Научный журнал

«ИЗВЕСТИЯ КАБАРДИНО-БАЛКАРСКОГО

НАУЧНОГО ЦЕНТРА РАН»

Свидетельство о регистрации ПИ № 77-14936 от 20 марта 2003 г. выдано Министерством РФ по делам печати, телерадиовещания и средств массовых коммуникаций

Журнал основан в 1998 г. Выходит 6 раз в год

ISSN 1991-6639 (печатная версия), ISSN 2949-1940 (электронная версия)

Текущий выпуск

Отправить статью

Перейти на старую версию журнала

Реферативные базы

The use of mivar expert systems for diagnosis of bacterial antibiotic resistance

N.Ch. Salakhutdinova, O.O. Varlamov

Upload the full text

PDF

Abstract: The study is dedicated to the use of mivar expert systems for identifying bacterial resistance to existing antibiotics. A modular architecture of the system was presented, which allows easy addition and updating of individual components. A knowledge base consisting of 56 rules for working with the expert system was created. It is proposed to implement the system using the KESMI software, which allowed for logical conclusions to be drawn. The system was tested on three different cases. The first case involved the presence of a mutation in the mecA gene, the second involved methylated ribosomes, and the third involved Gram-positive bacteria. Testing of the Mivar expert system showed that the bacteria’s resistance results matched the established knowledge base. The impact of using Mivar expert systems on the process of detecting antibiotic resistance has been studied. A description of the methodologies used to evaluate the system’s effectiveness was proposed. It was justified why the use of expert systems can significantly improve the diagnosis and treatment of infectious diseases.

Keywords: mivar, mivar expert system, Wi!Mi, Big Knowledge, bacterial antibiotic resistance, automated production control systems, smart production systems, automated process control systems

For citation. Salakhutdinova N.Ch., Varlamov O.O.The use of mivar expert systems for diagnosis of bacterial antibiotic resistance. News of the Kabardino-Balkarian Scientific Center of RAS. 2025. Vol. 27. No. 2. Pp. 55–73. DOI: 10.35330/1991-6639-2025-27-2-55-73

References
  1. Varlamov O.O. Evolyutsionnyye bazy dannykh i znaniy dlya adaptivnogo sinteza intellektual’nykh sistem. Mivarnoye informatsionnoye prostranstvo [Evolutionary databases and knowledge for adaptive synthesis of intelligent systems. mivar information space]. Moscow: Radio i svyaz’.2002. 286 p. EDN: RWTCOP. (In Russian)
  2. Torzhkov M.S., Koroleva Yu.P., Baldin A.V., et al. Creation of a mivar expert system for addressing ethical aspects of artificial intelligence for credit scoring. Problems of Artificial Intelligence. 2024. No. 4(35). Pp. 139–150. DOI: 10.24412/2413-7383-2024-4-139-150. EDN: BHOQXX. (In Russian)
  3. Varlamov O.O., Chuvikov D.A., Adamova L.E. et al. Logical, philosophical and ethical aspects of AI in medicine. International Journal of Machine Learning and Computing. 2019. Vol. 9. No. 6. Pp. 868–873. DOI: 10.18178/IJMLC. EDN: XJPKWA
  4. Varlamov O.O., Chuvikov D.A., Lemondzhava V.N. et al. A software package supporting decision making on the safety of thermolabile blood components. Biomedical Engineering. 2022. Vol. 55. No. 5. Pp. 355–359. DOI: 10.1007/s10527-022-10135-0. EDN: ICRHIB.
  5. Mashchenko  E.I.,  Karpov  D.K.,  Varlamov  O.O. et al.  Creation  of  a  mivar  expert  system for understanding  images  and  decision-making  in  fall  detection.  Problems  of  Artificial Intelligence. 2024. No. 4(35). Pp. 88–100. DOI: 10.24412/2413-7383-2024-4-88-100. EDN: FGLHZP. (In Russian)
  6. Podoprigorova N.S., Kozyrev S.A., Podoprigorova S.S. et al. Development of a mivar expert system for consensus algorithm selection in distributed ledgers. Problems of Artificial Intelligence. 2024. No. 4(35). Pp. 126–138. DOI: 10.24412/2413-7383-2024-4-126-138. EDN: AVXOTO. (In Russian)
  7. Shen C., Gun Sh., Varlamov O.O. et al. Dynamic trajectory planning for robots based on semantic object detection using a mivar expert system. Problems of Artificial Intelligence. 2024. No. 4(35). Pp. 164–176. DOI: 10.24412/2413-7383-2024-4-164-176. EDN: DHVOFC. (In Russian)
  8. Chuvikov D.A., Kazakova N.A., Varlamov O.O., Golovizhin A.V. 3D Modeling and 3D objects creation technology analysis for various intelligent systems. International Journal of Advanced Studies. 2014. Vol. 4. No. 4. Pp. 16–22. DOI: 10.12731/2227-930X-2014-4-3. EDN: TEBOFL
  9. Varlamov O.O. Creation of large knowledge bases and expansion of the application areas of mivar logical artificial intelligence technologies. Information and Mathematical Technologies in Science and Management. 2023. No. 4(32). Pp. 30–41. DOI: 10.25729/ESI.2023.32.4.003. EDN: THBEWN. (In Russian)
  10. Varlamov O.O. Mivar technologies as certain directions of artificial intelligence. Problems of Artificial Intelligence. 2015. No. 1(1). Pp. 23–37. EDN: WDNPGZ. (In Russian)
  11. Varlamov O.O. Automation of Human Mental Activities via Logical Artificial Intelligence as a Fundamental Mechanism for Humanity’s Development or Destruction.Problems of Artificial Intelligence. 2017. No. 3(6). Pp. 23–31. EDN: YNTRSV. (In Russian)
  12. Varlamov O.O., Lazarev V.M., Chuvikov D.A. On the prospects of creating autonomous intelligent  robots  based  on  mivar  technologies.  Radio  Industry.  2016.  No. 4.  Pp. 96–105. EDN: UQEVLG. (In Russian)
  13. Vladimirov A.N., Nosov A.V., Potapova T.S. Application of the multiprocessor computing cluster niir for parallelizing algorithms in scientific-technical and computational tasks. Trudy NII Radio[Proceedings of the NIIR Radio Institute]. 2009. No. 3. Pp. 120–123. EDN: KYNLNN. (In Russian)
  14. Semenov A.A. Research on the selection methods for advertising campaigns based on comparison of multidimensional vectors. Problems of Artificial Intelligence. 2020. No. 1(16). Pp. 89–104. EDN: UEBEPL. (In Russian)
  15. Shtrak A.A. Mivar knowledge base for automating the study of patent ductus arteriosus and hearing. MIVAR’24: Sbornik nauchnykh statey[MIVAR’24: Collection of Scientific Articles]. 2024. Pp. 548–556. EDN SXPYDW. (In Russian)
  16. Klinova V.K. Mivar knowledge base of portable spirometer for individual control of external respiratory functions. MIVAR’24: Sbornik nauchnykh statey [MIVAR’24: Collection of Scientific Articles]. 2024. Pp. 557–561. EDN: GHUNIK. (In Russian)
  17. Abrochnov E.S., Solovyeva A.M., Makeev V.A. et al. MES for selecting beneficial products. MIVAR’24: Sbornik nauchnykh statey [MIVAR’24: Collection of Scientific Articles]. 2024. Pp. 536–542. EDN: MRBKXC. (In Russian)
  18. Ovchinnikov D.A., Milevich A.A., Fonin M.A. et al. MES for improving the segmentation of trees from a point cloud. MIVAR’24: Sbornik nauchnykh statey [MIVAR’24: Collection of Scientific Articles]. 2024. Pp. 293–297. EDN: NOGUPU. (In Russian)
  19. Abdrashitova A.N., Vardumyan A.T., Golovatsky A.D. et al. Cloud system for creating mivar knowledge bases. MIVAR’24: Sbornik nauchnykh statey  [MIVAR’24: Collection of Scientific Articles]. 2024. Pp. 455–459. EDN: LKDKGC. (In Russian)
  20. Chuvikov D.A., Kim R.I., Baldin A.V. Analysis of large language models for building dialog systems. MIVAR’24: Sbornik nauchnykh statey  [MIVAR’24: Collection of Scientific Articles]. 2024. Pp. 426–431. EDN: IWVZPS. (In Russian)
  21. Varlamov O.O., Egorov S.A. Development of mivar technologies for semantic processing of text data streams. MIVAR’22: Sbornik nauchnykh statey  [MIVAR’22: Collection of Scientific Articles]. 2022. Pp. 194–212. EDN: PBFFTZ. (In Russian)
  22. Kotsenko A.A. Analysis of the application of mivar networks for automated process control systems using bipartite and tripartite graphs. MIVAR’24: Sbornik nauchnykh statey  [MIVAR’24: Collection of Scientific Articles]. 2024. Pp. 432–438. EDN: GLJGZV. (In Russian)
  23. Khabchaeva A.R., Chezegova P.A. et al. MES for categorizing critical information infrastructure in automated process control systems. MIVAR’24: Sbornik nauchnykh statey  [MIVAR’24: Collection of Scientific Articles]. 2024. Pp. 37–41. EDN: VEAGPO. (In Russian)
  24. Kovalenko A.V., Kondrakhin S.S., Smyslov D.O. MES for selecting a driving simulator for vehicle control skills development. MIVAR’24: Sbornik nauchnykh statey[MIVAR’24: Collection of Scientific Articles].2024. Pp. 67–72. EDN: ZOXOUI. (In Russian)
  25. Rudzinsky V.V. Mivar knowledge base for technical support of high-load fail-safe clusters. MIVAR’24:  Sbornik  nauchnykh  statey   [MIVAR’24:  Collection  of  Scientific  Articles].  2024. Pp. 169–173. EDN: ZJYOTC. (In Russian)
  26. Starykh F.A. MES for evaluating the content of packet data in a local network.  MIVAR’24: Sbornik nauchnykh statey  [MIVAR’24: Collection of Scientific Articles]. 2024. Pp. 102–106. EDN: FKVQMO. (In Russian)
  27. Pleshakov V.I. Development of a mivar logical inference machine for the Elbrus processor. MIVAR’24: Sbornik  nauchnykh  statey  [MIVAR’24:  Collection  of  Scientific  Articles].  2024. Pp. 450–454. EDN: EJDCAK. (In Russian)
  28. Kotsenko A.A. Development of mivar logical space models for ensuring 3d movement of autonomous robots. MIVAR’24: Sbornik nauchnykh statey  [MIVAR’24: Collection of Scientific Articles]. 2024. Pp. 361–366. EDN: HBLZQY. (In Russian)
  29. Sinitsyn L.S. Platform for SPR robot based on hybrid intelligent system. MIVAR’24: Sbornik nauchnykh statey  [MIVAR’24: Collection of Scientific Articles]. 2024. Pp. 384–388. EDN: QOSPPH. (In Russian)
  30. Varlamov O.O. On the creation of autonomous harvesters and tractors for agriculture based on mivar decision-making systems “ROBO!RAZUM”. Problemy iskusstvennogo intellekta [Problems of Artificial Intelligence]. 2019. No. 2(13). Pp. 49–62. EDN: AMUYCK. (In Russian)
  31. Fedyunov A.Yu., Nesterov Yu.G., Pravdina A.D. MES for climate control in a greenhouse.  MIVAR’24: Sbornik nauchnykh statey   [MIVAR’24: Collection of Scientific Articles]. 2024. Pp. 107–112. EDN: HSWYCJ. (In Russian)
  32. Varlamov O.O. 2024: An overview of the application areas of mivar logical artificial intelligence technologies. MIVAR’24: Sbornik nauchnykh statey [MIVAR’24: Collection of Scientific Articles]. 2024. Pp. 7–15. EDN: ATMAZU. (In Russian)
  33. Andreev A., Kotsenko A., Varlamov O. et al. Text Processing using LLM for automatic creation of agricultural crops knowledge bases. Bio Web of Conferences: International Scientific Conference on Biotechnology and Food Technology (BFT-2024). Vol. 130. Les Ulis: EDP Sciences, 2024. P. 01029. DOI: 10.1051/bioconf/202413001029. EDN: YTLLMF
  34. Aladin D.V., Aladina E.V., Chuvikov D.A. et al. Creating a “Logical intelligent plant care system” in digital agriculture based on mivar approach. IOP Conference Series: Earth and Environmental Science, 2022. Vol. 954. P. 012004. DOI: 10.1088/1755-1315/954/1/012004. EDN: HODWVY
  35. Chuvikov D.A., Aladin D.V., Adamova L.E. et al. A new method for creating mivar knowledge bases in tabular-matrix form for ground intelligent vehicle control systems. Journal of Physics: Conference Series: International Conference on Actual Issues of Mechanical Engineering. 2021. Vol. 2061. P. 012123. DOI: 10.1088/1742-6596/2061/1/012123. EDN: SLARYS
  36. Aladin D.V., Varlamov O.O., Chuvikov D.A. et al. Control of vehicles and robots: creation of planning systems in the state space (MIPRA). IOP Conference Series: Materials Science and Engineering: International Conference of Young Scientists and Students “Topical Problems of Mechanical Engineering”, 2020. Vol. 747. P. 012097. DOI: 10.1088/1757-899X/747/1/012097. EDN: OYOOVR
  37. Tabish A., Sarfaraz Ah., Muhammad A. Artificial intelligence for antimicrobial resistance prediction: challenges and opportunities towards practical implementation. Antibiotics (Basel). 2023. Vol. 12(3). P. 523. DOI: 10.3390/antibiotics12030523
  38. Tang R., Luo R., Tang S. et al. Machine learning in predicting antimicrobial resistance: a systematic review and meta-analysis. Int J Antimicrob Agents. 2022. Vol. 60. No. 5–6. P. 106684. DOI: 10.1016/j.ijantimicag.2022.106684
  39. Bilal H., Khan M.N., Khan S. et al. The role of artificial intelligence and machine learning in predicting  and  combating  antimicrobial  resistance.  Comput  Struct  Biotechnol  J.  2025. Vol. 27. Pp. 423–439. Available at: https://pmc.ncbi.nlm.nih.gov/ articles/PMC11791014/ (accessed 01.03.2025)
  40. De Vries S., Ten Doesschate T., Totté JEE et al. A semi-supervised decision support system to facilitate antibiotic stewardship for urinary tract infections. Comput Biol Med. 2022. Vol. 146. P. 105621. DOI: 10.1016/j.compbiomed.2022.105621
  41. Foroughi  M.,  Arzehgar  A.,  Seyedhasani  S.N.  et  al.  Application  of  machine  learning for  antibiotic  resistance  in  water  and  wastewater:  A  systematic  review.  Chemosphere.  2024. DOI: 10.1016/j.chemosphere.2024.142223
Information about the authors

Nuriya Ch. Salahutdinova, Student of the Institute of Artificial Intelligence of the Russian Technological University MIREA;

119454, Russia, Moscow, 78 Vernadsky avenue;

vnechkv@mail.ru, ORCID: https://orcid.org/0009-0000-3531-944X

Oleg O. Varlamov, Doctor of Technical Sciences, Professor:

Professor at the Department of Computational Complexes, Institute of Artificial Intelligence of the Russian Technological University MIREA;

119454, Russia, Moscow, 78 Vernadsky avenue;

2) Professor at the Department of Information Processing Systems and Control, Moscow State Technical University named after N. E. Bauman;

105005, Russia, Moscow, corp. 1, building 5, 2nd Baumanskaya street;

3) Chief Researcher at JSC M. A. Kartsev Research Institute of Computing Systems;

117437, Russia, Moscow, 108 Profsoyuznaya street;

ovar@narod.ru, ORCID: https://orcid.org/0000-0002-6081-3076, SPIN-code: 7983-9762

Правила для авторов

Редакционная коллегия

Рецензирование

Этика публикаций

Список авторов

Новости

Контакты