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Editorial for topical collections on emerging trends in artificial intelligence and machine learning

Kessentini, Y., Laga, H.ORCID: 0000-0002-4758-7510 and Tabia, H. (2022) Editorial for topical collections on emerging trends in artificial intelligence and machine learning. Neural Computing and Applications, 34 . Art. 14121.

Link to Published Version: https://doi.org/10.1007/s00521-022-07636-0
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Abstract

It gives us a great pleasure to introduce this special issue focused on the recent advances in various areas of pattern recognition, machine learning and artificial intelligence. We congratulate the authors who contributed successful submissions and thank the reviewers who worked hard on a tight timeframe.

As a result of the open call for papers, which was widely disseminated, we received 34 submissions which were judged to be within scope of the special issue. We encouraged contributions on any topic under the broad umbrella of NCAA. In addition, a selection of high-quality manuscripts presented at MedPRAI 2020 has been invited to submit an extended version of their work. Each submission was assigned to one of the guest editors, making sure that any potential conflict of interest is avoided. We then solicited reviews from experts in the field following the standard practices of the journal. Following a rigorous reviewing process, which extended to two or three rounds in some cases, we ultimately accepted 13 papers for publication in this special issue. These reflect both the range of the research in the field today and also the depth of the problems that are being studied.

We believe the research presented in this special issue will provide a valuable resource for those working in the field over the coming years. Once again, we thank everyone who contributed to the success of this special issue, both authors and reviewers. We also wish to thank journal staff members for their ongoing support and assistance.

Item Type: Journal Article
Murdoch Affiliation(s): IT, Media and Communications
Publisher: Springer London
URI: http://researchrepository.murdoch.edu.au/id/eprint/65625
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