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Article

Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties

by
Milan Gnjatović
1,*,
Nemanja Maček
2,
Muzafer Saračević
3,
Saša Adamović
4,
Dušan Joksimović
1 and
Darjan Karabašević
5
1
Department of Information Technology, University of Criminal Investigation and Police Studies, Cara Dušana 196, 11080 Beograd, Serbia
2
School of Electrical and Computer Engineering, Academy of Technical and Art Applied Studies, Vojvode Stepe 283, 11000 Beograd, Serbia
3
Department of Computer Sciences, University of Novi Pazar, Dimitrija Tucovića bb., 36300 Novi Pazar, Serbia
4
Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11000 Beograd, Serbia
5
Faculty of Applied Management, Economics and Finance, University Business Academy in Novi Sad, Jevrejska 24, 11000 Belgrade, Serbia
*
Author to whom correspondence should be addressed.
Submission received: 24 October 2022 / Revised: 7 December 2022 / Accepted: 16 December 2022 / Published: 21 December 2022
(This article belongs to the Section Mathematical Analysis)

Abstract

This paper introduces a heuristic for multiple sequence alignment aimed at improving real-time object recognition in short video streams with uncertainties. It builds upon the idea of the progressive alignment but is cognitively economical to the extent that the underlying edit distance approach is adapted to account for human working memory limitations. Thus, the proposed heuristic procedure has a reduced computational complexity compared to optimal multiple sequence alignment. On the other hand, its relevance was experimentally confirmed. An extrinsic evaluation conducted in real-life settings demonstrated a significant improvement in number recognition accuracy in short video streams under uncertainties caused by noise and incompleteness. The second line of evaluation demonstrated that the proposed heuristic outperforms humans in the post-processing of recognition hypotheses. This indicates that it may be combined with state-of-the-art machine learning approaches, which are typically not tailored to the task of object sequence recognition from a limited number of frames of incomplete data recorded in a dynamic scene situation.
Keywords: multiple sequence alignment; object recognition; uncertainty in vision task; cognitive economy multiple sequence alignment; object recognition; uncertainty in vision task; cognitive economy

Share and Cite

MDPI and ACS Style

Gnjatović, M.; Maček, N.; Saračević, M.; Adamović, S.; Joksimović, D.; Karabašević, D. Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties. Axioms 2023, 12, 3. https://doi.org/10.3390/axioms12010003

AMA Style

Gnjatović M, Maček N, Saračević M, Adamović S, Joksimović D, Karabašević D. Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties. Axioms. 2023; 12(1):3. https://doi.org/10.3390/axioms12010003

Chicago/Turabian Style

Gnjatović, Milan, Nemanja Maček, Muzafer Saračević, Saša Adamović, Dušan Joksimović, and Darjan Karabašević. 2023. "Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties" Axioms 12, no. 1: 3. https://doi.org/10.3390/axioms12010003

APA Style

Gnjatović, M., Maček, N., Saračević, M., Adamović, S., Joksimović, D., & Karabašević, D. (2023). Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties. Axioms, 12(1), 3. https://doi.org/10.3390/axioms12010003

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