Submission Deadline: 15 February 2020
IEEE Access invites manuscript submissions in the area of Machine Learning Designs, Implementations and Techniques.
Most modern machine learning research is devoted to improving the accuracy of prediction. However, less attention is paid to deployment of machine and deep learning systems, supervised /unsupervised techniques for mining healthcare data, and time series similarity and irregular temporal data analysis. Most deployments are in the cloud, with abundant and scalable resources, and a free choice of computation platform. However, with the advent of intelligent physical devices—such as intelligent robots or self-driven cars—the resources are more limited, and the latency may be strictly bounded.
To address these questions, the focus of this Special Section in IEEE Access is on machine and deep learning designs, implementations and techniques, including both system level topics and other research questions related to the general use and framework of machine learning algorithms.
The topics of interest include, but are not limited to:
We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.
Associate Editor: Shadi A. Aljawarneh, Jordan University of Science and Technology, Jordan
Relevant IEEE Access Special Sections:
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
Article submission: Contact Associate Editor and submit manuscript to: