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Symmetry
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Symmetry in Stochastic Models for Machine Learning Applications: Theoretical Insights...
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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 1129

Special Issue Editors

Prof. Dr. Zeev Volkovich

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
Dr. Renata Avros

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
Dr. Dvora Toledano-Kitai

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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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

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Research

26 pages, 1271 KB  
Article
Predicting the Forest Fire Duration Enriched with Meteorological Data Using Feature Construction Techniques
by Constantina Kopitsa, Ioannis G. Tsoulos, Andreas Miltiadous and Vasileios Charilogis
Symmetry 2025, 17(11), 1785; https://doi.org/10.3390/sym17111785 - 22 Oct 2025
Abstract
The spread of contemporary artificial intelligence technologies, particularly machine learning, has significantly enhanced the capacity to predict asymmetrical natural disasters. Wildfires constitute a prominent example, as machine learning can be employed to forecast not only their spatial extent but also their environmental and [...] Read more.
The spread of contemporary artificial intelligence technologies, particularly machine learning, has significantly enhanced the capacity to predict asymmetrical natural disasters. Wildfires constitute a prominent example, as machine learning can be employed to forecast not only their spatial extent but also their environmental and socio-economic impacts, propagation dynamics, symmetrical or asymmetrical patterns, and even their duration. Such predictive capabilities are of critical importance for effective wildfire management, as they inform the strategic allocation of material resources, and the optimal deployment of human personnel in the field. Beyond that, examination of symmetrical or asymmetrical patterns in fires helps us to understand the causes and dynamics of their spread. The necessity of leveraging machine learning tools has become imperative in our era, as climate change has disrupted traditional wildfire management models due to prolonged droughts, rising temperatures, asymmetrical patterns, and the increasing frequency of extreme weather events. For this reason, our research seeks to fully exploit the potential of Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (MRMR), and Grammatical Evolution, both for constructing Artificial Features and for generating Neural Network Architectures. For this purpose, we utilized the highly detailed and publicly available symmetrical datasets provided by the Hellenic Fire Service for the years 2014–2021, which we further enriched with meteorological data, corresponding to the prevailing conditions at both the onset and the suppression of each wildfire event. The research concluded that the Feature Construction technique, using Grammatical Evolution, combines both symmetrical and asymmetrical conditions, and that weather phenomena may provide and outperform other methods in terms of stability and accuracy. Therefore, the asymmetric phenomenon in our research is defined as the unpredictable outcome of climate change (meteorological data) which prolongs the duration of forest fires over time. Specifically, in the model accuracy of wildfire duration using Feature Construction, the mean error was 8.25%, indicating an overall accuracy of 91.75%. Full article
(This article belongs to the Special Issue Symmetry in Stochastic Models for Machine Learning Applications: Theoretical Insights and Applications)
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21 pages, 5735 KB  
Article
Numerical Investigation Using Machine Learning Process Combination of Bio PCM and Solar Salt for Thermal Energy Storage Applications
by Ravi Kumar Kottala, Sankaraiah Mogaligunta, Makham Satyanarayana Gupta, Seepana Praveenkumar, Ramakrishna Raghutu, Kiran Kumar Patro, Achanta Sampath Dakshina Murthy and Dharmaiah Gurram
Symmetry 2025, 17(7), 998; https://doi.org/10.3390/sym17070998 - 25 Jun 2025
Cited by 1 | Viewed by 747
Abstract
TGA kinetic analysis can assess the thermal stability and degradation properties of PCMs by calculating activation energies and onset degradation temperatures, which are critical elements when developing optimal PCM composition and assessing long-term performance in thermal energy storage applications. In this study, we [...] Read more.
TGA kinetic analysis can assess the thermal stability and degradation properties of PCMs by calculating activation energies and onset degradation temperatures, which are critical elements when developing optimal PCM composition and assessing long-term performance in thermal energy storage applications. In this study, we utilize a thermogravimetric analyzer to examine the thermal stability of both solar salt phase change material (i.e., commonly used in medium-temperature applications) (NaNO3 + KNO3) and a composite eutectic PCM mixture (i.e., PCM with 20% biochar). The activation energies of both the pure solar salt and composite solar salt PCM sample were evaluated using a variety of different kinetic models such as Kissinger–Akahira–Sunose (KAS), Flynn–Wall–Ozawa (FWO), and Starink. For pure PCM, the mean activation energies calculated using the KAS, FWO, and Starink methods are 581.73 kJ/mol, 570.47 kJ/mol, and 581.31 kJ/mol, respectively. Conversely, for the composite solar salt PCM sample, the calculated experimental average activation energies are 51.67 kJ/mol, 62.124 kJ/mol, and 51.383 kJ/mol. Additionally, various machine learning models, such as linear regression, decision tree regression, gradient boosting regression, random forest regression, polynomial regression, Gaussian process regression, and KNN regression models, are developed to predict the degradation behaviour of pure and composite solar salts under different loading rates. In the machine learning models, the mass loss of the samples is the output variable and the input features are PCM type, heating rate, and temperature. The machine learning models had a great prediction performance based on experimental TGA data, with KNN regression outperforming the other models by achieving the lowest RMSE of 0.0318 and the highest R2 score of 0.977. Full article
(This article belongs to the Special Issue Symmetry in Stochastic Models for Machine Learning Applications: Theoretical Insights and Applications)
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