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Entropy for Data-Driven Decision-Making Problems

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 2143

Special Issue Editors


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Research Institute of Sustainable Construction, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania
Interests: operations research; optimization and decision analysis; multicriteria decision making; multiattribute decision making (MADM); decision support systems; civil engineering; energy; sustainable development; fuzzy sets theory; fuzzy multicriteria decision making; sustainability; management; game theory and economical computing knowledge management
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Department of Economics, University of Molise, Via De Sanctis, 86100 Campobasso, Italy
Interests: multi-criteria; fuzzy set; soft computing; renewable energy; sustainability; circular economy; technology assessment; hypersoft sets; sustainable development goals
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few years, a need for data-driven decision-making modeling has arisen to deliver real-time solutions to problems by integrating models from the rapidly developing fields of machine learning, deep learning, and entropy. Machine learning is an approach for data analysis that constructs the analytical model by giving computer systems the ability to “learn.” Machine learning and deep learning models are based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. The concept of entropy was originally developed in the field of physics, but it is clear that entropy is deeply related to machine learning and deep learning. Furthermore, besides applications in machine learning, entropy is a general measure commonly used for the qualitative analysis of complex systems. In this regard, entropy is a powerful descriptive method that presents an operational and theoretical framework to attain both qualitative and quantitative descriptions of the intrinsic properties of machine learning and deep learning theories. Therefore, to understand the importance of entropy concepts in data-driven decision-making problems using machine learning and deep learning, in this Special Issue, we are interested in providing state‐of‐the‐art literature on entropy concepts and establishing a reliable connection between data-driven decision-making problems using machine learning and deep learning contexts.

Dr. Abbas Mardani
Prof. Dr. Edmundas Kazimieras Zavadskas
Prof. Dr. Fausto Cavallaro
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. Entropy 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 2600 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

  • entropy
  • data-driven decision-making
  • machine learning
  • deep learning
  • predictive modeling
  • decision making
  • complex systems

Published Papers (1 paper)

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Research

25 pages, 2604 KiB  
Article
Air Combat Intention Recognition with Incomplete Information Based on Decision Tree and GRU Network
by Jingyang Xia, Mengqi Chen and Weiguo Fang
Entropy 2023, 25(4), 671; https://doi.org/10.3390/e25040671 - 17 Apr 2023
Cited by 4 | Viewed by 1523
Abstract
Battlefield information is generally incomplete, uncertain, or deceptive. To realize enemy intention recognition in an uncertain and incomplete air combat information environment, a novel intention recognition method is proposed. After repairing the missing state data of an enemy fighter, the gated recurrent unit [...] Read more.
Battlefield information is generally incomplete, uncertain, or deceptive. To realize enemy intention recognition in an uncertain and incomplete air combat information environment, a novel intention recognition method is proposed. After repairing the missing state data of an enemy fighter, the gated recurrent unit (GRU) network, supplemented by the highest frequency method (HFM), is used to predict the future state of enemy fighter. An intention decision tree is constructed to extract the intention classification rules from the incomplete a priori knowledge, where the decision support degree of attributes is introduced to determine the node-splitting sequence according to the information entropy of partitioning (IEP). Subsequently, the enemy fighter intention is recognized based on the established intention decision tree and the predicted state data. Furthermore, a target maneuver tendency function is proposed to screen out the possible deceptive attack intention. The one-to-one air combat simulation shows that the proposed method has advantages in both accuracy and efficiency of state prediction and intention recognition, and is suitable for enemy fighter intention recognition in small air combat situations. Full article
(This article belongs to the Special Issue Entropy for Data-Driven Decision-Making Problems)
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