Special Issue "Fuzzy Systems for Data Managing in Business, Society, and Economics"

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Logic".

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 2411

Special Issue Editors

1. Computer Science Department, Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador
2. Human-IST Institute, University of Fribourg, Boulevard de Pérolles 90, Fribourg, Switzerland
Interests: eGovernment; eParticipation; eCollaboration; eDemocracy; eElection; eVoting; eCommunities; ePassports; recommender systems; fuzzy classification
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Dr. María Dolores Ruiz
E-Mail Website
Guest Editor
Department of Computer Science and Artificial Intelligence, Universidad de Granada, 18071 Granada, Spain
Interests: data mining; information retrieval; correlation statistical measures; fuzzy logic and fuzzy sets theory; sentence quantification and fuzzy quantification; information fusion; energy efficiency
Prof. Dr. Nadezhda Yarushkina
E-Mail Website
Guest Editor
Department of Information Systems, Ulyanovsk State Technical University, Ulyanovsk, Russia
Interests: fuzzy logic; fuzzy time series
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Department of Management Control and Information Systems, University of Chile, Av. Diagonal Paraguay 257, Santiago 8330015, Chile
Interests: information measures; intuitionistic fuzzy sets; aggregation operators; hesitant fuzzy sets; fuzzy decision making
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Collegues,

Today, the volume of data digitally stored has considerably increased in the different ambits of society and business. This kind of data often involves noise, uncertainty, and vagueness. In such domains, efficient handling is of extreme importance with such fuzziness of data, without neglecting other features that are important, like the volume of data to be processed, their complexity, heterogeneity, etc.

Over the last few decades, fuzzy systems (FS) have gained attention in different domains, including business, economics, social media, medicine, etc., as both a theoretical framework for the representation of fuzziness and a new solution for managing and solving a great myriad of problems. Conventional theories have serious limitations that handling with different types of fuzziness in collected data may contain. FS has established a new research direction as well as a wide variety of methodologies and approaches that help to analyze large volumes of data. FS can handle not only expert knowledge, offering a closer representation to human thinking, but also automatically extracted knowledge from different devices such as sensors or sources, e.g., text in social media, or time series in the economic field.

Fuzzy systems open the way for new developments and methodologies. For instance, in the ambit of data mining and machine learning, different intelligent analysis techniques have been developed, experiencing a considerable growth in recent years due to two key factors: (a) Knowledge hidden in organizations’ databases can be exploited to improve strategic and managerial decision-making in the current ultra-competitive markets; (b) the large volume of data managed by organizations makes it impossible to carry out an analysis process manually. Moreover, the volume of information also digitally stored in text format in open sources such as the web, including log files registering the use of information or social media content, has contributed to increasing the interest on text and web mining techniques. On one hand, these techniques aim to automatize the analysis process by introducing a variety of intelligent techniques to learn, optimize, and represent uncertain and imprecise knowledge. On the other hand, these tools offer the possibility to analyze massive data, offering more efficient algorithms and a suitable selection of obtained results in terms of their novelty, usefulness, and interpretability.

This Special Issue is dedicated to high-quality research works and solutions proposing original FS applications in business, society, and economy, addressing theoretical and/or practical problems employing a solid theory basis and/or empirical analysis.

Prof. Dr. Luis Terán
Dr. María Dolores Ruiz
Prof. Dr. Nadezhda Yarushkina
Dr. Rajkumar Verma
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. Axioms is an international peer-reviewed open access monthly 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 1600 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.


  • Imprecision, uncertainty, and vagueness management in FS
  • Data, text, web, and stream mining
  • Temporal data series in FS
  • Big data in FS
  • Data pre- and post-processing in FS
  • Parallel and distributed algorithms in FS
  • Information summarization and visualization in FS
  • Information measures in FS
  • Information aggregation methods in FS
  • Semantic models to represent input data and extracted knowledge in FS
  • Applications of FS in several ambits: security, economy, health, tourism, biological process, customer profiles, insurance, decision making, expert systems, finance, anomaly detection, medical diagnosis, emergency management, situation recognition, pattern recognition, etc.

Published Papers (1 paper)

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Discrete and Fuzzy Models of Time Series in the Tasks of Forecasting and Diagnostics
Axioms 2020, 9(2), 49; https://doi.org/10.3390/axioms9020049 - 30 Apr 2020
Cited by 4 | Viewed by 1695
The development of the economy and the transition to industry 4.0 creates new challenges for artificial intelligence methods. Such challenges include the processing of large volumes of data, the analysis of various dynamic indicators, the discovery of complex dependencies in the accumulated data, [...] Read more.
The development of the economy and the transition to industry 4.0 creates new challenges for artificial intelligence methods. Such challenges include the processing of large volumes of data, the analysis of various dynamic indicators, the discovery of complex dependencies in the accumulated data, and the forecasting of the state of processes. The main point of this study is the development of a set of analytical and prognostic methods. The methods described in this article based on fuzzy logic, statistic, and time series data mining, because data extracted from dynamic systems are initially incomplete and have a high degree of uncertainty. The ultimate goal of the study is to improve the quality of data analysis in industrial and economic systems. The advantages of the proposed methods are flexibility and orientation to the high interpretability of dynamic data. The high level of the interpretability and interoperability of dynamic data is achieved due to a combination of time series data mining and knowledge base engineering methods. The merging of a set of rules extracted from the time series and knowledge base rules allow for making a forecast in case of insufficiency of the length and nature of the time series. The proposed methods are also based on the summarization of the results of processes modeling for diagnosing technical systems, forecasting of the economic condition of enterprises, and approaches to the technological preparation of production in a multi-productive production program with the application of type 2 fuzzy sets for time series modeling. Intelligent systems based on the proposed methods demonstrate an increase in the quality and stability of their functioning. This article contains a set of experiments to approve this statement. Full article
(This article belongs to the Special Issue Fuzzy Systems for Data Managing in Business, Society, and Economics)
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