Special Issue "Real-Time Machine Learning"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editors

Dr. Erik Linstead
Website
Guest Editor
Fowler School of Engineering; Electrical Engineering and Computer Science, Chapman University, Orange, CA 92866, USA
Interests: machine learning; computer vision; software engineering; medical informatics
Dr. Elizabeth Stevens
Website
Guest Editor
Fowler School of Engineering; Electrical Engineering and Computer Science, Chapman University, Orange, CA 92866, USA
Interests: data science; machine learning; medical informatics

Special Issue Information

Dear Colleagues,

In recent years, interest in machine learning and its applications has grown exponentially, with impacts from this field transcending disciplinary boundaries in both academia and industry. Despite advances in algorithms, software, and hardware, significant hurdles to deploying machine learning pipelines in real-time, embedded environments still exist. This is due in large part to constraints such as power consumption, cooling, processing capability, and requirements for determinism that can be more easily addressed in enterprise computing environments. The purpose of this Special Issue is to present original work that provides insight into how machine learning is most effectively integrated into resource-constrained computing architectures. We solicit topics from all areas of real-time machine learning, including, but not limited to, training and deployment of machine learning models on real-time systems, modeling energy efficiency of machine learning algorithms, hardware-based machine learning models, real-time software and hardware architectures for machine learning, and novel applications of machine learning designed for embedded, real-time environments.

Dr. Erik Linstead
Dr. Elizabeth Stevens
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 papers will be 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. Electronics 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 1500 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

  • real-time machine learning
  • machine learning hardware architectures
  • embedded machine learning applications
  • embedded machine learning algorithms
  • energy efficient machine learning
  • resource-constrained machine learning

Published Papers

This special issue is now open for submission.
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