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Biologically Inspired Cognitive Architectures Meeting

BICA 2019: Biologically Inspired Cognitive Architectures 2019 pp 277–282Cite as

Factographic Information Retrieval for Biological Objects

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 948))

Abstract

This paper describes the results of work to develop an automated factographic information retrieval system for biological forensic objects. The effective factographic information retrieval problem has been investigated in the paper. This factographic information retrieval includes a pattern recognition algorithm, and they are implemented for retrieval only one image among the variety of similar images of biological forensic objects. The automated factographic information retrieval system includes special retrieval block and human operator. Analytical formula was obtained to evaluate the effectiveness of factographic information retrieval using indicator. This formula is presented by effectiveness indicator: average length of the recommendatory list provided by the retrieval block enabling the human operator to take the final decision. The paper describes the structure of the algorithm for factographic information retrieval. Properties of the important indicator of effectiveness – the average length of the recommendatory list for the human operator were explored.

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References

  1. Ko J, Si L, Nyberg E, Mitamura T (2010) Probabilistic models for answer-ranking in multilingual question-answering. ACM Trans Inf Syst (TOIS) 28(3):37. Article 16

    Article  Google Scholar 

  2. Kulik S (2016) Factographic information retrieval for communication in multicultural society. In: Procedia - social and behavioral sciences (International conference on communication in multicultural society, CMSC 2015, 6–8 December 2015, Moscow, Russian Federation), vol 236, pp 29–33

    Article  Google Scholar 

  3. Gridnev AA, Voznenko TI, Chepin EV (2018) The decision-making system for a multi-channel robotic device control. Procedia Comput Sci 123:149–154

    Article  Google Scholar 

  4. Samsonovich AV (2013) Emotional biologically inspired cognitive architecture. Biol Inspired Cogn Arch 6:109–125. https://doi.org/10.1016/j.bica.2013.07.009

    Article  Google Scholar 

  5. Chistyakov IS, Chepin EV (2019) Gesture recognition system based on convolutional neural networks. In: 2019 IOP conference series: materials science and engineering, vol 498, p 012023

    Article  Google Scholar 

  6. Samsonovich AV (2018) On semantic map as a key component in socially-emotional BICA. Biol Inspired Cogn. Arch 23:1–6. https://doi.org/10.1016/j.bica.2017.12.002

    Article  Google Scholar 

  7. Artamonov AA, Ionkina KV, Kirichenko AV, Lopatina EO, Tretyakov ES, Cherkasskiy AI (2018) Agent-based search in social networks. Int J Civ Eng Technol 9(13):28–35

    Google Scholar 

  8. Miloslavskaya N, Tolstoy A (2016) State-level views on professional competencies in the field of IoT and cloud information security. In: 2016 IEEE 4th international conference on future internet of things and cloud workshops (FiCloudW), Vienna, pp 83–90

    Google Scholar 

  9. Kulik S, Nikonets D (2016) Forensic handwriting examination and human factors: improving the practice through automation and expert training. In: Proceedings of the third international conference on digital information processing, data mining, and wireless communications, DIPDMWC 2016, 06–08 July, Moscow, Russia, pp 221–226

    Google Scholar 

  10. Miloslavskaya N, Tolstoy A (2016) Big data, fast data and data lake concepts. Procedia Comput Sci 88:300–305

    Article  Google Scholar 

  11. Kulik, S.D.: Factographic information retrieval for semiconductor physics, micro - and nanosystems. AMNST 2017. In: IOP conference series: materials science and engineering, vol 498, 012026 (2019)

    Article  Google Scholar 

  12. Shishkin S, Nuzhdin Y, Svirin E, Trofimov A, Fedorova A, Kozyrskiy B, Velichkovsky B (2016) EEG negativity in fixations used for gaze-based control: toward converting intentions into actions with an eye-brain-computer interface. Front Neurosci 10:1–20

    Article  Google Scholar 

  13. Kireev V, Silenko A, Guseva A (2017) Cognitive competence of graduates, oriented to work in the knowledge management system in the state corporation “ROSATOM”. J Phys: Conf Ser 781(1):012060

    Google Scholar 

  14. Nuzhdin YO, Shishkin SL, Fedorova AA, Trofimov AG, Svirin EP, Kozyrskiy BL, Medyntsev AA, Dubynin IA, Velichkovsky BM (2017) The expectation based eye-brain-computer interface: an attempt of online test. In: Proceedings of the 2017 ACM workshop on an application-oriented approach to BCI out of the laboratory. ACM, pp 39–42

    Google Scholar 

  15. Yasnitsky LN, Vauleva SV, Safonova DN, Cherepanov FM (2015) The use of artificial intelligence methods in the analysis of serial killers’ personal characteristics. Criminol J Baikal Natl Univ Econ Law 9(3):423–430

    Google Scholar 

  16. Kireev VS, Guseva AI, Bochkaryov PV, Kuznetsov IA, Filippov SA (2019) Association rules mining for predictive analytics in IoT cloud system. In: Samsonovich A (ed) Biologically inspired cognitive architectures 2018, vol 848. Advances in intelligent systems and computing. Springer, Cham, pp 107–112

    Chapter  Google Scholar 

  17. Kulik S (2016) Factographic information retrieval for competences forming. In: Proceedings of the third international conference on digital information processing, data mining, and wireless communications, DIPDMWC 2016, 06–08 July, Moscow, Russian Federation, pp 245–250

    Google Scholar 

  18. Yasnitsky LN, Dumler AA, Bogdanov KV, Poleschuk AN, Cherepanov FM, Makurina TV, Chugaynov SV (2013) Diagnosis and prognosis of cardiovascular diseases on the basis of neural networks. Biomed Eng 47(3):160–163

    Article  Google Scholar 

  19. Artamonov A, Onykiy B, Ananieva A, Ionkina K, Kshnyakov D, Danilova V, Korotkov M (2016) Regular agent technologies for the formation of dynamic profile. Procedia Comput Sci 88:482–486

    Article  Google Scholar 

  20. Verbitsky NS, Chepin EV, Gridnev AA (2018) Experimental studies of a convolutional neural network for application in the navigation system of a mobile robot. Procedia Comput Sci 145:611–616

    Article  Google Scholar 

  21. Voznenko TI, Chepin EV, Urvanov GA (2018) The control system based on extended BCI for a robotic wheelchair. Procedia Comput Sci 123:522–527

    Article  Google Scholar 

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Acknowledgements

This work was supported by Competitiveness Growth Program of the Federal Autonomous Educational Institution of Higher Education National Research Nuclear University MEPhI (Moscow Engineering Physics Institute). The topics presented in this paper have been discussed with a number of people in various conferences. I thank all them for the received comments.

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Authors and Affiliations

  1. National Research Nuclear University MEPHI, Kashirskoe shosse 31, Moscow, 115409, Russia

    Sergey D. Kulik

  2. Moscow State University of Psychology and Education (MSUPE), Sretenka st. 29, Moscow, 127051, Russia

    Sergey D. Kulik

Authors
  1. Sergey D. Kulik
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Correspondence to Sergey D. Kulik .

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Editors and Affiliations

  1. Moscow Engineering Physics Institute (MEPhI), Department of Cybernetics, National Research Nuclear University (NRNU), Moscow, Russia

    Alexei V. Samsonovich

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Kulik, S.D. (2020). Factographic Information Retrieval for Biological Objects. In: Samsonovich, A. (eds) Biologically Inspired Cognitive Architectures 2019. BICA 2019. Advances in Intelligent Systems and Computing, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-25719-4_35

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