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Thu, 16 Aug 2018 14:25:51 +1000
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We apology for possible cross posting, and thank you for your attension and
re-distribution.
---
CALL FOR PAPERS
https://www.comsoc.org/commag/cfp/data-science-and-artificial-intelligence-communications


The objective of the Data Science and Artificial Intelligence for
Communications Series of the IEEE Communications Magazine is to provide a
forum across industry and academia to
advance the development of network and system solutions using data science
and artificial intelligence.

Innovations in artificial intelligence, machine learning, reinforcement
learning and network data analytics introduce new opportunities in various
areas, such as channel modeling
and estimation, cognitive communications, interference alignment, mobility
management, resource allocation, network control and management, network
tomography, multi-agent systems,
prioritization of network ultra-broadband deployments. These new analytic
platforms will help revolutionize our networks and user experience. Through
gathering, processing, learning
and controlling the vast amounts of information in an intelligent manner
future networks will enable unprecedented automation and optimization.

This Series solicits articles addressing numerous topics within its scope
including, but not limited to, the following:

• All aspects of artificial intelligence, machine learning, reinforcement
learning and data analytics aiming at enabling and enhancing next
generation networks. The scope of issues
        that can be addressed includes both conventional measures such as
traffic management, QoE, service quality, as well as future network
behavior through intelligent services and applications.
• Methods, systems and infrastructure for the analysis of network, service
traffic and user behavior for efficient and reliable design of networks,
including deep learning and
statistical methods for network tomography.
• Predictive analytics and artificial intelligence for network
optimization, network security, network assurance, and data privacy and
integrity. Diagnosis of network failures using analytics and AI.
• Automated communication infrastructure among smart machines and agents
(including humans, e.g. speech and vision), and information fusion for
automation and enablement of multi-agent systems.
• Communication and networking to facilitate smart data-centric applications

SUBMISSION GUIDELINES

Manuscripts must be submitted through the magazine’s submissions Website at
http://mc.manuscriptcentral.com/commag-ieee. You will need to register and
then proceed to the author center.
On the manuscript details page, please select Data Science and Artificial
Intelligence for Communications Series from the drop-down menu.
Manuscripts should be tutorial in nature and
should not be under review for any other conference or journal.  They
should be written in a style comprehensible and accessible to readers
outside the specialty of the article.
Mathematical equations should not be used. For detailed submission
guidelines please refer to the magazine website for the list of guidelines
that must be followed by all submissions
to the IEEE Communications Magazine:
https://www.comsoc.org/commag/paper-submission-guidelines

Authors are encouraged to contact the Series Editor before submitting an
article in order to ensure that the article will be appropriate for the
Series. Papers can be submitted anytime
during the year. They will receive a review process, and, if accepted, they
will be published in the first slot available for this Series.

SERIES EDITORS
Irena Atov, Microsoft, USA
[log in to unmask]

Kwang-Cheng Chen, University of South Florida, USA
[log in to unmask]

Shui Yu, University of Technology Sydney, Australia
[log in to unmask]


-- 

-----------------------------

Shui YU, PhD,  Professor

Editor for

IEEE Communications Surveys and Tutorials,

IEEE Transactions on Computational Social Systems,

IEEE Comm Letters, IEEE Access,

IEEE Internet of Things Journal, IEEE Communication Magazine,

Elsevier Journal of Network and Computer Applications.

School of Software, University of Technology Sydney,

Sydney,  Australia.

http://www.nsp.org.au/syu <http://www.deakin.edu.au/~syu>/

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