Symmetry in Stochastic Models for Machine Learning Applications: Theoretical Insights and Applications

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 137

Special Issue Editors


E-Mail Website
Guest Editor
Software Engineering Department, ORT Braude College, Karmiel 21982, Israel
Interests: data mining; text mining; computational biology; patter recognition; probability theory

E-Mail Website
Guest Editor
Software Engineering Department, Braude College of Engineering, Braude, Karmiel 2161002, Israel
Interests: data mining; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Software Engineering Department, Braude College of Engineering, Braude, Karmiel 2161002, Israel
Interests: data mining; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

"Symmetry in Stochastic Models for Machine Learning Applications: Theoretical Insights and Applications" focuses on integrating symmetry principles into stochastic modeling to address critical challenges, particularly within mathematical statistics, data mining, machine learning, and deep learning. This Special Issue encompasses a broad spectrum of research, including investigations into the role of symmetry in probability theory, stochastic processes, and statistical distributions. It also emphasizes the application of symmetry in data analysis, specifically focusing on pattern recognition, clustering, and optimization techniques. Furthermore, the Special Issue explores interdisciplinary applications across fields such as artificial intelligence, biomedical data analysis, and dynamic systems. A core theme is developing and evaluating novel methods for incorporating symmetry into stochastic models and assessing their impact on statistical modeling. By bridging theoretical and applied perspectives, this Special Issue aims to provide valuable insights into how symmetry can enhance the robustness, interpretability, and efficiency of contemporary stochastic and statistical methodologies, including those crucial for deep learning.

We encourage submissions that

  • Present novel theoretical contributions to advance modern data analysis.
  • Propose innovative algorithms and techniques specifically designed for graph analysis.
  • Demonstrate the application of stochastic models in solving real-world problems.
  • Evaluate the performance of stochastic model methods when applied to large-scale ("big data") problems.

This Special Issue aims to propel state-of-the-art analysis by fostering interdisciplinary collaboration among researchers from diverse fields, thereby stimulating scientific and applied advancements.

Prof. Dr. Zeev Volkovich
Dr. Renata Avros
Dr. Dvora Toledano-Kitai
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 100 words) can be sent to the Editorial Office for announcement on this website.

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
  • probability theory
  • stochastic processes
  • statistical distributions
  • pattern recognition
  • data analysis
  • clustering
  • optimization techniques
  • artificial intelligence
  • machine learning
  • deep learning
  • dynamic systems
  • symmetry-aware algorithms
  • symmetry-based optimization
  • symmetry-preserving transformations
  • symmetry-induced regularization
  • statistical modeling

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Published Papers

This special issue is now open for submission.
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