Fuzzy Transformation and Its Application in Data and Image Analysis

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 623

Special Issue Editors


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Dipartimento di Architettura, Università degli Studi di Napoli Federico II, Via Toledo 402, 80134 Napoli, Italy
Interests: fuzzy sets and fuzzy relations; soft computing; fuzzy transform image processing theory; machine learning; data mining
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Architecture, University of Naples Federico II | UNINA, Napoli, Italy
Interests: GIS; fuzzy intelligent systems in data and spatial data analysis; fuzzy clustering in spatial analysis and hot spot analysis; fuzzy reasoning in GIS environments

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Guest Editor
Dipartimento di Architettura, Università degli Studi di Napoli Federico II, Via Toledo 402, 80134 Napoli, Italy
Interests: fuzzy relation equations; fixed point theory; geographical information systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to announce the launch of a Special Issue focusing on "Fuzzy Transformation and Its Application in Data and Image Analysis". With this Special Issue, we aim to offer contributing authors the opportunity to present their recent results on the application of fuzzy transform-based methods and techniques in image and data analysis, considering the rapid development of machine learning methods and big data analytics. Among the topics that this new Special Issue, dedicated to fuzzy transform applications in image and data analysis, will address, we consider the following non-exhaustive list:

  • Multidimensional and high-order fuzzy transform applied in data analysis;
  • Multidimensional and high-order fuzzy transform applied in image and video processing;
  • Machine learning hybrid models based on fuzzy transform;
  • Multidimensional fuzzy transform applied to time series analysis;
  • Fuzzy transform methods for regression analysis;
  • Knowledge extraction models based on fuzzy transform;
  • Fuzzy transform methods applied for big data analysis;
  • Fuzzy transform applied in high resolution image processing;
  • High-order fuzzy transform for massive data mining;
  • Hybrid fuzzy transform-based machine and deep learning methods..

Moreover, this Special Issue is open to the discussion of new ideas, in addition to the aforementioned topics.

If this initiative suits your interests, please submit your contributions to be included in this Special Issue.

Prof. Dr. Ferdinando Di Martino
Dr. Barbara Cardone
Prof. Dr. Salvatore Sessa
Guest Editors

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Keywords

  • direct and inverse fuzzy transform
  • high order fuzzy transform
  • multidimensional fuzzy transform
  • fuzzy transform in data analysis
  • fuzzy transform in image and video processing

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Published Papers (1 paper)

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Research

16 pages, 2400 KiB  
Article
Multidimensional Fuzzy Transforms with Inverse Distance Weighted Interpolation for Data Regression
by Barbara Cardone and Ferdinando Di Martino
Electronics 2025, 14(6), 1199; https://doi.org/10.3390/electronics14061199 - 18 Mar 2025
Viewed by 257
Abstract
The main limitation of the Multidimensional Fuzzy Transform algorithm applied in regression analysis is that it cannot be used if the data are not dense enough concerning the fuzzy partitions; in these cases, less refined fuzzy partitions must be used, to the detriment [...] Read more.
The main limitation of the Multidimensional Fuzzy Transform algorithm applied in regression analysis is that it cannot be used if the data are not dense enough concerning the fuzzy partitions; in these cases, less refined fuzzy partitions must be used, to the detriment of the accuracy of the results. In this study, a variation of the Multidimensional Fuzzy Transform regression algorithm is proposed, in which the inverse distance weighted interpolation method is applied as a data augmentation algorithm to satisfy the criterion of sufficient data density concerning the fuzzy partitions. A preprocessing phase determines the optimal values of the parameters to be set in the algorithm’s execution. Comparative tests with other well-known regression methods are performed on five regression datasets extracted from the UCI Machine Learning Repository. The results show that the proposed method provides the best performance in terms of reductions in regression errors. Full article
(This article belongs to the Special Issue Fuzzy Transformation and Its Application in Data and Image Analysis)
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