Machine Learning and Knowledge Extraction

Machine Learning and Knowledge Extraction is an international, scientific, peer-reviewed, open access journal. It publishes original research articles, reviews, tutorials, research ideas, short notes and Special Issues that focus on machine learning and applications. Please see our video on YouTube explaining the MAKE journal concept. The journal is published quarterly online by MDPI.
  • Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
  • High Visibility: indexed within ESCI (Web of Science)dblp, and many other databases.
  • Rapid Publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 18.2 days after submission; acceptance to publication is undertaken in 4.6 days (median values for papers published in this journal in the first half of 2021).
  • Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
  • MAKE is a companion journal of Entropy.
Add your e-mail address to receive forthcoming issues of this journal:
Topic in Applied Sciences, AI, BDCC, MCA, MAKE
Applied Computer Vision and Pattern Recognition Editors-in-Chief: Antonio Fernández-Caballero, Byung-Gyu Kim, Hugo Pedro Proença
Deadline: 31 March 2022
Special Issue in MAKE
Interpretable and Annotation-Efficient Learning for Medical Image Computing Guest Editors: Jaime Cardoso, Nicholas Heller, Pedro Henriques Abreu, Ivana Išgum, Diana Mateus
Deadline: 31 December 2021
Special Issue in MAKE
Language Processing and Knowledge Extraction Guest Editors: Alberto Simões, Pedro Rangel Henriques
Deadline: 31 January 2022
Special Issue in MAKE
Advances in Explainable Artificial Intelligence (XAI) Guest Editor: Luca Longo
Deadline: 28 February 2022
Special Issue in MAKE
Advances in Reinforcement Learning Guest Editor: Ausif Mahmood
Deadline: 15 March 2022
Back to TopTop