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Electronics
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Fuzzy-Logic-Based Approaches for Knowledge Management and User Modelling
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Fuzzy-Logic-Based Approaches for Knowledge Management and User Modelling

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 April 2025) | Viewed by 3822

Special Issue Editors

Dr. Konstantina Chrysafiadi

E-Mail Website
Guest Editor
Department of Informatics, University of Piraeus, M. Karaoli & A. Dimitriou St. 80, 18534 Piraeus, Greece
Interests: artificial intelligence; fuzzy-logic-based systems; knowledge systems; adaptive systems; user modeling and distance learning
Special Issues, Collections and Topics in MDPI journals
Dr. Dionisios Sotiropoulos

E-Mail Website
Guest Editor
Department of Informatics, University of Piraeus, Karaoli & Dimitriou 80, 18534 Piraeus, Greece
Interests: machine learning; data mining; evolutionary computing; signal processing; digital social networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fuzzy logic is a computational intelligence method that was introduced by Lotfi A. Zadeh. It allows for the representation of vague and imprecise data with linguistic terms, making it an ideal approach for handling the uncertainty and vagueness that exist in both everyday scenarios and diverse scientific areas. Incorporating fuzzy logic into systems enables them to reason in a human-like way. In recent years, the increased interest in artificial intelligence and in developing smart systems that imitate human thinking and behavior have prompted the extensive use of fuzzy logic.

This Special Issue aims to explore the latest advancements and applications of fuzzy-logic in knowledge management and user modeling. It seeks to advance the understanding and application of fuzzy logic in these domains, shedding light on its potential to address challenges such as information overload, data ambiguity, and user behavior understanding and prediction. Particularly, this Special issue aims to inspire further research in innovative methodologies for tackling the complexities inherent in managing knowledge resources, and for handling and capturing the nuanced and subjective nature of user preferences, interests, and interactions. It delves into various applications of fuzzy logic in knowledge management, showcasing how it enables more nuanced reasoning and decision-making processes in the face of incomplete or ambiguous information, and into the diverse applications of fuzzy logic in user modeling, highlighting its role in personalization, recommendation systems, and adaptive interfaces. Therefore, this Special Issue seeks to provide insights into the current innovations, state-of-the-art methodologies, challenges, and future directions in the fields of knowledge management and the development more responsive, adaptable and context-aware user models. 

The topics of interest include (but are not limited to) the following:

  • Fuzzy logic in expert systems
  • Fuzzy logic-based knowledge representation models
  • Fuzzy systems for information retrieval and recommendation
  • Fuzzy logic in decision support systems
  • Fuzzy clustering and classification techniques
  • Fuzzy-logic-based semantic web and ontology engineering
  • Integration of fuzzy logic with other AI techniques for enhanced knowledge management
  • Fuzzy-logic-based personalized recommendation systems
  • Fuzzy-logic-based user modelling
  • Fuzzy-logic-based adaptive user interfaces
  • Fuzzy-logic-based user behavior prediction
  • Fuzzy-logic-based emotion recognition in user modelling

Dr. Konstantina Chrysafiadi
Dr. Dionisios Sotiropoulos
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 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. Electronics 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

  • fuzzy logic
  • fuzzy inference systems
  • fuzzy-rule-based systems
  • expert systems
  • artificial intelligence
  • granular computing
  • fuzzy rough sets
  • fuzzy clustering
  • fuzzy neural networks
  • fuzzy deep neural networks
  • explainable AI based on fuzzy logic
  • fuzzy logic-based knowledge management
  • fuzzy logic-based decision support systems
  • fuzzy logic-based adaptive systems
  • fuzzy logic-based recommendation systems
  • fuzzy logic-based user modelling
  • fuzzy logic-based behavior prediction

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Published Papers (4 papers)

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19 pages, 4715 KiB  
Article
Fuzzy Battery Manager: Charging and Balancing Rechargeable Battery Cells with Fuzzy Logic
by Adnan K. Shaout and Zachary Brauchler
Electronics 2025, 14(7), 1470; https://doi.org/10.3390/electronics14071470 - 6 Apr 2025
Viewed by 267
Abstract
This paper presents the design, implementation, and testing of a fuzzy battery manager featuring a novel hardware design. The system uses a fuzzy inference system to charge and balance two battery cells in series, integrating a microcontroller and a battery charging IC to [...] Read more.
This paper presents the design, implementation, and testing of a fuzzy battery manager featuring a novel hardware design. The system uses a fuzzy inference system to charge and balance two battery cells in series, integrating a microcontroller and a battery charging IC to demonstrate battery management with real hardware. It supports two battery chemistries, showcasing how the fuzzy system can be flexibly adapted to different rechargeable battery technologies. The fuzzy battery manager successfully achieves its goal of charging and balancing cells with high adaptability by simply adjusting membership functions. Its stability and effectiveness on real hardware have been confirmed. This adaptability offers significant potential across various industries. For example, a replacement battery pack designed for longevity using LiFePO4 cells could serve as an alternative to Li-Ion cells in electric vehicles, especially since LiFePO4 cells endure many more charge cycles, albeit with lower charge densities. The required membership functions for this replacement battery could be stored in just a few bytes of ROM within the battery pack, enabling seamless integration and use with existing vehicles and charging systems. Full article
(This article belongs to the Special Issue Fuzzy-Logic-Based Approaches for Knowledge Management and User Modelling)
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18 pages, 2560 KiB  
Article
Fuzzy Logic-Based Dynamic Difficulty Adjustment for Adaptive Game Environments
by Panagiotis D. Paraschos and Dimitrios E. Koulouriotis
Electronics 2025, 14(1), 146; https://doi.org/10.3390/electronics14010146 - 2 Jan 2025
Viewed by 1255
Abstract
Game players frequently seek video games that offer great replayability and unpredictable challenges, aiming to minimize anxiety and maximize fun. One of the most suitable game genres for achieving these objectives is the “shoot ’em up” genre, although it is mainly focused on [...] Read more.
Game players frequently seek video games that offer great replayability and unpredictable challenges, aiming to minimize anxiety and maximize fun. One of the most suitable game genres for achieving these objectives is the “shoot ’em up” genre, although it is mainly focused on experienced players. Given the latter, challenges should be balanced and adapted to player characteristics and style in an effort to strengthen their engagement with the game. In the present study, a fuzzy logic-based dynamic difficulty adjustment approach is proposed for dynamically tailoring challenges according to players’ behavior and strategies. The testbed of this approach is a shoot ’em up game where the players are faced with a challenging and adaptable opponent. The functionality of the proposed system is examined through game scenarios. The evaluation illustrates that fuzzy logic-based dynamic difficulty adjustment provides balanced challenges to players by easily defining the behavior of the game environment and opponents through a set of rules. Full article
(This article belongs to the Special Issue Fuzzy-Logic-Based Approaches for Knowledge Management and User Modelling)
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27 pages, 1921 KiB  
Article
A Fuzzy Decision Support System for Real Estate Valuations
by Francisco-Javier Gutiérrez-García, Silvia Alayón-Miranda and Pedro Pérez-Díaz
Electronics 2024, 13(24), 5046; https://doi.org/10.3390/electronics13245046 - 22 Dec 2024
Viewed by 637
Abstract
The field of real estate valuations is multivariate in nature. Each property has different intrinsic attributes that have a bearing on its final value: location, use, purpose, access, the services available to it, etc. The appraiser analyzes all these factors and the current [...] Read more.
The field of real estate valuations is multivariate in nature. Each property has different intrinsic attributes that have a bearing on its final value: location, use, purpose, access, the services available to it, etc. The appraiser analyzes all these factors and the current status of other similar properties on the market (comparable assets or units of comparison) subjectively, with no applicable rules or metrics, to obtain the value of the property in question. To model this context of subjectivity, this paper proposes the use of a fuzzy system. The inputs to the fuzzy system designed are the variables considered by the appraiser, and the output is the adjustment coefficient to be applied to the price of each comparable asset to obtain the price of the property to be appraised. To design this model, data have been extracted from actual appraisals conducted by three professional appraisers in the urban center of Santa Cruz de Tenerife (Canary Islands, Spain). The fuzzy system is a decision-helping tool in the real estate sector: appraisers can use it to select the most suitable comparables and to automatically obtain the adjustment coefficients, freeing them from the arduous task of calculating them manually based on the multiple parameters to consider. Finally, an evaluation is presented that demonstrates its applicability. Full article
(This article belongs to the Special Issue Fuzzy-Logic-Based Approaches for Knowledge Management and User Modelling)
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23 pages, 1948 KiB  
Article
PerFuSIT: Personalized Fuzzy Logic Strategies for Intelligent Tutoring of Programming
by Konstantina Chrysafiadi and Maria Virvou
Electronics 2024, 13(23), 4827; https://doi.org/10.3390/electronics13234827 - 6 Dec 2024
Cited by 1 | Viewed by 907
Abstract
Recent advancements in intelligent tutoring systems (ITS) driven by artificial intelligence (AI) have attracted substantial research interest, particularly in the domain of computer programming education. Given the diversity in learners’ backgrounds, cognitive abilities, and learning paces, the development of personalized tutoring strategies to [...] Read more.
Recent advancements in intelligent tutoring systems (ITS) driven by artificial intelligence (AI) have attracted substantial research interest, particularly in the domain of computer programming education. Given the diversity in learners’ backgrounds, cognitive abilities, and learning paces, the development of personalized tutoring strategies to support the effective attainment of learning objectives has become a critical challenge. This paper introduces personalized fuzzy logic strategies for intelligent programming tutoring (PerFuSIT), an innovative fuzzy logic-based module designed to select the most appropriate tutoring strategy from five available options, based on individual learner characteristics. The available strategies include revisiting previous content, progressing to the next topic, providing supplementary materials, assigning additional exercises, or advising the learner to take a break. PerFuSIT’s decision-making process incorporates a range of learner-specific parameters, such as performance metrics, error types, indicators of carelessness, frequency of help requests, and the time required to complete tasks. Embedded within the traditional ITS framework, PerFuSIT introduces a sophisticated reasoning mechanism for dynamically determining the optimal instructional approach. Experimental evaluations demonstrate that PerFuSIT significantly enhances learner performance and improves the overall efficacy of interactions with the ITS. The findings highlight the potential of fuzzy logic to optimize adaptive tutoring strategies by customizing instruction to individual learners’ strengths and weaknesses, thereby providing more effective and personalized educational support in programming instruction. Full article
(This article belongs to the Special Issue Fuzzy-Logic-Based Approaches for Knowledge Management and User Modelling)
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