Special Issue "Fuzzy Logic in Artificial Intelligence Systems"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Set Theory".

Deadline for manuscript submissions: 30 November 2022.

Special Issue Editor

Dr. Danilo Pelusi
E-Mail Website
Guest Editor
Faculty of Communication Sciences, University of Teramo, 64100 Teramo, Italy
Interests: evolutionary algorithms; fuzzy logic; neural networks; genetic algorithms; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The capability to learn from data is a proper feature of machine learning (ML). MLs are based on the idea that systems can learn from data whether suitably trained. However, such systems do not provide the expected results because the degree of knowledge is poor or even missing. For designing suitable artificial intelligent systems, either learning and knowledge are needed. Thus, the task of fuzzy logic is to add knowledge to the systems for improving their performances. The combination between knowledge and learning has given rise to the design of hybrid intelligent systems. The optimization of this kind of systems is also based on nature-inspired optimization algorithms. Therefore, ML nature-inspired systems can be enhanced by adding fuzzy techniques. This Special Issue provides a platform for researchers from academia and industry to present their novel and unpublished works in the domain of intelligent control, robotics, intelligent transportation systems, pattern recognition, medical diagnosis, decision systems, and optimization of complex problems.  

Dr. Danilo Pelusi
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 papers will be 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 1800 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.


  • fuzzy controllers
  • neuro-fuzzy systems
  • soft computing
  • fuzzy-evolutionary systems
  • pattern recognition
  • intelligent health systems
  • expert systems
  • intelligent agents
  • machine learning
  • intelligent transportation systems
  • data mining
  • intelligent optimization
  • decision systems

Published Papers (1 paper)

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Improving Facial Emotion Recognition Using Residual Autoencoder Coupled Affinity Based Overlapping Reduction
Mathematics 2022, 10(3), 406; https://doi.org/10.3390/math10030406 (registering DOI) - 27 Jan 2022
Emotion recognition using facial images has been a challenging task in computer vision. Recent advancements in deep learning has helped in achieving better results. Studies have pointed out that multiple facial expressions may present in facial images of a particular type of emotion. [...] Read more.
Emotion recognition using facial images has been a challenging task in computer vision. Recent advancements in deep learning has helped in achieving better results. Studies have pointed out that multiple facial expressions may present in facial images of a particular type of emotion. Thus, facial images of a category of emotion may have similarity to other categories of facial images, leading towards overlapping of classes in feature space. The problem of class overlapping has been studied primarily in the context of imbalanced classes. Few studies have considered imbalanced facial emotion recognition. However, to the authors’ best knowledge, no study has been found on the effects of overlapped classes on emotion recognition. Motivated by this, in the current study, an affinity-based overlap reduction technique (AFORET) has been proposed to deal with the overlapped class problem in facial emotion recognition. Firstly, a residual variational autoencoder (RVA) model has been used to transform the facial images to a latent vector form. Next, the proposed AFORET method has been applied on these overlapped latent vectors to reduce the overlapping between classes. The proposed method has been validated by training and testing various well known classifiers and comparing their performance in terms of a well known set of performance indicators. In addition, the proposed AFORET method is compared with already existing overlap reduction techniques, such as the OSM, ν-SVM, and NBU methods. Experimental results have shown that the proposed AFORET algorithm, when used with the RVA model, boosts classifier performance to a greater extent in predicting human emotion using facial images. Full article
(This article belongs to the Special Issue Fuzzy Logic in Artificial Intelligence Systems)
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