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Entropy-Based Uncertainty Management Approaches for Analytics and Information Fusion (AIF)

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (25 September 2022) | Viewed by 426

Special Issue Editors


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Guest Editor
Institut Mines Télécom Atlantique (IMT-Atlantique), Brest, 44300 Nantes, France
Interests: analytics and information fusion, BigData, machine, and deep Learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Image & Information Processing Department (iTi), IMT-Atlantique, Technopôle Brest Iroise CS 83818, 29238 Brest, France
Interests: data sciences; information fusion; scene interpretation; signal and image processing; artificial intelligence

Special Issue Information

Dear Colleagues,

Uncertainty comes with our limited capacity or deployed means to perceive real-world situations. Managing and reducing uncertainty is an important, even crucial, issue of the so-called analytics and information fusion (AIF) processing chain to support situation awareness (SAW). The AIF chain involves the analysis, modeling, and processing of uncertain information. By modeling uncertain information, the essential characteristics of the given information can be extracted effectively. Uncertainty management is the key to uncertain information modeling, and many scholars have put forward various theories (such as evidence theory, fuzzy sets, possibility theory, rough sets, D number, Z number, and hybridization of these methods). Various uncertainty typologies (aleatory, epistemic, fuzziness, nonspecificity, discord, ambiguity, divergence, etc.) and measurement methods have been proposed, but there is still no full consensus, making this an open research issue. Shannon first extended the definition of entropy to the field of information theory, known as Shannon entropy. Shannon entropy is a powerful tool in the probability theory framework, but this concept needs to be rethought when using other frameworks such as possibility theory, evidence theory, and fuzzy sets. New interpretations of entropy are needed (e.g., Deng’s entropy). What would be the required properties and behaviors of uncertainty measures with respect to their representations along the AIF processing chain? How should they (their roles) be used to manage AIF processing?

Dr. Éloi Bossé
Prof. Dr. Basel Solaiman
Guest Editors

Manuscript Submission Information

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Keywords

  • evidence theory
  • uncertainty measure
  • Deng entropy
  • Shannon entropy
  • fuzzy entropy
  • ambiguity measure
  • divergence measure
  • possibilistic entropy
  • measures of fuzziness
  • belief interval
  • evidential distance
  • measures of similarity

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Published Papers

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