Symmetry/Asymmetry and Artificial Intelligence: Models, Methods, and Applications

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 444

Special Issue Editors


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Guest Editor
Intelligent Processing and Security of Systems, Faculty of Science, Mohammed V University in Rabat, Rabat 1014, RP, Morocco
Interests: computer science; software engineering; graph theory; data science; artificial intelligence

E-Mail Website
Guest Editor
Intelligent Processing and Security of Systems, Faculty of Science, Mohammed V University in Rabat, Rabat 1014, RP, Morocco
Interests: software engineering; artificial intelligence; analysis and conceptual modeling; software requirements; multi-agent systems; generative AI; digital health

E-Mail Website
Guest Editor
Precision Medicine and One Health Laboratory; Faculty of Medicine, Mohammed VI University of Sciences and Health, Casablanca 82403, Morocco
Interests: digital health; personalized medicine; cancer; microbiome; tissue engineering and biomaterials; cell therapy and regenerative medicine

Special Issue Information

Dear Colleagues,

This Special Issue, “Symmetry/Asymmetry and Artificial Intelligence: Models, Methods, and Applications”, invites high-quality contributions exploring the interplay between symmetry principles and artificial intelligence. Symmetry and asymmetry are fundamental concepts that underpin many aspects of data representation, model design, and computational efficiency. In artificial intelligence, they emerge naturally in machine learning, deep learning, graph theory, and intelligent systems, where exploiting symmetry can reduce complexity, enhance generalization, and improve interpretability, while asymmetry often drives adaptability, robustness, and novel learning strategies.

The aim of this Special Issue is to bring together theoretical developments, methodological innovations, and real-world applications that reveal how symmetry and asymmetry shape intelligent models and algorithms. We encourage submissions addressing new frameworks, optimization strategies, and domain-specific applications where symmetry or asymmetry plays a critical role. Both fundamental research and applied studies across diverse areas of AI are welcome.

Prof. Dr. Soumia Ziti
Prof. Dr. Nassim Kharmoum
Dr. Al Idrissi Najib
Guest Editors

Manuscript Submission Information

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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 and asymmetry in AI
  • machine learning and deep learning
  • graph theory and graph neural networks
  • intelligent systems and optimization
  • data representation and structural patterns
  • algorithmic efficiency and robustness
  • application symmetry in computation
  • adaptive and asymmetric models

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Published Papers (1 paper)

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Research

18 pages, 4729 KB  
Article
Improved YOLOv5s-Based Crack Detection Method for Sealant-Spraying Devices
by Weiyi Kong, Hua Ding, Qingzhang Cheng, Ning Li, Xiaochun Sun and Xiaoxin Dong
Symmetry 2025, 17(12), 2089; https://doi.org/10.3390/sym17122089 - 5 Dec 2025
Viewed by 268
Abstract
The manual spraying of sealant on train side doors is associated with high costs and significant safety risks. To address this challenge, this study proposes an automated crack localization method for sealant-spraying devices by enhancing the YOLOv5s network, with a specific focus on [...] Read more.
The manual spraying of sealant on train side doors is associated with high costs and significant safety risks. To address this challenge, this study proposes an automated crack localization method for sealant-spraying devices by enhancing the YOLOv5s network, with a specific focus on leveraging principles of symmetry. First, an automated sealant-spraying device is designed for operation and data acquisition. Geometric symmetry is then exploited through Zhang’s camera calibration method to accurately establish the two-dimensional mapping between spatial coordinates and the image plane, a process fundamental to spatial reasoning. The core of our approach lies in introducing structural and computational symmetry into the deep learning model. The original YOLOv5s network is improved by integrating the Selective Context Convolutional module and the Skew Intersection over Union (IoU) Loss function, which streamline computation and boost detection accuracy. Furthermore, we replace the standard C3 module with an improved version that incorporates a Reparameterization Visual Transfer block, enhancing feature representation through structural re-parameterization symmetry between training and inference phases. Validation using data from a coal handling facility demonstrates that the improved YOLOv5s model achieves superior performance in precision, mAP@0.5, and recall compared to the original. The results underscore the critical role of geometric and architectural symmetry in developing robust and efficient vision systems for industrial automation. Full article
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