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Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysis Cover

Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysis

CONTEMPORARY MILITARY CHALLENGES
Volume 28 (2026): Issue 2 (June 2026)
By: ,  ,  ,   and    
Open Access
|Jul 2026
Figures & tables

Figures & Tables

Figure 1:

Quantum tunnelling enhances machine learning by incorporating models of human-like bistable perception of optical illusions and cognitive biases into the neural network. The principle of energy quantisation aligns with human mental states (depicted by lines on the head silhouettes), where transitions between energy levels enable nuanced militarycivilian vehicle differentiation.

Figure 2:

Example of a CIFAR-military vehicle subdataset

Figure 3:

Accuracy over epochs: (a) QT-RNN sentiment analysis, (b) QT-BNN military-civilian classification.Accuracy of the respective classical models shown for reference.

Figure 4:

Representative examples of civilian vehicles misclassified as military by the QT-BNN and classical models.

Positive and Negative Words: Military Context

Positive WordsNegative Words
AchieveAdvanceAbortAmbiguous
AuthorizeClearBreakdownCancel
CommandConfirmCompromisedConflicted
DecisiveDefinitiveDegradeDefeat
DeployDesignatedDeniedDisrupt
EffectiveEngageDoubtfulFailure
EstablishedMission-readyIneffectiveMisfire
Objective-securedOn-targetObstructedOff-course
SuccessValidatedUnconfirmedVoid
DOI: https://doi.org/10.2478/cmc-2026-0013 | Journal eISSN: 2463-9575 | Journal ISSN: 2232-2825
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Language: English, Slovenian
Page range: 49 - 62
Published on: Jul 2, 2026
Published by: General Staff of the Slovenian Armed Forces
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Cognitive modelling and systems,
deep learning architecture,
human-robot/agent interaction,
machine learning
Related subjects:
Social sciences,
Sociology,
Sociology of science, technology, and environment,
Political science,
Political thought,
Military policy,
International relations

© 2026 Milan Maksimovic, Anna Bohdanets, Immaculate Motsi-Omoijiade, Guido Governatori, Ivan S. Maksymov, published by General Staff of the Slovenian Armed Forces
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 28 (2026): Issue 2 (June 2026)
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