neurocom­puters. 2023. V. 25. № 3. Р. 5-19. DOI: https://doi.org/10.18127/j19998554-202303-01 (In Russian)

1. Butler J.M. Advanced Topics in Forensic DNA Typing: Methodology. Academic Press. 2012. 699 p. DOI 10.1016/C2011-0-04189-3.
2. Zhivotovsky L.A. DNA in court. Chemistry and life. 2001. №12. P. 23–27. (In Russian)
3. Yasnitskiy L.N., Vauleva S.V., Safonova D.N., Cherepanov F.M. The use of artificial intelligence methods in the identification of serial killers. Criminological Journal of the Baikal State University of Economics and Law. 2015.  V. 9. № 3.  P. 423–430.  DOI 10.17150/1996-7756.2015.9(3).423-430. (In Russian)
4. Balukhto A.N., Galushkin A.I., Kovalchuk D.V., Nazarov L.E., Tomashevich N.S. Neurocomputers in applied problems of image processing. Ser. Library of the journal "Neurocomputers: development, application". V. 8. M.: Radio Engineering. 2003. (In Russian)
5. Hopfield J.J. Neural networks and physical systems with emergent collective computational abilities. USA: PNAS. 1982 2558 p. DOI 10.1073/pnas.79.8.2554.
6. Werbos P.J. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Science. Thesis (Ph.D.). Appl. Math. Harvard University. 1974.
7. Petrova V.I., Zaitseva K.V. Study of the dynamics of innovation activity in Russian regions using neural network modeling. Economic analysis: theory and practice. 2017. V. 16. № 5 (464). P. 887–901. (In Russian)
8. Glinsky V.V., Serga L.K., Zaikov K.A., Khvan M.S. Fuzzy neural networks in environmental safety assessment. Statistical issues. 2015. № 12. P. 61–68. (In Russian)
9. Kulik S.D., Zhizhilev A.V. Neural network technologies in the financial market. Neurocomputers: development, application. 2009. № 4. P. 59–78. (In Russian)
10. Stepashina E.V. Optimization of financial indicators of an enterprise based on a neural network model. Information systems and technologies. 2014. № 5 (85). P. 34–42. (In Russian)
11. Rakhmatullaev M.A., Altynbekova L.B. Artificial neural networks in the military sphere. Young scientist. 2020. № 19 (309). P. 48–53. (In Russian)
12. Bauer E.V., Voronova L.I. Application of recognition systems for weapons and military equipment. Telecommunications and information technology. 2021. V. 8. № 1. P. 87–93. (In Russian)
13. Abrosimov V.K. Artificial intelligence and the problems of the development of weapons and military equipment. Armament and economy. 2021. № 2 (56). P. 5–21. (In Russian)
14. Pomorin I.A. How humanity does not get into the neural networks of a computer. Artificial intelligence in neural networks for cinema, television and education. The world of cinema technology. 2023. V. 17. № 1. P. 3–9. (In Russian)
15. Timoshkin A.G., Vlasov A.I. On the strategy and tactics of marketing policy of a multidisciplinary computer company. Instruments and control systems. 1996. № 9. P. 59–61. (In Russian)
16. Labinsky A.Yu., Druzhkina T.A. Features of the use of genetic algorithms and neural networks. Natural and man-made risks (physical, mathematical and applied aspects). 2015. № 4. P. 56–61. (In Russian)
17. Leistner L., Buytash P. Chemistry in Criminology: Translated from Hungarian by I.V. Mishin. Edited by N.M. Kuzmin M.: World. 1990. 302 p. (In Russian)
18. Yakovets E.N. Problems of analytical work in the operational investigative activities of internal affairs bodies. M.: Izdat. house of
    I.I. Shumilova. 2005. 219 p. (In Russian)
19. Zakharov E.R., Zakharova V.O., Vlasov A.I. Methods and Algorithms for Generating a Storage Key Based on Biometric Parameters. Proceedings – 2021 International Russian Automation Conference. Sochi: Institute of Electrical and Electronics Engineers Inc. 2021.
    P. 137–141. DOI 10.1109/RusAutoCon52004.2021.9537423.
20. Ren D., Bedzyk L.A., Thomas S.M., Ye R.W., Wood T.K. Gene expression in Escherichia coli biofilms. Applied microbiology and biotechnology. 2004. № 64(4). P.515–524. DOI 10.1007/s00253-003-1517-y.
21. Coulocheri S., Pigis D., Papavassiliou K., Papavassiliou A. Hydrogen bonds in protein-DNA complexes: Where geometry meets plasticity. Biochimie. 2007. V. 89. № 11. P. 1291–1303. DOI 10.1016/j.biochi.2007.07.020.
22. Mandel-Gutfreund Y., Schueler O, Margalit H. Comprehensive Analysis of Hydrogen Bonds in Regulatory Protein DNA-Com­plexes: In Search of Common Principles. Journal of Molecular Biology. 1995. V. 253. № 2. P. 370–382. DOI 10.1006/jmbi.1995.0559.
23. Luscombe N., Laskowski R., Thornton J. Amino acid-base interactions: A three-dimensional analysis of protein-DNA interactions at an atomic level. Nucleic acids research. 2001. V. 29. № 13. P. 2860–2874. DOI 10.1093/nar/29.13.2860.
24. Kopa Yu., Momynaliev K. DNA macromatrices and transcriptional profiling. Nanoindustry. 2007. №1. P. 28–33. (In Russian)
25. Chemeris A.V., Akhunov E.D., Vakhitov V.A. DNA sequencing. M.: Nauka. 1999. 429 с. ISBN 5-02-004412-1. (In Russian)
26. Soifer V.A. Computer image processing. Part 1. Mathematical models. Soros Educational Magazine. 1996. V.2. №2. P. 118–124. (In Russian)
27. Neurocomputers in image processing systems. Ed. Galushkina A.I., Gulyaeva Yu.V. M: Radio engineering. 2003. 192 p. ISBN 5-93108-029-5. (In Russian)
28. Averyanikhin A.E., Vlasov A.I., Evdokimova E.V. Hierarchical pyramidal subdiscretization in deep convolutional networks for visual image recognition. Neurocomputers: development, application. 2021. V. 23. № 1. P. 17–31. DOI 10.18127/j19998554-202101-03. (In Russian)
29. Vlasov A.I., Papulin S.Yu. Data analysis using a histogram model of a combination of features. Neurocomputers: development, application. 2019. V. 21. № 5. P. 18–27. DOI 10.18127/j19998554-201905-02. (In Russian)
30. Glushko A.A., Busov V.D., Perederin K.D. Methods of algorithmic design of artificial intelligence. Technologies of engineering and information systems. 2019. № 2. P. 72–88. (In Russian)
31. Adamova A.A., Zaikin V.A., Gordeev D.V. Methods and technologies of machine learning and neural network technologies in computer vision problems. Neurocomputers: development, application. 2021. V. 23. № 4. P. 25–39. DOI 10.18127/j19998554-202104-03. (In Russian)
32. Dmitriev V.E., Popov D.V. Analysis of wearable devices for infrared thermography. Technologies of engineering and information systems. 2018. №2. P. 66–77. (In Russian)
33. Gridnev V.N., Sergeeva M.D., Chebova A.I. Linear models for recognition of thermal imaging images of electronic cell malfunctions. Control. Diagnostics. 2014. № 8. P. 57–66. DOI 10.14489/td.2014.08.pp.057-066. (In Russian)
34. Panfilova S.P., Vlasov A.I., Gridnev V.N., Chervinsky A.S. Contactless thermal control of electronic computing facilities. Technology and design in electronic equipment. 2007. № 6 (72). P. 42–49. (In Russian)
35. Gridnev V.N., Vlasov A.I., Panfilova S.P., Chervinsky A.S. Contactless thermal control of electronic equipment products. Electronics manufacturing. 2007. № 3. P. 25–30. (In Russian)
36. Viryasova A.Yu., Vlasov A.I., Gladkikh A.A. Neural network methods of flaw detection of integral structures. Neurocomputers: development, application. 2019. № 2. P. 54–67. DOI 10.18127/j19998554-201902-06. (In Russian)
37. Buyanov A.I., Vlasov A.I., Zagoskin A.V. Application of neural network methods in the flaw detection of printed circuit boards. Neurocomputers: development, application. 2002. № 3. P. 42–70. (In Russian)
38. Gridnev V.N., Vlasov A.I., Konstantinov P., Yudin A.V. Neural network methods of PCB flaw detection. Electronic components. 2004.
    № 8. P. 148–155. (In Russian)
39. Vlasov A.I., Zavyalov N.V., Selivanov K.V., Kalchenko I.I. Application of neural networks in detecting defects of printed circuit boards. Neurocomputers: development, application. 2022. V. 24. № 6. P. 5–19. DOI 10.18127/j19998554-202206-01. (In Russian)
40. Shakhnov V.A., Vlasov A.I., Polyakov Yu.A., Kuznetsov A.S. Neurocomputers: architecture and circuit engineering. M.: Mechanical Engineering. 2000. 24 p. (In Russian)
41. Dorrer M.G. An attempt to use neural networks to predict psychological compatibility in a group. Neuroinformatics and its applications. 1994. №1. P. 13. (In Russian)
42. Tsvetkov A.A., Shorokh D.K., Zubareva M.G., Yurkov S.V., Shuklin A.V., Zhenya A.L., Anufriev I.B. Algorithms for object recognition. Materials of the IV International Scientific Conference "Technical Sciences: problems and prospects". St. Petersburg: Own publishing house. 2016. №4. P. 20–28. (In Russian)
43. Sukhodolov A.P., Bychkova A.M. Artificial intelligence in crime counteraction, prediction, prevention and evolution. Russian Journal of Criminology. 2018. V. 12. № 6. С. 753–766. DOI 10.17150/2500-4255.2018.12(6).753-766.
44. Gritsaev S.I., Pomazanov V.V., Zabolotnaya Yu.A. Computerization of targeting and investigation planning. Polythematic online electronic scientific journal of the Kuban State Agrarian University. 2015. № 108. P. 491–499. (In Russian)
45. Gustov G.A. Program-target method of organizing the disclosure of murders. Ed. 2nd St. Petersburg: St. Petersburg Law Institute of the Prosecutor General's Office of the Russian Federation. 1997. 121 p. (In Russian)
46. Goryainova K.K., Ovchinsky V.S., Sinilova G.K. Theory of operational investigative activity. 1998. 795 p. (In Russian)
47. Starkov I.I., Vlasov A.I. Application of machine learning methods in plagiarism detection systems in order to solve the problem of highly specialized concepts. Neurocomputers: development, application. 2022. V. 24. № 6. P. 30–37. DOI 10.18127/j19998554-202206-03. (In Russian)
48. Gordeev A.Y. Prospects for the development and use of artificial intelligence and neural networks to counter crime in Russia (based on foreign experience). Scientific portal of the Ministry of Internal Affairs of Russia. 2021. № 1 (53). P. 123–135. (In Russian)