Special Issue "Emerging Applications of Machine Learning in Smart Systems Symmetry"

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

Deadline for manuscript submissions: 31 May 2023 | Viewed by 1539

Special Issue Editors

School of Computer Science, The University of Sydney, Darlington, NSW 2008, Australia
Interests: network and communications; cyber security; IoT; machine learning; technology for developing country
Special Issues, Collections and Topics in MDPI journals
School of Computer, Data and Mathematical Sciences, Western Sydney University, Kingswood, NSW 2747, Australia
Interests: networking; internet of things; cybersecurity; machine learning; smart aging
Special Issues, Collections and Topics in MDPI journals
School of Engineering and Technology, Central Queensland University, Sydney, NSW 2000, Australia
Interests: information systems; artificial intelligence and image processing; computer software; communications technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the wave of the third Artificial Intelligence (AI) revolution, smart systems have become a norm in our day to day life. Machine learning, a subset of AI, is one of the most influential enablers behind these smart systems. On the other hand, the symmetry concept in machine learning models is gaining momentum. Machine learning is bringing both positive and negative emotions and concerns to the research community. Consequently, human-centred Artificial Intelligence is in the spotlight. The ethical concern related by machine learning is of paramount importance in smart healthcare, autonomous, and other intelligent systems. Machine learning has also become a critical technology to enable contemporary cybersecurity systems.

This Special Issue of Symmetry features emerging smart systems, along with state-of-the-art machine learning and applied symmetrical methods in developing models and their applications. We are seeking contributions around this theme. The researchers are encouraged to submit their original works that cover some novel aspects of machine learning in the context of smart systems.

The journal focuses on the following, however, are not limited to innovative applications of:

  • machine learning and cyber-physical systems;
  • intelligent healthcare;
  • predictive models in healthcare;
  • applied machine learning in cybersecurity;
  • Society 5.0 and smart aging;
  • human in the loop;
  • machine learning and environment;
  • machine learning and Industry 4.0 

Submit your paper and select the Journal “Symmetry” and the Special Issue “Emerging Applications of Machine Learning in Smart Systems Symmetry” via: MDPI submission system. Our papers will be published on a rolling basis and we will be pleased to receive your submission once you have finished it.

Dr. Farnaz Farid 
Dr. Farhad Ahamed
Dr. Mahmoud Elkhodr 
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 2000 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.

Published Papers (1 paper)

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Research

Article
Modifications for the Differential Evolution Algorithm
Symmetry 2022, 14(3), 447; https://doi.org/10.3390/sym14030447 - 23 Feb 2022
Cited by 2 | Viewed by 837
Abstract
Differential Evolution (DE) is a method of optimization used in symmetrical optimization problems and also in problems that are not even continuous, and are noisy and change over time. DE optimizes a problem with a population of candidate solutions and creates new candidate [...] Read more.
Differential Evolution (DE) is a method of optimization used in symmetrical optimization problems and also in problems that are not even continuous, and are noisy and change over time. DE optimizes a problem with a population of candidate solutions and creates new candidate solutions per generation in combination with existing rules according to discriminatory rules. The present work proposes two variations for this method. The first significantly improves the termination of the method by proposing an asymptotic termination rule, which is based on the differentiation of the average of the function values in the population of DE. The second modification proposes a new scheme for a critical parameter of the method, which improves the method’s ability to better explore the search space of the objective function. The proposed variations have been tested on a number of problems from the current literature, and from the experimental results, it appears that the proposed modifications render the method quite robust and faster even in large-scale problems. Full article
(This article belongs to the Special Issue Emerging Applications of Machine Learning in Smart Systems Symmetry)
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