New Trends in Computational Intelligence and Applications 2022

A special issue of Mathematical and Computational Applications (ISSN 2297-8747).

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 6200

Special Issue Editors


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Guest Editor
Centro de Investigación en Inteligencia Artificial, University of Veracruz, Xalapa 91000, Mexico
Interests: machine learning; medical image processing; unsupervised learning; artificial intelligence applications
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Special Issue Information

Dear Colleagues,

This Special Issue will mainly consist of selected papers presented at the 4th Workshop on New Trends in Computational Intelligence and Applications (CIAPP 2022, see https://bi-level.org/ciapp/ for detailed information). Papers considered to fit the scope of the journal and to be of sufficient quality after evaluation by the reviewers will be published free of charge.

The main topics of this Special Issue are:

  • Machine learning
  • Data mining
  • Statistical learning
  • Automatic image processing
  • Intelligent agents/multi-agent systems
  • Evolutionary computing
  • Swarm intelligence
  • Combinatorial and numerical optimization
  • Parallel and distributed computing in computational intelligence

Dr. Marcela Quiroz-Castellanos
Dr. Héctor-Gabriel Acosta-Mesa
Dr. Efrén Mezura-Montes
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. Mathematical and Computational Applications is an international peer-reviewed open access semimonthly 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 1400 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 (2 papers)

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Research

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15 pages, 381 KiB  
Communication
Estimation of the Equivalent Circuit Parameters in Transformers Using Evolutionary Algorithms
by Hector Ascencion-Mestiza, Serguei Maximov, Efrén Mezura-Montes, Juan Carlos Olivares-Galvan, Rodrigo Ocon-Valdez and Rafael Escarela-Perez
Math. Comput. Appl. 2023, 28(2), 36; https://doi.org/10.3390/mca28020036 - 3 Mar 2023
Cited by 3 | Viewed by 2054
Abstract
The conventional methods of parameter estimation in transformers, such as the open-circuit and short-circuit tests, are not always available, especially when the transformer is already in operation and its disconnection is impossible. Therefore, alternative (non-interruptive) methods of parameter estimation have become of great [...] Read more.
The conventional methods of parameter estimation in transformers, such as the open-circuit and short-circuit tests, are not always available, especially when the transformer is already in operation and its disconnection is impossible. Therefore, alternative (non-interruptive) methods of parameter estimation have become of great importance. In this work, no-interruption, transformer equivalent circuit parameter estimation is presented using the following metaheuristic optimization methods: the genetic algorithm (GA), particle swarm optimization (PSO) and the gravitational search algorithm (GSA). These algorithms provide a maximum average error of 12%, which is twice as better as results found in the literature for estimation of the equivalent circuit parameters in transformers at a frequency of 50 Hz. This demonstrates that the proposed GA, PSO and GSA metaheuristic optimization methods can be applied to estimate the equivalent circuit parameters of single-phase distribution and power transformers with a reasonable degree of accuracy. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2022)
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Review

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19 pages, 1098 KiB  
Review
An Overview of the Vision-Based Human Action Recognition Field
by Fernando Camarena, Miguel Gonzalez-Mendoza, Leonardo Chang and Ricardo Cuevas-Ascencio
Math. Comput. Appl. 2023, 28(2), 61; https://doi.org/10.3390/mca28020061 - 13 Apr 2023
Cited by 2 | Viewed by 3308
Abstract
Artificial intelligence’s rapid advancement has enabled various applications, including intelligent video surveillance systems, assisted living, and human–computer interaction. These applications often require one core task: video-based human action recognition. Research in human video-based human action recognition is vast and ongoing, making it difficult [...] Read more.
Artificial intelligence’s rapid advancement has enabled various applications, including intelligent video surveillance systems, assisted living, and human–computer interaction. These applications often require one core task: video-based human action recognition. Research in human video-based human action recognition is vast and ongoing, making it difficult to assess the full scope of available methods and current trends. This survey concisely explores the vision-based human action recognition field and defines core concepts, including definitions and explanations of the common challenges and most used datasets. Additionally, we provide in an easy-to-understand manner the literature approaches and their evolution over time, emphasizing intuitive notions. Finally, we explore current research directions and potential future paths. The core goal of this work is to provide future works with a shared understanding of fundamental ideas and clear intuitions about current works and find new research opportunities. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2022)
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