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New Trends in Computational Intelligence and Applications 2023
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New Trends in Computational Intelligence and Applications 2023

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 6802

Special Issue Editors

Dr. Efrén Mezura-Montes

E-Mail Website
Guest Editor
Artificial Intelligence Research Institute, University of Veracruz, Xalapa, Veracruz 91097, Mexico
Interests: evolutionary computing; machine learning; swarm intelligence; optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Rafael Rivera-López

E-Mail Website
Guest Editor
DSC, Tecnológico Nacional de México, Instituto Tecnológico de Veracruz, Veracruz 91897, Veracruz, Mexico
Interests: evolutionary algorithms; machine learning; object-oriented programming

Special Issue Information

Dear Colleagues,

This Special Issue will mainly comprise selected papers presented at the 5th Workshop on New Trends in Computational Intelligence and Applications (CIAPP 2023, see https://bi-level.org/ciapp/ for detailed information). Papers considered to be relevant to the scope of the journal and to be of sufficient quality after evaluation by the reviewers will be published free of charge.

The primary topics of this Special Issue are as follows:

  • 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. Efrén Mezura-Montes
Dr. Rafael Rivera-López
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.

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

Published Papers (5 papers)

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Research

13 pages, 2327 KiB  
Article
An Alternative Analysis of Computational Learning within Behavioral Neuropharmacology in an Experimental Anxiety Model Investigation
by Isidro Vargas-Moreno, Héctor Gabriel Acosta-Mesa, Juan Francisco Rodríguez-Landa, Martha Lorena Avendaño-Garrido, Rafael Fernández-Demeneghi and Socorro Herrera-Meza
Math. Comput. Appl. 2024, 29(5), 76; https://doi.org/10.3390/mca29050076 - 9 Sep 2024
Viewed by 866
Abstract
Behavioral neuropharmacology, a branch of neuroscience, uses behavioral analysis to demonstrate treatment effects on animal models, which is fundamental for pre-clinical evaluation. Typically, this determination is univariate, neglecting the relevant associations for understanding treatment effects in animals and humans. This study implements regression [...] Read more.
Behavioral neuropharmacology, a branch of neuroscience, uses behavioral analysis to demonstrate treatment effects on animal models, which is fundamental for pre-clinical evaluation. Typically, this determination is univariate, neglecting the relevant associations for understanding treatment effects in animals and humans. This study implements regression trees and Bayesian networks from a multivariate perspective by using variables obtained from behavioral tests to predict the time spent in the open arms of the elevated arm maze, a key variable to assess anxiety. Three doses of allopregnanolone were analyzed and compared to a vehicle group and a diazepam-positive control. Regression trees identified cut-off points between the anxiolytic and anxiogenic effects, with the anxiety index standing out as a robust predictor, combined with the percentage of open-arm entries and the number of entries. Bayesian networks facilitated the visualization and understanding of the interactions between multiple behavioral and biological variables, demonstrating that treatment with allopregnanolone (2 mg) emulates the effects of diazepam, validating the multivariate approach. The results highlight the relevance of integrating advanced methods, such as Bayesian networks, into preclinical research to enrich the interpretation of complex behavioral data in animal models, which can hardly be observed with univariate statistics. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2023)
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20 pages, 748 KiB  
Article
Causal Analysis to Explain the Performance of Algorithms: A Case Study for the Bin Packing Problem
by Jenny Betsabé Vázquez-Aguirre, Guadalupe Carmona-Arroyo, Marcela Quiroz-Castellanos and Nicandro Cruz-Ramírez
Math. Comput. Appl. 2024, 29(5), 73; https://doi.org/10.3390/mca29050073 - 28 Aug 2024
Viewed by 703
Abstract
This work presents a knowledge discovery approach through Causal Bayesian Networks for understanding the conditions under which the performance of an optimization algorithm can be affected by the characteristics of the instances of a combinatorial optimization problem (COP). We introduce a case study [...] Read more.
This work presents a knowledge discovery approach through Causal Bayesian Networks for understanding the conditions under which the performance of an optimization algorithm can be affected by the characteristics of the instances of a combinatorial optimization problem (COP). We introduce a case study for the causal analysis of the performance of two state-of-the-art algorithms for the one-dimensional Bin Packing Problem (BPP). We meticulously selected the set of features associated with the parameters that define the instances of the problem. Subsequently, we evaluated the algorithmic performance on instances with distinct features. Our analysis scrutinizes both instance features and algorithm performance, aiming to identify causes influencing the performance of the algorithms. The proposed study successfully identifies specific values affecting algorithmic effectiveness and efficiency, revealing shared causes within some value ranges across both algorithms. The knowledge generated establishes a robust foundation for future research, enabling predictions of algorithmic performance, as well as the selection and design of heuristic strategies for improving the performance in the most difficult instances. The causal analysis employed in this study did not require specific configurations, making it an invaluable tool for analyzing the performance of different algorithms in other COPs. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2023)
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15 pages, 4012 KiB  
Article
Estimation of Anthocyanins in Heterogeneous and Homogeneous Bean Landraces Using Probabilistic Colorimetric Representation with a Neuroevolutionary Approach
by José-Luis Morales-Reyes, Elia-Nora Aquino-Bolaños, Héctor-Gabriel Acosta-Mesa and Aldo Márquez-Grajales
Math. Comput. Appl. 2024, 29(4), 68; https://doi.org/10.3390/mca29040068 - 19 Aug 2024
Viewed by 692
Abstract
The concentration of anthocyanins in common beans indicates their nutritional value. Understanding this concentration makes it possible to identify the functional compounds present. Previous studies have presented color characterization as two-dimensional histograms, based on the probability mass function. In this work, we proposed [...] Read more.
The concentration of anthocyanins in common beans indicates their nutritional value. Understanding this concentration makes it possible to identify the functional compounds present. Previous studies have presented color characterization as two-dimensional histograms, based on the probability mass function. In this work, we proposed a new type of color characterization represented by three two-dimensional histograms that consider chromaticity and luminosity channels in order to verify the robustness of the information. Using a neuroevolutionary approach, we also found a convolutional neural network (CNN) for the regression task. The results demonstrate that using three two-dimensional histograms increases the accuracy compared to the color characterization represented by one two-dimensional histogram. As a result, the precision was 93.00 ± 5.26 for the HSI color space and 94.30 ± 8.61 for CIE L*a*b*. Our procedure is suitable for estimating anthocyanins in homogeneous and heterogeneous colored bean landraces. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2023)
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24 pages, 3501 KiB  
Article
Induction of Convolutional Decision Trees with Success-History-Based Adaptive Differential Evolution for Semantic Segmentation
by Adriana-Laura López-Lobato, Héctor-Gabriel Acosta-Mesa and Efrén Mezura-Montes
Math. Comput. Appl. 2024, 29(4), 48; https://doi.org/10.3390/mca29040048 - 27 Jun 2024
Viewed by 833
Abstract
Semantic segmentation is an essential process in computer vision that allows users to differentiate objects of interest from the background of an image by assigning labels to the image pixels. While Convolutional Neural Networks have been widely used to solve the image segmentation [...] Read more.
Semantic segmentation is an essential process in computer vision that allows users to differentiate objects of interest from the background of an image by assigning labels to the image pixels. While Convolutional Neural Networks have been widely used to solve the image segmentation problem, simpler approaches have recently been explored, especially in fields where explainability is essential, such as medicine. A Convolutional Decision Tree (CDT) is a machine learning model for image segmentation. Its graphical structure and simplicity make it easy to interpret, as it clearly shows how pixels in an image are classified in an image segmentation task. This paper proposes new approaches for inducing a CDT to solve the image segmentation problem using SHADE. This adaptive differential evolution algorithm uses a historical memory of successful parameters to guide the optimization process. Experiments were performed using the Weizmann Horse dataset and Blood detection in dark-field microscopy images to compare the proposals in this article with previous results obtained through the traditional differential evolution process. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2023)
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23 pages, 6500 KiB  
Article
M5GP: Parallel Multidimensional Genetic Programming with Multidimensional Populations for Symbolic Regression
by Luis Cárdenas Florido, Leonardo Trujillo, Daniel E. Hernandez and Jose Manuel Muñoz Contreras
Math. Comput. Appl. 2024, 29(2), 25; https://doi.org/10.3390/mca29020025 - 18 Mar 2024
Cited by 2 | Viewed by 2590
Abstract
Machine learning and artificial intelligence are growing in popularity thanks to their ability to produce models that exhibit unprecedented performance in domains that include computer vision, natural language processing and code generation. However, such models tend to be very large and complex and [...] Read more.
Machine learning and artificial intelligence are growing in popularity thanks to their ability to produce models that exhibit unprecedented performance in domains that include computer vision, natural language processing and code generation. However, such models tend to be very large and complex and impossible to understand using traditional analysis or human scrutiny. Conversely, Symbolic Regression methods attempt to produce models that are relatively small and (potentially) human-readable. In this domain, Genetic Programming (GP) has proven to be a powerful search strategy that achieves state-of-the-art performance. This paper presents a new GP-based feature transformation method called M5GP, which is hybridized with multiple linear regression to produce linear models, implemented to exploit parallel processing on graphical processing units for efficient computation. M5GP is the most recent variant from a family of feature transformation methods (M2GP, M3GP and M4GP) that have proven to be powerful tools for both classification and regression tasks applied to tabular data. The proposed method was evaluated on SRBench v2.0, the current standard benchmarking suite for Symbolic Regression. Results show that M5GP achieves performance that is competitive with the state-of-the-art, achieving a top-three rank on the most difficult subset of black-box problems. Moreover, it achieves the lowest computation time when compared to other GP-based methods that have similar accuracy scores. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2023)
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