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Symmetry
Special Issues
Application of Symmetry/Asymmetry and Machine Learning
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Application of Symmetry/Asymmetry and Machine Learning

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 2224

Special Issue Editors

Prof. Dr. Mohamed Hamlich

E-Mail Website
Guest Editor
Complex Cyber Physical System Laboratory, University of HASSAN II, Casablanca, Morocco
Interests: artificial general intelligence; machine learning; IoT; robotics; data science
Prof. Dr. Sebastián Ventura

E-Mail Website
Guest Editor
Department of Computer Science and Numerical Analysis, University of Cordoba, Cordoba, Spain
Interests: artificial intelligence; data science; computational intelligence; intelligent applications of data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue “Application of Symmetry/Asymmetry and Machine Learning” will investigate the critical roles that symmetry and asymmetry play in advancing machine learning. Symmetry and asymmetry influence key aspects such as data representation, feature extraction, classification, and anomaly detection. This Issue invites research on the innovative uses of these properties to improve the efficiency, security, and scalability of learning systems. By leveraging symmetry, models can achieve more robust and generalizable representations, while asymmetry can reveal unique patterns for specialized tasks. Contributions may explore both theoretical frameworks and practical algorithms that make effective use of these principles. The Special Issue also examines how symmetry-oriented approaches can support automated machine learning, enabling systems to discover and exploit invariances without human intervention. Ultimately, this collection aims to deepen our understanding of symmetry and asymmetry in machine learning and to inspire new directions for research and real-world applications.

We are pleased to invite you to submit original research papers, which have not been submitted or published in or are not under consideration for any other conferences or journals. Topics of interest include, but are not limited to, the following:

  • Utilization of symmetry and asymmetry in machine learning algorithms for blockchain technology, focusing on improving the efficiency, security, and scalability of blockchain systems.
  • Novel machine learning models motivated by symmetry/asymmetry properties, including the following:
    • Supervised and unsupervised learning;
    • Computer vision and natural language processing;
    • Deep learning and neural networks;
    • Pattern recognition;
    • Statistical modeling and inference.
  • Asymmetry in machine learning applied to fields such as engineering, healthcare, agriculture, astronomy, sports, cybersecurity, and education, with emphasis on novel prediction and classification models.
  • Symmetry and asymmetry in image processing and computer vision, especially using embedded systems, including the following:
    • Symmetry detection algorithms for improved recognition and classification;
    • Asymmetry analysis for object detection and scene understanding;
    • Embedded system architectures optimizing symmetrical and asymmetrical image data processing;
    • Practical applications in robotics, surveillance, medical imaging, and autonomous vehicles;
    • Machine learning techniques leveraging symmetry/asymmetry for enhanced image analysis and feature extraction.
  • Artificial General Intelligence and Agentic AI:
    • Large Language Models (LLMs).
    • Agentic AI applications;
    • Multi Agent Systems;
    • AI powered XAR Applications;
    • Blockchain and AI;
    • Risks and Ethical challenges of AI;
    • Governance challenges of AI.

We look forward to receiving your contributions.

Prof. Dr. Mohamed Hamlich
Prof. Dr. Sebastián Ventura
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • symmetry
  • asymmetry
  • machine learning
  • blockchain technology
  • image processing
  • computer vision
  • embedded systems
  • pattern recognition
  • deep learning
  • feature extraction

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

30 pages, 7225 KB  
Article
Causal Learning for Continuous Variables with an Improved Bayesian Network Constructed by Symmetric Kernel Function Acceleration
by Chenghao Wei, Pukai Wang, Chen Li and Zhiwei Ye
Symmetry 2026, 18(5), 731; https://doi.org/10.3390/sym18050731 - 24 Apr 2026
Viewed by 231
Abstract
Bayesian network-based causal structure learning provides an effective framework for uncovering causal relationships among continuous variables. However, many existing methods for continuous data still rely on strong parametric distribution assumptions, which may introduce information loss and reduce Bayesian network modeling accuracy. Kernel density [...] Read more.
Bayesian network-based causal structure learning provides an effective framework for uncovering causal relationships among continuous variables. However, many existing methods for continuous data still rely on strong parametric distribution assumptions, which may introduce information loss and reduce Bayesian network modeling accuracy. Kernel density estimation (KDE), a non-parametric statistical method that is more flexible in density estimation form, offers a versatile framework for conducting conditional independence (CI) tests. This approach enables the estimation of mutual information and conditional mutual information, thereby facilitating the identification of underlying structural relationships. Nevertheless, the high computational cost of KDE-based CI testing restricts its practical application in continuous-variable causal learning. To address this issue, this study introduces a radial symmetric kernel-based acceleration scheme within a Fast Fourier Transform (FFT) framework to improve the efficiency of density estimation. On this basis, an enhanced Bayesian network structure learning method is developed for continuous variables, enabling more efficient estimation of mutual information and conditional mutual information while improving the computational efficiency and empirical stability of variable dependency discovery. With proper bandwidth and grid resolution, the proposed MMHC-FFTKDE framework achieves a reduction in computational runtime and improves efficiency compared to MMHC-KDE in the ablation setting, while maintaining competitive F1-scores and SHD for causal structure discovery. Full article
(This article belongs to the Special Issue Application of Symmetry/Asymmetry and Machine Learning)
24 pages, 1727 KB  
Article
Symmetry-Guided Deep Generative Model for Multi-Step Evolution of Complex Dynamical Systems
by Ying Xu, Chengbo Zhu, Nannan Su, Yingying Wang and Ziqi Fan
Symmetry 2026, 18(3), 450; https://doi.org/10.3390/sym18030450 - 6 Mar 2026
Viewed by 333
Abstract
Complex dynamical systems are characterized by inherent nonlinearity, high dimensionality, spatiotemporal uncertainty, and implicit symmetry, posing fundamental challenges for their mathematical modeling and multi-step evolution prediction. For example, wind power exhibits strong randomness, intermittency, and latent temporal symmetry. To address this, this paper [...] Read more.
Complex dynamical systems are characterized by inherent nonlinearity, high dimensionality, spatiotemporal uncertainty, and implicit symmetry, posing fundamental challenges for their mathematical modeling and multi-step evolution prediction. For example, wind power exhibits strong randomness, intermittency, and latent temporal symmetry. To address this, this paper proposes a symmetry-guided deep generative model, the bi-directional recurrent generative adversarial network (BDR-GAN), for the multi-step rolling prediction of such systems. The BDR-GAN formalizes multi-step evolution as a conditional probability distribution learning problem. It systematically integrates three forms of symmetry to enhance modeling validity: bi-directional temporal symmetry captured by a BiLSTM-based generator, structural symmetry within the adversarial learning framework between the generator and a 1D-CNN discriminator, and rolling symmetry enabled by a recursive prediction strategy that supports cyclic state updates. Theoretical analysis demonstrates that this symmetry-embedded adversarial mechanism enables BDR-GAN to effectively approximate the underlying dynamic operators and the conditional distribution of future states, improving the learned model’s generalization. Experimental validation on wind power datasets confirms the framework’s superiority. Compared to benchmark models, BDR-GAN achieves superior prediction accuracy (e.g., RMSE 0.236, MAPE 5.12%), provides reliable uncertainty quantification (PICP 95.5%), and exhibits enhanced robustness against noise and variability. This work provides a generalizable, symmetry-guided modeling framework for the multi-step evolution of complex dynamical systems, offering theoretical and technical support for high-precision prediction in critical applications such as wind power integration and smart grid operation. Full article
(This article belongs to the Special Issue Application of Symmetry/Asymmetry and Machine Learning)
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19 pages, 656 KB  
Article
Bias-Alleviated Zero-Shot Sports Action Recognition Enabled by Multi-Scale Semantic Alignment
by Qiang Zheng, Wen Qin, Fanyi Meng and Hongyang Liu
Symmetry 2025, 17(11), 1959; https://doi.org/10.3390/sym17111959 - 14 Nov 2025
Viewed by 836
Abstract
Zero-shot action recognition remains challenging due to the visual–semantic gap and the persistent bias toward seen classes, particularly under the generalized setting where both seen and unseen categories appear during inference. To address these issues, we propose Multi-Scale Semantic Alignment framework for Zero-Shot [...] Read more.
Zero-shot action recognition remains challenging due to the visual–semantic gap and the persistent bias toward seen classes, particularly under the generalized setting where both seen and unseen categories appear during inference. To address these issues, we propose Multi-Scale Semantic Alignment framework for Zero-Shot Sports Action Recognition (MSA-ZSAR), a framework that integrates a multi-scale spatiotemporal feature extractor to capture both coarse and fine-grained motion dynamics, a dual-branch semantic alignment strategy that adapts to different levels of semantic availability, and a bias-suppression mechanism to improve the balance between seen and unseen recognition. This design ensures that the model can effectively align visual features with semantic representations while alleviating overfitting to source classes. Extensive experiments demonstrate the effectiveness of the proposed framework. MSA-ZSAR achieves 52.8% unseen accuracy, 69.7% seen accuracy, and 61.3% harmonic mean, consistently surpassing prior approaches. These results confirm that the proposed framework delivers balanced and superior performance in realistic generalized zero-shot scenarios. Full article
(This article belongs to the Special Issue Application of Symmetry/Asymmetry and Machine Learning)
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