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MCA
Special Issues
New Trends in Computational Intelligence and Applications 2025
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New Trends in Computational Intelligence and Applications 2025

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 346

Special Issue Editors

Dr. Nancy Pérez-Castro

E-Mail Website
Guest Editor
Computer Engineering Program, Universidad del Papaloapan, Av. Ferrocarril s/n, Col.Ciudad Universitaria, Loma Bonita C.P. 68400, Oaxaca, Mexico
Interests: deep learning; metaheuristics; temporal data mining
Dr. Aldo Márquez-Grajales

E-Mail
Guest Editor
Área Académica de Computación y Electrónica, Instituto de Ciencias Básicas e Ingeniería, Universidad Autónoma del Estado de Hidalgo, Carretera Pachuca-Tulancingo Km. 4.5, Col. Carboneras, Mineral de la Reforma, Hidalgo C.P. 42184, Mexico
Interests: machine learning; bio-inspired computation; time series analysis; artificial intelligence applied to climate science

Special Issue Information

Dear Colleagues, 

Computational Intelligence (CI) paradigms have become a critical factor in the resurgence of Artificial Intelligence, which is now part of daily life. Therefore, basic and applied CI research have substantially grown, and more spaces for discussion on these topics are required.

The Workshop on New Trends in Computational Intelligence and Applications aims to put together researchers, practitioners, students, and those interested in presenting novel findings and applications related to computational intelligence techniques. The workshop also aims to serve as a platform for establishing possible collaborations among attendees.

This Special Issue will comprise selected papers presented at the 7th Workshop on New Trends in Computational Intelligence and Applications (CIAPP 2025; see https://ciapp.bi-level.org/ for detailed information). Papers considered relevant to the journal's scope and of high quality after evaluation by the reviewers will be published free of charge.

The topics include, but are not limited, to the following:

  • 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. Nancy Pérez-Castro
Dr. Aldo Márquez-Grajales
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 1600 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
  • data mining
  • statistical learning

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

Published Papers (1 paper)

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Research

20 pages, 4230 KB  
Article
HGREncoder: Enhancing Real-Time Hand Gesture Recognition with Transformer Encoder—A Comparative Study
by Luis Gabriel Macías, Jonathan A. Zea, Lorena Isabel Barona, Ángel Leonardo Valdivieso and Marco E. Benalcázar
Math. Comput. Appl. 2025, 30(5), 101; https://doi.org/10.3390/mca30050101 - 16 Sep 2025
Viewed by 21
Abstract
In the field of Hand Gesture Recognition (HGR), Electromyography (EMG) is used to detect the electrical impulses that muscles emit when a movement is generated. Currently, there are several HGR models that use EMG to predict hand gestures. However, most of these models [...] Read more.
In the field of Hand Gesture Recognition (HGR), Electromyography (EMG) is used to detect the electrical impulses that muscles emit when a movement is generated. Currently, there are several HGR models that use EMG to predict hand gestures. However, most of these models have limited performance in real-time applications, with the highest recognition rate achieved being 65.78 ± 15.15%, without post-processing steps. Other non-generalizable models, i.e., those trained with a small number of users, achieved a window-based classification accuracy of 93.84%, but not in time-real applications. Therefore, this study addresses these issues by employing transformers to create a generalizable model and enhance recognition accuracy in real-time applications. The architecture of our model is composed of a Convolutional Neural Network (CNN), a positional encoding layer, and the transformer encoder. To obtain a generalizable model, the EMG-EPN-612 dataset was used. This dataset contains records of 612 individuals. Several experiments were conducted with different architectures, and our best results were compared with other previous research that used CNN, LSTM, and transformers. The findings of this research reached a classification accuracy of 95.25 ± 4.9% and a recognition accuracy of 89.7 ± 8.77%. This recognition accuracy is a significant contribution because it encompasses the entire sequence without post-processing steps. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2025)
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