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Symmetry
Special Issues
Applications Based on Symmetry/Asymmetry in Machine Learning
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Applications Based on Symmetry/Asymmetry in Machine Learning

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1395

Special Issue Editors

Dr. Masoud Barati

E-Mail Website
Guest Editor
School of Information Technology, Carleton University, Ottawa, ON K1S 5B6, Canada
Interests: cloud; fog and edge computing; internet of things; network security; data privacy; ad-hoc and wireless sensor networks; data modelling
Dr. Ahmad Zareie

E-Mail Website
Guest Editor
Department of Computer Science, University of Manchester, Manchester, UK
Interests: social networks analysis; complex networks; distributed systems
Dr. Vahid Seydi

E-Mail Website
Guest Editor
Centre for Applied Marine Sciences, School of Ocean Sciences, Bangor University, Menai Bridge, UK
Interests: explainable machine learning; deep learning; probabilistic graphical models; reinforcement learning; soft computing; fuzzy

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the applications of symmetry and asymmetry in machine learning with a focus on supporting blockchain technology. Symmetry and asymmetry play crucial roles in various aspects of machine learning, including data representation, feature extraction, classification, and anomaly detection. This Special Issue invites authors to contribute their research on the innovative utilization of symmetry and asymmetry in machine learning algorithms and techniques for blockchain applications. The goal is to deepen our understanding of how symmetry and asymmetry can enhance the efficiency, security, and scalability of blockchain systems while leveraging the potential of machine learning.

Dr. Masoud Barati
Dr. Ahmad Zareie
Dr. Vahid Seydi
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

  • machine learning
  • federated learning
  • blockchain technology
  • data representation and modelling
  • data classification
  • anomaly detection
  • security

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

Published Papers (1 paper)

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Research

15 pages, 3352 KiB  
Article
Adaptive Difference Least Squares Support Vector Regression for Urban Road Collapse Timing Prediction
by Yafang Han, Limin Quan, Yanchun Liu, Yong Zhang, Minghou Li and Jian Shan
Symmetry 2024, 16(8), 977; https://doi.org/10.3390/sym16080977 - 1 Aug 2024
Viewed by 753
Abstract
The accurate prediction of urban road collapses is of paramount importance for public safety and infrastructure management. However, the complex and variable nature of road subsidence mechanisms, coupled with the inherent noise and non-stationarity in the data, poses significant challenges to the development [...] Read more.
The accurate prediction of urban road collapses is of paramount importance for public safety and infrastructure management. However, the complex and variable nature of road subsidence mechanisms, coupled with the inherent noise and non-stationarity in the data, poses significant challenges to the development of precise and real-time prediction models. To address these challenges, this paper develops an Adaptive Difference Least Squares Support Vector Regression (AD-LSSVR) model. The AD-LSSVR model employs a difference transformation to process the input and output data, effectively reducing noise and enhancing model stability. This transformation extracts trends and features from the data, leveraging the symmetrical characteristics inherent within it. Additionally, the model parameters were optimized using grid search and cross-validation techniques, which systematically explore the parameter space and evaluate model performance of multiple subsets of data, ensuring both precision and generalizability of the selected parameters. Moreover, a sliding window method was employed to address data sparsity and anomalies, ensuring the robustness and adaptability of the model. The experimental results demonstrate the superior adaptability and precision of the AD-LSSVR model in predicting road collapse timing, highlighting its effectiveness in handling the complex nonlinear data. Full article
(This article belongs to the Special Issue Applications Based on Symmetry/Asymmetry in Machine Learning)
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