Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware

Machine Learning and Knowledge Extraction

MDPI AG · Switzerland · ISSN 2504-4990
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Machine Learning and Knowledge Extraction is a peer-reviewed english-language open-access journal published by MDPI AG in Switzerland, operating under an open-access model since 2019. The journal covers Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware.

Authors are required to pay an article processing charge to publish in Machine Learning and Knowledge Extraction; refer to the journal's APC page for the current rate. A waiver policy is offered for eligible authors. Articles are released under the CC BY license (see other journals using this license).

Keywords

data ecosystems, data fusion, data mapping, data visualisation

At a glance

Publisher
MDPI AG
Country
Switzerland
Primary language
English
License
CC BY
Peer review
Anonymous peer review
OA since
2019

ISSN

electronic
2504-4990

Frequently asked questions

Is Machine Learning and Knowledge Extraction peer-reviewed?
Yes — Machine Learning and Knowledge Extraction operates a Anonymous peer review review process.
Does Machine Learning and Knowledge Extraction charge an article processing charge (APC)?
Yes — refer to the journal's APC page for the current rate. A waiver policy is available for eligible authors.
What subjects does Machine Learning and Knowledge Extraction publish?
Machine Learning and Knowledge Extraction publishes research in Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware.
What languages does Machine Learning and Knowledge Extraction accept?
Machine Learning and Knowledge Extraction accepts manuscripts in English.
What is the ISSN of Machine Learning and Knowledge Extraction?
ISSN: 2504-4990 (electronic).
How do I submit a manuscript to Machine Learning and Knowledge Extraction?
Submit through the journal's official portal: visit submission page →

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