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MCA
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New Trends in Computational Intelligence and Applications 2024
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New Trends in Computational Intelligence and Applications 2024

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

Deadline for manuscript submissions: closed (15 April 2025) | Viewed by 1373

Special Issue Editors

Dr. Mario Graff

E-Mail Website
Guest Editor
CONACYT—INFOTEC Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación, Aguascalientes 20313, Mexico
Interests: natural language processing; machine learning; evolutionary computation
Dr. Héctor-Gabriel Acosta-Mesa

E-Mail Website
Guest Editor
Instituto de Investigaciones en Inteligencia Artificial, University of Veracruz, Xalapa 91000, Mexico
Interests: computer vision; machine learning; medical image processing; artificial intelligence applications
Special Issues, Collections and Topics in MDPI journals

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 has substantially grown, and more spaces for discussion on these topics are required.

This Special Issue will comprise selected papers presented at the 6th Workshop on New Trends in Computational Intelligence and Applications (CIAPP 2024, see https://ciapp.bi-level.org/2024/ for detailed information). Papers considered relevant to the journal's scope and of sufficient 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. Mario Graff
Dr. Héctor-Gabriel Acosta-Mesa
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.

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 (2 papers)

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13 pages, 4627 KiB  
Article
Detection of Abnormal Pedestrian Flows with Automatic Contextualization Using Pre-Trained YOLO11n
by Adrián Núñez-Vieyra, Juan C. Olivares-Rojas, Rogelio Ferreira-Escutia, Arturo Méndez-Patiño, José A. Gutiérrez-Gnecchi and Enrique Reyes-Archundia
Math. Comput. Appl. 2025, 30(2), 44; https://doi.org/10.3390/mca30020044 - 17 Apr 2025
Viewed by 167
Abstract
Recently, video surveillance systems have evolved from expensive, human-operated monitoring systems that were only useful after the crime was committed to systems that monitor 24/7, in real time, and with less and less human involvement. This is partly due to the use of [...] Read more.
Recently, video surveillance systems have evolved from expensive, human-operated monitoring systems that were only useful after the crime was committed to systems that monitor 24/7, in real time, and with less and less human involvement. This is partly due to the use of smart cameras, the improvement of the Internet, and AI-based algorithms that allow the classifying and tracking of objects in images and in some cases identifying them as threats. Threats are often associated with abnormal or unexpected situations such as the presence of unauthorized persons in a given place or time, the manifestation of a different behavior by one or more persons compared to the behavior of the majority, or simply an unexpected number of people in the place, which depends largely on the available information of their context, i.e., place, date, and time of capture. In this work, we propose a model to automatically contextualize video capture scenarios, generating data such as location, date, time, and flow of people in the scene. A strategy to measure the accuracy of the data generated for such contextualization is also proposed. The pre-trained YOLO11n algorithm and the Bot-SORT algorithm gave the best results in person detection and tracking, respectively. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2024)
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21 pages, 6361 KiB  
Article
Imaging Estimation for Liver Damage Using Automated Approach Based on Genetic Programming
by David Herrera-Sánchez, Héctor-Gabriel Acosta-Mesa, Efrén Mezura-Montes, Socorro Herrera-Meza, Eduardo Rivadeneyra-Domínguez, Isaac Zamora-Bello and María Fernanda Almanza-Domínguez
Math. Comput. Appl. 2025, 30(2), 25; https://doi.org/10.3390/mca30020025 - 28 Feb 2025
Viewed by 523
Abstract
Computer vision and image processing have become relevant in recent years due to their capabilities to support different tasks in several areas. Image classification, segmentation, and estimation are relevant issues addressed using various techniques. Imaging estimation is very important and helpful in biological [...] Read more.
Computer vision and image processing have become relevant in recent years due to their capabilities to support different tasks in several areas. Image classification, segmentation, and estimation are relevant issues addressed using various techniques. Imaging estimation is very important and helpful in biological applications. This work proposes a new approach for estimating the damages in the livers of the Wistar rats, using high-resolution RGB images. Instead of using invasive methods to determine the level of damage, the proposal allows us to measure the damage in the livers. The proposal is based on Genetic Programming (GP), the paradigm of evolutionary computing, which has become relevant in recent years for image-processing tasks. It provides flexibility, which allows the use of image processing functions to extract meaningful information from raw images. Furthermore, it allows the configuration of the regression model by performing a hyperparameter tuning to improve estimation performance. The approach includes a new set of functions through which the regression model is configured. Additionally, a set of functions is included to change the color spaces of the images to extract meaningful features from them. The results demonstrate the effectiveness of our approach when making the hyperparameter tuning and the efficiency in dealing with different color spaces, thus achieving the promised results when estimating according to the R2, Mean Average Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) indicators. The proposed method achieves values higher than 0.5 of R2 and lower than 0.51 of MSE, using different regression models. Additionally, the approach demonstrates that image preprocessing is necessary for improving the model’s performance, which is better than only using raw data where the values of RMSE are greater than 1.5. The lowest MSE value of our proposed method was 0.51, outperforming the methods without preprocessing. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2024)
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