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Selected Papers from CD-MAKE 2020 and ARES 2020

Special Issue Information

Dear Colleagues,

This Special Issue will mainly consist of extended papers selected from those presented at the 4th International Cross Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE 2020) as well as the 15th International Conference on Availability, Reliability and Security (ARES 2020). Please visit the conference websites for a detailed description: https://www.ares-conference.eu/ and https://cd-make.net/

Each submission to this Special Issue should contain at least 50% of new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases and a change of title, abstract and keywords. These extended submissions will undergo a peer-review process according to the journal’s rules of action. At least two technical committees will act as reviewers for each extended article submitted to this Special Issue; if needed, additional external reviewers will be invited to guarantee a high-quality reviewing process.

Prof. Dr. Edgar Weippl
Mr. Peter Kieseberg
Prof. Dr. Andreas Holzinger
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 250 words) can be sent to the Editorial Office for assessment.

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. Machine Learning and Knowledge Extraction 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

  • DATA – data fusion, preprocessing, mapping, knowledge representation, environments, etc.
  • LEARNING – algorithms, contextual adaptation, causal reasoning, transfer learning, etc.
  • VISUALIZATION – intelligent interfaces, human-AI interaction, dialogue systems, explanation interfaces, etc.
  • PRIVACY – data protection, safety, security, reliability, verifiability, trust, ethics and social issues, etc.
  • NETWORK – graphical models, graph-based machine learning, Bayesian inference, etc.
  • TOPOLOGY – geometrical machine learning, topological and manifold learning, etc.
  • ENTROPY – time and machine learning, entropy-based learning, etc.

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Special Issue Policies

Published Papers

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Mach. Learn. Knowl. Extr. - ISSN 2504-4990
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