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Fuzzy Data Analytics: Current Trends and Future Perspectives

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

Deadline for manuscript submissions: 15 October 2026 | Viewed by 682

Editors


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Guest Editor
School of IT & Engineering (SITE), Melbourne Institute of Technology, Melbourne, VIC 3000, Australia
Interests: decision making; artificial intelligence; data mining; fuzzy optimization

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Guest Editor
Department of Management & Marketing, La Trobe University, Melbourne, VIC 3038, Australia
Interests: decision support and group support systems; decision theory; logistics and supply chain management; sustainability and environmental issues; optimisation; sustainable procurement; sustainable development

Special Issue Information

Dear Colleagues,

Fuzzy data analytics has become a foundational component in modelling, interpreting and managing complex, uncertain and imprecise information in modern intelligent systems. With the rapid expansion of data-centric technologies—including machine learning, IoT sensing, cyber–physical systems and autonomous decision environments—traditional crisp models are often insufficient for capturing vagueness, ambiguity and incomplete knowledge. Fuzzy set theory and its contemporary extensions offer powerful mechanisms for representing and analysing such uncertainty.

In this Special Issue, we are particularly interested in new theories, algorithms and applications that advance the field of fuzzy data analytics, as well as forward-looking perspectives that identify open challenges and emerging research opportunities. We welcome interdisciplinary contributions that explore the integration of fuzzy systems with artificial intelligence, optimisation, machine learning and real-world decision-support environments.

Topics of interest include, but are not limited to:

  • Advanced fuzzy set models: hesitant, intuitionistic, probabilistic, type-2 and higher-order fuzzy representations
  • Fuzzy similarity, entropy and knowledge measures for data-driven inference
  • Integration of fuzzy logic with machine learning, deep learning and explainable AI
  • Granular computing and fuzzy information processing in intelligent systems
  • Fuzzy multi-criteria decision making (MCDM) and decision-support frameworks
  • Fuzzy analytics for big data, IoT, sensor networks and uncertainty-aware environments
  • Applications in environmental modelling, wildfire risk assessment, transportation, health, finance and socio-technical systems
  • Future trends and emerging research directions in fuzzy data analytics and computational intelligence

We invite high-quality research articles, reviews, methodological developments and application-oriented studies that contribute to the evolving landscape of fuzzy data analytics.

Dr. Bahram Farhadinia
Dr. Ashkan Hafezalkotob
Guest Editors

Manuscript Submission Information

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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-anonymized 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.

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Keywords

  • fuzzy data analytics
  • fuzzy information measures
  • fuzzy multi-criteria decision making (MCDM)
  • granular computing
  • uncertainty modelling
  • fuzzy machine learning
  • explainable AI (XAI)
  • intelligent decision support systems

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Published Papers (1 paper)

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Research

23 pages, 469 KB  
Article
Entropy-Based Fuzzy Data Analytics for Time-Sequential Decision Making: A Case Study in Supply Chain Optimisation
by Bahram Farhadinia, Raza Nowrozy, Atefe Taghavi, Mansoureh Maadi and Savitri Bevinakoppa
Electronics 2026, 15(8), 1760; https://doi.org/10.3390/electronics15081760 - 21 Apr 2026
Viewed by 431
Abstract
Decision-making problems in complex environments are often characterised by uncertainty, vagueness, and dynamically evolving information. In such contexts, decision makers may express hesitant and fluctuating evaluations over time, which cannot be adequately captured by classical hesitant fuzzy frameworks. To address this limitation, time-sequential [...] Read more.
Decision-making problems in complex environments are often characterised by uncertainty, vagueness, and dynamically evolving information. In such contexts, decision makers may express hesitant and fluctuating evaluations over time, which cannot be adequately captured by classical hesitant fuzzy frameworks. To address this limitation, time-sequential hesitant fuzzy sets (TSHFSs) have been introduced as an effective tool for modelling temporal hesitancy. However, the development of information measures for TSHFSs, particularly entropy measures for quantifying uncertainty and deriving criteria weights, remains limited. In this paper, we propose a novel class of entropy measures for TSHFSs by constructing transformation mechanisms based on proximity-driven formulations derived from similarity structures. The proposed measures are developed using arithmetic and algebraic operators to capture the dispersion of information across time sequences, enabling a more refined representation of temporal uncertainty. These entropy measures are further integrated into a multi-criteria decision-making (MCDM) framework, where they are employed to determine criteria weights under incomplete information and combined with the TOPSIS method for ranking alternatives. The effectiveness of the proposed framework is validated through comparative analysis with existing TSHFS entropy measures and sensitivity analysis under varying decision conditions. The results demonstrate that the proposed measures maintain ranking consistency while providing improved discrimination and interpretability of alternatives. In particular, the framework effectively captures fluctuating hesitancy and enhances the robustness of decision outcomes in dynamic environments. The proposed approach contributes to the advancement of TSHFS-based decision analysis by offering a mathematically grounded and practically applicable entropy-driven framework for handling time-dependent uncertainty in complex decision-making problems. Full article
(This article belongs to the Special Issue Fuzzy Data Analytics: Current Trends and Future Perspectives)
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