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Mathematics
Special Issues
Fuzzy Systems and Hybrid Intelligence Models
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Fuzzy Systems and Hybrid Intelligence Models

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 935

Special Issue Editor

Dr. Paulo Vitor Campos Souza

E-Mail Website
Guest Editor
Intelligent Digital Agent Unit in Povo-Trento, Fondazione Bruno Kessler, 38122 Trento, Italy
Interests: fuzzy neural networks; evolving systems; pattern classification problems; interpretability

Special Issue Information

Dear Colleagues,

We are excited to announce a Special Issue of the Mathematics Journal dedicated to exploring fuzzy systems and hybrid intelligence models. With their inherent ability to handle uncertainty and imprecision, fuzzy systems have long been at the forefront of intelligent systems research. This Special Issue aims to showcase the latest advancements in fuzzy systems, focusing on their integration into hybrid intelligence models applied to different areas such as industry, medicine, cyber security, and others.

In an ever-evolving technological landscape, hybrid intelligence models that combine fuzzy logic with other AI techniques have demonstrated remarkable potential for solving complex, real-world problems. This issue seeks contributions that highlight the development and applications of evolving fuzzy systems, shedding light on how these models can enhance decision-making processes across various domains, such as healthcare, finance, robotics, and beyond.

We invite researchers and practitioners to submit their original research, reviews, and case studies that delve into the innovative fusion of fuzzy systems and hybrid intelligence. Join us in advancing the frontiers of intelligent systems and exploring their practical applications. Together, we aim to foster a deeper understanding of fuzzy systems' role in shaping the future of AI and decision support systems.

Dr. Paulo Vitor Campos Souza
Guest Editor

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. Mathematics 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 2600 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

  • hybrid systems
  • fuzzy neural networks
  • evolving fuzzy neural network
  • knowledge-based systems
  • fuzzy systems
  • neuro-fuzzy approaches

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Published Papers (1 paper)

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Research

12 pages, 3564 KiB  
Article
Association between Premature Birth and Air Pollutants Using Fuzzy and Adaptive Neuro-Fuzzy Inference System (ANFIS) Techniques
by Taynara de Oliveira Castellões, Paloma Maria Silva Rocha Rizol and Luiz Fernando Costa Nascimento
Mathematics 2024, 12(18), 2828; https://doi.org/10.3390/math12182828 - 12 Sep 2024
Viewed by 555
Abstract
This article uses machine learning techniques as fuzzy and neuro-fuzzy ANFISs, to develop and compare prediction models capable of relating pregnant women’s exposure to air pollutants, such as Nitrogen Dioxide and Particulate Matter, the mother’s age, and the number of prenatal consultations to [...] Read more.
This article uses machine learning techniques as fuzzy and neuro-fuzzy ANFISs, to develop and compare prediction models capable of relating pregnant women’s exposure to air pollutants, such as Nitrogen Dioxide and Particulate Matter, the mother’s age, and the number of prenatal consultations to the incidence of premature birth. In the current literature, studies can be found that relate prematurity to the exposure of pregnant women to NO2, O3, and PM10; to Toluene and benzene, mainly in the window 5 to 10 days before birth; and to PM10 in the week before birth. Both models used logistic regression to quantify the effects of pollutants as a result of premature birth. Datasets from Brazil—Departamento de Informatica do Sistema Único de Saúde (DATASUS) and Companhia Ambiental do Estado de São Paulo (CETESB)—were used, covering the period from 2016 to 2018 and comprising women living in the city of São José dos Campos (SP), Brazil. In order to evaluate and compare the different techniques used, evaluation metrics were calculated, such as correlation (r), coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Mean Absolute Error (MAE). These metrics are widely used in the literature due to their ability to evaluate the robustness and efficiency of prediction models. For the RMSE, MAPE, MSE, and MAE metrics, lower values indicate that prediction errors are smaller, demonstrating better model accuracy and confidence. In the case of (r) and R2, a positive and strong result indicates alignment and better performance between the real and predicted data. The neuro-fuzzy ANFIS model showed superior performance, with a correlation (r) of 0.59, R2 = 0.35, RMSE = 2.83, MAPE = 5.35%, MSE = 8.00, and MAE = 1.70, while the fuzzy model returned results of r = 0.20, R2 = 0.04, RMSE = 3.29, MSE = 10.81, MAPE = 6.67%, and MAE = 2.01. Therefore, the results from the ANFIS neuro-fuzzy system indicate greater prediction capacity and precision in relation to the fuzzy system. This superiority can be explained by integration with neural networks, allowing data learning and, consequently, more efficient modeling. In addition, the findings obtained in this study have potential for the formulation of public health policies aimed at reducing the number of premature births and promoting improvements in maternal and neonatal health. Full article
(This article belongs to the Special Issue Fuzzy Systems and Hybrid Intelligence Models)
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