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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: closed (28 February 2026) | Viewed by 7958

Special Issue Editors

Dr. Nancy Pérez-Castro

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Guest Editor
Artificial Intelligence Research Institute, University of Veracruz, Campus Sur, Calle Paseo Lote II, Sección Segunda No. 112, Nuevo Xalapa 91097, Mexico
Interests: deep learning; metaheuristics; temporal data mining
Dr. Aldo Márquez-Grajales

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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 250 words) can be sent to the Editorial Office for assessment.

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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Published Papers (6 papers)

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18 pages, 787 KB  
Article
A Comparison Between Heuristic and Automatic Design in Variational Quantum Circuits for the MaxCut Problem Under Noise Effects
by Emmanuel Isaac Juárez Caballero, Horacio Tapia-McClung and Efrén Mezura-Montes
Math. Comput. Appl. 2026, 31(3), 78; https://doi.org/10.3390/mca31030078 - 7 May 2026
Viewed by 260
Abstract
The selection of the right topology (ansatz) for a Variational Quantum Algorithm (VQA) is a complex task that usually involves deep knowledge of a particular problem. The importance of the selection is greater when we consider the current state of quantum hardware, particularly [...] Read more.
The selection of the right topology (ansatz) for a Variational Quantum Algorithm (VQA) is a complex task that usually involves deep knowledge of a particular problem. The importance of the selection is greater when we consider the current state of quantum hardware, particularly the noise associated with the complexity of Variational Quantum Circuits (VQCs) that implement VQAs. Here, a comparison is presented between two confronted approaches for solving the MaxCut problem: QAOA, which has a theoretical proof of convergence, and the automatic design proposal (QNAS), which relies on evolutionary algorithms (NSGA-II) to discover efficient circuits. The comparison was made across 490 graph instances from different graph topologies and sizes (n=4 to n=16), accounting for noise models such as depolarizing noise, gate errors, and readout noise. The results show that QAOA achieves an approximation ratio (rA) ≈1 on complete graphs at the cost of being almost 12 times more complex than QNAS in ideal conditions while approaching the random noise floor (rA≈0.5). QNAS was capable of finding circuits less complex while maintaining 69% of the fidelity at a cost of having an rA on the interval 0.7≤rA≤0.8. However, when the comparison is made across sparse graphs, performance is comparable, while QNAS is less complex. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2025)
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26 pages, 1015 KB  
Article
AI-Driven Biopsychosocial Screening for Breast Cancer: Enhancing Risk Prediction via Differential Evolutionary Linear Discriminant Analysis for Feature Extraction
by José Luis Llaguno-Roque, Adriana Laura López-Lobato, Juan Carlos Pérez-Arriaga, Héctor Gabriel Acosta-Mesa, Ángel J. Sánchez-García, Gabriel Gutiérrez-Ospina, Antonia Barranca-Enríquez and Tania Romo-González
Math. Comput. Appl. 2026, 31(3), 66; https://doi.org/10.3390/mca31030066 - 24 Apr 2026
Viewed by 1147
Abstract
In Mexico, the high prevalence and mortality rates associated with breast cancer (BC) constitute a critical public health challenge that demands context-specific preventive measures. This study proposes an integrative framework for predicting BC risk based on a biopsychosocial model. We hypothesize that emotional [...] Read more.
In Mexico, the high prevalence and mortality rates associated with breast cancer (BC) constitute a critical public health challenge that demands context-specific preventive measures. This study proposes an integrative framework for predicting BC risk based on a biopsychosocial model. We hypothesize that emotional suppression and repression act as key neuroendocrine disruptors and predisposing factors within the Mexican female population. To test this, we systematically compared the predictive performance of various machine learning classification models using the clinical, psychological, and combined profiles of 110 women. These models were evaluated with and without the application of a robust evolutionary algorithm: Differential Evolutionary Linear Discriminant Analysis for Feature Extraction (DE−LDAFE). The results demonstrated that integrating clinical and psychological data into a combined latent space significantly improved the performance of the classification algorithms. The Artificial Neural Network achieved the highest metrics (0.9975 Precision; 0.9976 F1-score). However, due to the inherent “black-box” nature of these models (limited clinical interpretability), the Decision Tree emerged as the optimal practical alternative, providing highly competitive (0.8874 Precision; 0.8853 F1-score) and interpretable results. These findings provide empirical evidence that psychological factors, rather than being mere incidental comorbidities, could be associated with the etiology of breast cancer and be used as risk factors in predicting the disease. Ultimately, this AI-driven biopsychosocial screening model offers a scalable, low-cost, and context-adapted risk assessment tool for early BC diagnosis in Mexican women. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2025)
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25 pages, 4170 KB  
Article
Neuroevolution of Liquid State Machine Based on Neural Configurations and Positions
by Carlos-Alberto López-Herrera, Héctor-Gabriel Acosta-Mesa, Efrén Mezura-Montes and Jesús-Arnulfo Barradas-Palmeros
Math. Comput. Appl. 2026, 31(2), 65; https://doi.org/10.3390/mca31020065 - 21 Apr 2026
Viewed by 575
Abstract
Liquid State Machines (LSMs), a reservoir computing model based on recurrent spiking neural networks, provide a powerful framework for solving spatiotemporal classification tasks by leveraging rich temporal dynamics and event-driven processing. Although the traditional LSM formulation assumes a fixed, randomly generated reservoir, recent [...] Read more.
Liquid State Machines (LSMs), a reservoir computing model based on recurrent spiking neural networks, provide a powerful framework for solving spatiotemporal classification tasks by leveraging rich temporal dynamics and event-driven processing. Although the traditional LSM formulation assumes a fixed, randomly generated reservoir, recent research has explored optimization strategies to improve liquid dynamics. However, most existing approaches focus primarily on optimizing synaptic connectivity or reservoir structure, while the role of neuron-level parameters remains largely underexplored. This work proposes a neuroevolutionary strategy based on a Genetic Algorithm (GA) that encodes both neuron configurations and their spatial positions, explicitly treating neuron-level parameters as optimization targets. By evolving neuron-specific parameters and spatial positions, the method induces diverse reservoir dynamics. Unlike approaches that directly optimize synaptic weights, the proposed representation maintains an encoding whose dimensionality scales linearly with the number of neurons. The approach was evaluated on four synthetic benchmark tasks, including one Frequency Recognition task and three Pattern Recognition tasks, using compact reservoirs composed of only 20 Leaky Integrate-and-Fire neurons. Despite the small reservoir size, the method achieved state-of-the-art or highly competitive performance, reaching mean accuracies of up to 99.71%. In the most challenging case (PR12), performance improved when the reservoir size was increased to 64 neurons. The method was further evaluated on two real-world datasets, N-MNIST and the Free Spoken Digit Dataset (FSDD), using reservoirs of 300 neurons, achieving 90.65% and 81.47% accuracy, respectively, while using substantially fewer neurons than many existing LSM-based approaches. These results highlight the potential of evolving neuron configurations and spatial organization to produce compact and effective liquid reservoirs. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2025)
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21 pages, 1463 KB  
Article
A Mathematical Framework for E-Commerce Sales Prediction Using Attention-Enhanced BiLSTM and Bayesian Optimization
by Hao Hu, Jinshun Cai and Chenke Xu
Math. Comput. Appl. 2026, 31(1), 17; https://doi.org/10.3390/mca31010017 - 22 Jan 2026
Viewed by 798
Abstract
Accurate sales prediction is crucial for inventory and marketing in e-commerce. Cross-border sales involve complex patterns that traditional models cannot capture. To address this, we propose an improved Bidirectional Long Short-Term Memory (BiLSTM) model, enhanced with an attention mechanism and Bayesian hyperparameter optimization. [...] Read more.
Accurate sales prediction is crucial for inventory and marketing in e-commerce. Cross-border sales involve complex patterns that traditional models cannot capture. To address this, we propose an improved Bidirectional Long Short-Term Memory (BiLSTM) model, enhanced with an attention mechanism and Bayesian hyperparameter optimization. The attention mechanism focuses on key temporal features, improving trend identification. The BiLSTM captures both forward and backward dependencies, offering deeper insights into sales patterns. Bayesian optimization fine-tunes hyperparameters such as learning rate, hidden-layer size, and dropout rate to achieve optimal performance. These innovations together improve forecasting accuracy, making the model more adaptable and efficient for cross-border e-commerce sales. Experimental results show that the model achieves an Root Mean Square Error (RMSE) of 13.2, Mean Absolute Error (MAE) of 10.2, Mean Absolute Percentage Error (MAPE) of 8.7 percent, and a Coefficient of Determination (R2) of 0.92. It outperforms baseline models, including BiLSTM (RMSE 16.5, MAPE 10.9 percent), BiLSTM with Attention (RMSE 15.2, MAPE 10.1 percent), Temporal Convolutional Network (RMSE 15.0, MAPE 9.8 percent), and Transformer for Time Series (RMSE 14.8, MAPE 9.5 percent). These results highlight the model’s superior performance in forecasting cross-border e-commerce sales, making it a valuable tool for inventory management and demand planning. Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2025)
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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
Cited by 1 | Viewed by 3091
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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31 pages, 461 KB  
Systematic Review
Techniques Applied to Autonomous Liquid Pouring: A Scoping Review
by Jeeangh Jennessi Reyes-Montiel, Ericka Janet Rechy-Ramirez and Antonio Marin-Hernandez
Math. Comput. Appl. 2026, 31(1), 30; https://doi.org/10.3390/mca31010030 - 14 Feb 2026
Viewed by 795
Abstract
In recent years, autonomous liquid pouring systems have gained more relevance, with applications from daily service tasks to complex industrial operations. While seemingly simple for humans, this task poses major challenges for automated systems, as it requires precise control and adaptation to varying [...] Read more.
In recent years, autonomous liquid pouring systems have gained more relevance, with applications from daily service tasks to complex industrial operations. While seemingly simple for humans, this task poses major challenges for automated systems, as it requires precise control and adaptation to varying container geometries, liquid properties, and environmental conditions. This review examines the state-of-the-art on liquid pouring through five research questions: (1) What are the characteristics of the liquids used in the experiments? (2) What are the characteristics of the containers used in the experiments and how do they affect the performance of the pouring tasks? (3) What techniques are used to control liquid pouring (i.e., to control the robotic arm or device)? (4) What metrics are used to assess the methods for pouring liquid? (5) What devices are used to measure poured volume? This scoping review follows the Arksey and O’Malley framework, and uses the PRISMA-ScR protocol to filter the articles. A total of 285 studies published between 2018 and 2025 were screened from IEEE Xplore, SpringerLink, ScienceDirect, Web of Science, and EBSCOhost, of which 23 met the inclusion criteria. Results showed that the most widely used methods for autonomous liquid pouring were classical control methods—PID, PD (30.4% of the studies). Conversely, the least widely used methods for autonomous liquid pouring were learning, imitation learning, and probabilistic models (15% of the studies). Full article
(This article belongs to the Special Issue New Trends in Computational Intelligence and Applications 2025)
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