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Applied Sciences
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Applications of Fuzzy Systems and Fuzzy Decision Making
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Applications of Fuzzy Systems and Fuzzy Decision Making

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 1385

Special Issue Editor

Prof. Dr. Diana Kalibatiene

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Guest Editor
Department of Information Systems, Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania
Interests: ontology based information systems; multi-criteria decision making methods application; fuzzy theory application
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the years, significant developments have been made in fuzzy systems. Fuzzy logic can be applied in areas such as fuzzy clustering in image processing, classification, regression, and decision making; fuzzy control to map expert knowledge to control systems; fuzzy modeling to combine expert knowledge; and fuzzy optimization to solve development problems.

An advanced fuzzy system is a flexible method of combining multiple conflicting, cooperative, and collaborative sets of knowledge. Combined with the features of artificial intelligence and decision-making systems, a number of studies have focused on the many applications of of fuzzy decision making. Those intelligent systems, together with other technologies, have opened up a new way of thinking, as well as new approaches to research, development, and application.

This Special Issue aims to present the latest results on advances in fuzzy sets, fuzzy systems, decision making, and related applications.

The main areas include, but are not limited to, intelligent systems, sustainable development, socio–cyber–physical systems, e-administration, environmental engineering, smart cities, healthcare, security, visualization, business process automation, manufacturing systems, logistics, telecommunication, infrastructure, and transportation.

Prof. Dr. Diana Kalibatiene
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. Applied Sciences 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 2400 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.

Published Papers (2 papers)

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Research

17 pages, 4183 KiB  
Article
Bayesian Linguistic Conditional System as an Attention Mechanism in a Failure Mode and Effect Analysis
by Roberto Baeza-Serrato
Appl. Sci. 2024, 14(3), 1126; https://doi.org/10.3390/app14031126 - 29 Jan 2024
Cited by 1 | Viewed by 511
Abstract
Fuzzy Inference System behavior can be described qualitatively using a natural language, which is known as the expert-driven approach to handling non-statistical uncertainty. Generally, practical applications involve conceptualizing the problem by integrating linguistic uncertainty and using data by integrating stochastic uncertainty. The proposed [...] Read more.
Fuzzy Inference System behavior can be described qualitatively using a natural language, which is known as the expert-driven approach to handling non-statistical uncertainty. Generally, practical applications involve conceptualizing the problem by integrating linguistic uncertainty and using data by integrating stochastic uncertainty. The proposed probabilistic fuzzy system uses the Gaussian Density Function (GDF) to assign a probability to input variables integrating stochastic uncertainty. In addition, a linguistic interpretation is used to project various categories of the GDF integrating linguistic uncertainty. Likewise, one of the relevant aspects of the proposal is to weigh each input variable according to the heuristic interpretation that determines the probability assigned to each of them a priori. Therefore, the main contribution of the research focuses on using the Bayesian Linguistic Conditional System (BLCS) as a mechanism of attention to relate the categories of the different input variables and find their posterior-weighted probability at a normalization stage. Finally, the knowledge base is established through linguistic rules, and the system’s output is a Bayesian classifier multiplying its normalized posterior conditional probabilities. The highest probability value of the knowledge base is identified, and the Risk Priority Number Weighted (RPNW) is determined using their respective posterior-normalized probabilities for each input variable. The results are expressed on a simple and precise scale from 1 to 10. They are compared with the Risk Priority Number (RPN), which results in a Failure Mode and Effect Analysis (FMEA). They show similar behaviors for multiple combinations in the evaluations while highlighting different scales. Full article
(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)
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31 pages, 7579 KiB  
Article
Selection of Optimal Segmentation Algorithm for Satellite Images by Intuitionistic Fuzzy PROMETHEE Method
by Edgaras Janusonis, Giruta Kazakeviciute-Januskeviciene and Romualdas Bausys
Appl. Sci. 2024, 14(2), 644; https://doi.org/10.3390/app14020644 - 12 Jan 2024
Viewed by 592
Abstract
The combination of MCDM and fuzzy sets offers new potential ways to solve the challenges posed by complex image contents, such as selecting the optimal segmentation algorithm or evaluating the segmentation quality based on various parameters. Since no single segmentation algorithm can achieve [...] Read more.
The combination of MCDM and fuzzy sets offers new potential ways to solve the challenges posed by complex image contents, such as selecting the optimal segmentation algorithm or evaluating the segmentation quality based on various parameters. Since no single segmentation algorithm can achieve the best results on satellite image datasets, it is essential to determine the most appropriate segmentation algorithm for each satellite image, the content of which can be characterized by relevant visual features. In this research, we proposed a set of visual criteria representing the fundamental aspects of satellite image segmentation. The segmentation algorithms chosen for testing were evaluated for their performance against each criterion. We introduced a new method to create a decision matrix for each image using fuzzy fusion, which combines the image content vector and the evaluation matrix of the studied segmentation algorithms. An extension of the Preference Ranking Organization Method Enrichment Evaluation (PROMETHEE) using intuitive fuzzy sets (IFSs) was applied to solve this problem. The results acquired by the proposed methodology were validated by comparing them with those obtained in expert ratings and by performing a sensitivity analysis. Full article
(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)
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