Selected Papers from CD-MAKE 2022–2023 and ARES 2022
A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990).
Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 158
Special Issue Editors
Interests: artificial intelligence; trustworthy AI; high risk AI; information security; cyber resilience; information security risk analysis
Special Issues, Collections and Topics in MDPI journals
Interests: fundamental and applied research on blockchain and distributed ledger technologies; security of production systems engineering
Interests: artificial intelligence (AI); machine learning (ML); explainable AI (xAI); causability; decision support systems; medical AI; health informatics
Special Issues, Collections and Topics in MDPI journals
Interests: digital forensics; privacy aware machine learning; trustworthy AI; blockchain and AI
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue will mainly consist of extended papers selected from the papers presented at the 6th and 7th International Cross Domain Conference for Machine Learning & Knowledge Extraction (CD-MAKE 2022 and 2023) as well as the 17th International Conference on Availability, Reliability and Security (ARES 2022). Please visit the conferences’ websites for detailed descriptions: https://www.ares-conference.eu/ and https://cd-make.net/.
Each submission to this Special Issue should contain at least 50% 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. Simon Tjoa
Prof. Dr. Edgar Weippl
Prof. Dr. Andreas Holzinger
Dr. Peter Kieseberg
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. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly 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.
