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Symmetry
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Symmetry in Computational Intelligence and Applications
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Symmetry in Computational Intelligence and Applications

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 12294

Special Issue Editors

Dr. Danilo Sipoli Sanches

E-Mail Website
Guest Editor
Departamento Acadêmico de Computação, Universidade Tecnológica Federal do Paraná, Curitiba, Brazil
Interests: bioinformatics; non-coding DNA/RNA; meachine learning
Dr. Lucas Dias Hiera Sampaio

E-Mail Website
Guest Editor
Department of Computer Science, Universidade Tecnológica Federal do Paraná, Paraná, Brazil
Interests: telecommunications; evolutionary computation
Dr. Henrique Yoshikazu Shishido

E-Mail Website
Guest Editor
Department of Computing, Universidade Tecnológica Federal do Paraná, Paraná, Brazil
Interests: grid; cloud and fog computing; scheduling on cloud; edge; workflow; health informatics; learning objects; evolutionary computation
Dr. Cléber Gimenez Corrêa

E-Mail Website
Guest Editor
Department of Computer Science, Universidade Tecnológica Federal do Paraná, Paraná, Brazil
Interests: human-computer interaction; virtual reality; augmented reality
Dr. Silvio Ricardo Rodrigues Sanches

E-Mail Website
Guest Editor
Department of Computer Science, Universidade Tecnológica Federal do Paraná, Paraná, Brazil
Interests: computer vision; virtual reality; augmented reality

Special Issue Information

Dear Colleagues,

This Special Issue covers symmetry/asymmetry phenomena on computational intelligence methods and applications in different fields, including: artificial intelligence, cognitive approaches, web intelligence, knowledge mining, cybernetic, cyber-physical systems, cloud computing, telecommunications, computer vision, virtual reality, and bioinformatics.

We hope to spread information among researchers, designers, manufacturers, and users in this exciting field with this Special Issue.

Dr. Danilo Sipoli Sanches
Dr. Lucas Dias Hiera Sampaio
Dr. Henrique Yoshikazu Shishido
Dr. Cléber Gimenez Corrêa
Dr. Silvio Ricardo Rodrigues Sanches
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.

 

Published Papers (3 papers)

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Research

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17 pages, 9527 KiB  
Article
Two Residual Attention Convolution Models to Recover Underexposed and Overexposed Images
by Noorman Rinanto and Shun-Feng Su
Symmetry 2023, 15(10), 1850; https://doi.org/10.3390/sym15101850 - 01 Oct 2023
Viewed by 767
Abstract
Inconsistent lighting phenomena in digital images, such as underexposure and overexposure, pose challenges in computer vision. Many studies have developed to address these issues. However, most of these techniques cannot remedy both exposure problems simultaneously. Meanwhile, existing methods that claim to be capable [...] Read more.
Inconsistent lighting phenomena in digital images, such as underexposure and overexposure, pose challenges in computer vision. Many studies have developed to address these issues. However, most of these techniques cannot remedy both exposure problems simultaneously. Meanwhile, existing methods that claim to be capable of handling these cases have not yielded optimal results, especially for images with blur and noise distortions. Therefore, this study proposes a system to improve underexposed and overexposed photos, consisting of two different residual attention convolution networks with the CIELab color space as the input. The first model working on the L-channel (luminance) is responsible for recovering degraded image illumination by using residual memory block networks with self-attention layers. The next model based on dense residual attention networks aims to restore degraded image colors using ab-channels (chromatic). A properly exposed image is produced by fusing the output of these models and converting them to RGB color space. Experiments on degraded synthetic images from two public datasets and one real-life exposure dataset demonstrate that the proposed system outperforms the state-of-the-art algorithms in optimal illumination and color correction outcomes for underexposed and overexposed images. Full article
(This article belongs to the Special Issue Symmetry in Computational Intelligence and Applications)
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19 pages, 897 KiB  
Article
Fathoming the Mandela Effect: Deploying Reinforcement Learning to Untangle the Multiverse
by A’aeshah Alhakamy
Symmetry 2023, 15(3), 699; https://doi.org/10.3390/sym15030699 - 10 Mar 2023
Viewed by 8277
Abstract
Multiverse is a hypothetical idea that other universes can exist beyond our own. Various scientific theories have suggested scenarios such as the existence of bubble universes that constantly expand or string theory that attempts to merge gravity with other forces. Thus, a multiverse [...] Read more.
Multiverse is a hypothetical idea that other universes can exist beyond our own. Various scientific theories have suggested scenarios such as the existence of bubble universes that constantly expand or string theory that attempts to merge gravity with other forces. Thus, a multiverse is a complex theoretical phenomenon that can best be conceived through computer simulation. Albeit within the multiverse, the causality of the Mandela effect is entirely possible. To examine the behavior of the multiverse as a representative ensemble, each universe as a specific ensemble element needs to be generated. Our universe generation is based on unique universes for two binary attributes of a population of n=303. The maximum possible universes this could produce within the multiverse is in the exponent of 182. To computationally confine the simulation to the scope of this study, the sample count of the multiverse is nmultiverse=606. Parameters representing the existence of each multiverse are implemented through the μ and σ values of each universe’s attributes. By using a developed reinforcement learning algorithm, we generate a multiverse yielding various universes. The computer gains consciousness of the parameters that can represent the expanse of possibility to exist for multiple universes. Furthermore, for each universe, a heart attack prediction model is performed to understand the universe’s environment and behavior. We test the Mandela effect or déjà vu of each universe by comparing error test losses with the training size of order M. Our model can measure the behavior of environments in different regions referred to as specific ensemble elements. By explicitly exploiting the attributes of each universe, we can get a better idea of the possible outcomes for the creation of other specific ensemble elements, as seen in the multiverse space planes. Full article
(This article belongs to the Special Issue Symmetry in Computational Intelligence and Applications)
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Review

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25 pages, 591 KiB  
Review
Exploring Cybersecurity Education and Training Techniques: A Comprehensive Review of Traditional, Virtual Reality, and Augmented Reality Approaches
by Abdullah M. Alnajim, Shabana Habib, Muhammad Islam, Hazim Saleh AlRawashdeh and Muhammad Wasim
Symmetry 2023, 15(12), 2175; https://doi.org/10.3390/sym15122175 - 07 Dec 2023
Viewed by 2604
Abstract
Considering the alarming increase in cyberattacks and their potential financial implications, the importance of cybersecurity education and training cannot be overstated. This paper presents a systematic literature review that examines different cybersecurity education and training techniques with a focus on symmetry. It primarily [...] Read more.
Considering the alarming increase in cyberattacks and their potential financial implications, the importance of cybersecurity education and training cannot be overstated. This paper presents a systematic literature review that examines different cybersecurity education and training techniques with a focus on symmetry. It primarily focuses on traditional cybersecurity education techniques and emerging technologies, such as virtual reality (VR) and augmented reality (AR), through the lens of symmetry. The main objective of this study is to explore the existing cybersecurity training techniques, identify the challenges involved, and assess the effectiveness of cybersecurity training based on VR and AR while emphasizing the concept of symmetry. Through careful selection criteria, 66 primary studies were selected from a total of 150 pertinent research studies. This article offers valuable insights into the pros and cons of conventional training approaches, explores the use of VR and AR in cybersecurity education concerning symmetry, and thoroughly discusses the challenges associated with these technologies. The findings of this review contribute significantly to the continuing efforts in cybersecurity education by offering recommendations for improving employees’ knowledge, engagement, and motivation in cybersecurity training programs while maintaining symmetry in the learning process. Full article
(This article belongs to the Special Issue Symmetry in Computational Intelligence and Applications)
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