Special Issue "Entropy for Machine Learning and Complex Systems Toward Regional Sustainable Development"
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".
Deadline for manuscript submissions: closed (31 July 2021).
Special Issue Editors
Interests: machine learning; sustainable development; entropy; decision making methods; operation management; sustainability
Special Issues and Collections in MDPI journals
Interests: multicriteria decision making; energy; sustainable development; machine learning, entropy, fuzzy sets theory; fuzzy multicriteria decision making; sustainability
Special Issues and Collections in MDPI journals
Interests: multi-criteria decision making problems; computational intelligence; sustainability Neuro-fuzzy systems; fuzzy; rough and intuitionistic fuzzy set theory; neutrosophic theory
Special Issues and Collections in MDPI journals
Interests: multi-criteria; fuzzy set; soft computing; renewable energy; sustainability; circular economy; technology assessment; HyperSoft sets
Special Issues and Collections in MDPI journals
Special Issue Information
Dear Colleagues,
In recent decades, a need has arisen for forecasting and predictive modeling to deliver real-time solutions to sustainable development problems by integrating the models from the rapidly developing fields of machine learning, complex systems, and entropy. Machine learning is an approach for data analysis, which constructs the analytical model by giving computer systems the ability to “learn.” Machine learning is based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. The concept of entropy originally developed from physics fields, but, it is clear that entropy is deeply related to machine learning and complex systems. Besides applications in machine learning, entropy is a general measure, commonly used for qualitative analysis of complex systems. In this regard, entropy is a powerful descriptive method, which presents an operational and theoretical framework to attain both qualitative and quantitative descriptions of the intrinsic properties of machine learning and complex systems theories. Therefore, to understand the importance of entropy concepts in machine learning and complex systems, in this Special Issue, we are interested in providing state‐of‐the‐art literature of entropy concepts and establishing a reliable connection between machine learning, complex systems, and the sustainable development context.
Dr. Abbas Mardani
Prof. Dr. Edmundas Kazimieras Zavadskas
Dr. Dragan Pamučar
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 papers will be 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 1800 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
- machine learning
- complex systems
- predictive modeling
- sustainable development
- forecasting
- decision making
- complex systems