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Design, Development and Application of Fuzzy Systems
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Design, Development and Application of Fuzzy Systems

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

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 5744

Special Issue Editor

Prof. Dr. Diana Kalibatiene

E-Mail Website
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,

Since Dr. Lotfi Zadeh introduced a fuzzy system in 1965, its application for fuzzy inference has spread into multidiscipline areas such as engineering, management, medicine, etc. They are applied for fuzzy clustering in image processing, classification, regression, decision making, fuzzy control to map expert knowledge to control systems, fuzzy modelling to combine expert knowledge, fuzzy optimization to solve development problems, etc. One of the advantages of fuzzy systems is their capability to handle numeric and linguistic data in the same framework. Additionally, fuzzy systems provide a flexible method of combining multiple conflicting, cooperating, and collaborating knowledge.

Combining those characteristics of fuzzy systems with the features of artificial intelligence allows us to represent the knowledge of experts or acquired through the learning process into multiple composite smart systems. Those smart systems have opened up a new way of thinking, research, development, and application together with other technologies.

This special issue will address some evolving research about the design, development, and application of fuzzy systems. Thus, this special issue will highlight novel, practical, and high-quality research regarding the design, development and application of fuzzy systems. A special emphasis will be on methods and approaches of design, development and application of fuzzy systems, hybrid fuzzy systems, artificial intelligence and fuzzy systems.

Main application areas include, but are not limited to: business and finance, 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, etc.

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.

Keywords

  • methods and approaches of design, development and application of fuzzy systems
  • hybrid fuzzy systems
  • artificial intelligence and fuzzy systems
  • business and finance
  • 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, etc

Published Papers (3 papers)

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Research

17 pages, 16261 KiB  
Article
A Novel Classification Method Using the Takagi–Sugeno Model and a Type-2 Fuzzy Rule Induction Approach
by Martin Tabakov, Adrian B. Chlopowiec and Adam R. Chlopowiec
Appl. Sci. 2023, 13(9), 5279; https://doi.org/10.3390/app13095279 - 23 Apr 2023
Cited by 2 | Viewed by 1359
Abstract
The main purpose of this research was to introduce a classification method, which combines a rule induction procedure with the Takagi–Sugeno inference model. This proposal is a continuation of our previous research, in which a classification process based on interval type-2 fuzzy rule [...] Read more.
The main purpose of this research was to introduce a classification method, which combines a rule induction procedure with the Takagi–Sugeno inference model. This proposal is a continuation of our previous research, in which a classification process based on interval type-2 fuzzy rule induction was introduced. The research goal was to verify if the Mamdani fuzzy inference used in our previous research could be replaced with the first-order Takagi–Sugeno inference system. In the both cases to induce fuzzy rules, a new concept of a fuzzy information system was defined in order to deal with interval type-2 fuzzy sets. Additionally, the introduced rule induction assumes an optimization procedure concerning the footprint of uncertainty of the considered type-2 fuzzy sets. A key point in the concept proposed is the generalization of the fuzzy information systems’ attribute information to handle uncertainty, which occurs in real data. For experimental purposes, the classification method was tested on different classification benchmark data and very promising results were achieved. For the data sets: Breast Cancer Data, Breast Cancer Wisconsin, Data Banknote Authentication, HTRU 2 and Ionosphere, the following F-scores were achieved, respectively: 97.6%, 96%, 100%, 87.8%, and 89.4%. The results proved the possibility of applying the Takagi–Sugeno model in the classification concept. The model parameters were optimized using an evolutionary strategy. Full article
(This article belongs to the Special Issue Design, Development and Application of Fuzzy Systems)
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16 pages, 2551 KiB  
Article
Performance Appraisal of Urban Street-Lighting System: Drivers’ Opinion-Based Fuzzy Synthetic Evaluation
by Fawaz Alharbi, Meshal I. Almoshaogeh, Anwar H. Ibrahim, Husnain Haider, Abd Elaziz M. Elmadina and Ibrahim Alfallaj
Appl. Sci. 2023, 13(5), 3333; https://doi.org/10.3390/app13053333 - 6 Mar 2023
Cited by 4 | Viewed by 1712
Abstract
Saudi Arabian urban roads and highways have witnessed a large number of traffic crashes. Road lighting is one of the most important factors influencing drivers’ safety during the nighttime. Street-lighting design (e.g., spacing and height), visibility (e.g., lane marking and oncoming vehicles), and [...] Read more.
Saudi Arabian urban roads and highways have witnessed a large number of traffic crashes. Road lighting is one of the most important factors influencing drivers’ safety during the nighttime. Street-lighting design (e.g., spacing and height), visibility (e.g., lane marking and oncoming vehicles), and drivers’ satisfaction (e.g., glare effect on eyes and overall ambiance) are primary criteria affecting the performance of an urban street-lighting system (USLS). The present study presents a methodology for the performance appraisal of USLS in Qassim, Saudi Arabia. An online questionnaire survey was developed to obtain drivers’ opinions on nine sub-criteria (three under each primary criterion). The responses were translated into a five-scale subjective rating system from very low to very high. Fuzzy synthetic evaluation (FSE) effectively aggregated the statistically diverse (p-value < 0.001) responses obtained on the three primary criteria. The study found that electronic billboards’ positioning, oncoming vehicle lights, and poor lighting in the course of bad weather (mainly dust) are mainly affecting the performance of USLS in the view of road users. The performance levels ranged between “medium” and “high”, with no criteria or sub-criteria achieving a “very high” level, suggesting a need for upgrades, such as conversion to LED lights and smart lighting control systems. The proposed methodology benefits the transportation ministries to identify lacking components of USLSs in different regions of Saudi Arabia. The methodology provides the opportunity to include additional or site-specific factors for appraising the performance of USLS before (during planning and design) or after the implementation of improvement actions. Full article
(This article belongs to the Special Issue Design, Development and Application of Fuzzy Systems)
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20 pages, 2436 KiB  
Article
Applying Fuzzy Inference and Machine Learning Methods for Prediction with a Small Dataset: A Case Study for Predicting the Consequences of Oil Spills on a Ground Environment
by Anastasiya Burmakova and Diana Kalibatienė
Appl. Sci. 2022, 12(16), 8252; https://doi.org/10.3390/app12168252 - 18 Aug 2022
Cited by 3 | Viewed by 2019
Abstract
Applying machine learning (ML) and fuzzy inference systems (FIS) requires large datasets to obtain more accurate predictions. However, in the cases of oil spills on ground environments, only small datasets are available. Therefore, this research aims to assess the suitability of ML techniques [...] Read more.
Applying machine learning (ML) and fuzzy inference systems (FIS) requires large datasets to obtain more accurate predictions. However, in the cases of oil spills on ground environments, only small datasets are available. Therefore, this research aims to assess the suitability of ML techniques and FIS for the prediction of the consequences of oil spills on ground environments using small datasets. Consequently, we present a hybrid approach for assessing the suitability of ML (Linear Regression, Decision Trees, Support Vector Regression, Ensembles, and Gaussian Process Regression) and the adaptive neural fuzzy inference system (ANFIS) for predicting the consequences of oil spills with a small dataset. This paper proposes enlarging the initial small dataset of an oil spill on a ground environment by using the synthetic data generated by applying a mathematical model. ML techniques and ANFIS were tested with the same generated synthetic datasets to assess the proposed approach. The proposed ANFIS-based approach shows significant performance and sufficient efficiency for predicting the consequences of oil spills on ground environments with a smaller dataset than the applied ML techniques. The main finding of this paper indicates that FIS is suitable for prediction with a small dataset and provides sufficiently accurate prediction results. Full article
(This article belongs to the Special Issue Design, Development and Application of Fuzzy Systems)
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