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AI Data Science 2023 : Artificial Intelligence and Data Science for Communications

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Link: https://www.comsoc.org/publications/magazines/ieee-communications-magazine/cfp/artificial-intelligence-and-data-science
 
When Jan 1, 2022 - Jan 1, 2024
Where IEEE Communications Magazine
Submission Deadline TBD
Categories    artificial intelligence   machine learning   reinforcement learning   smart machines
 

Call For Papers

IEEE Communications Magazine is a hybrid open access periodical. IEEE has recently signed an agreement with academic institutions in your country offering the possibility for authors affiliated to these institutions to publish in open access periodicals at no additional cost (more details at Institutional OA Agreements - IEEE Open).

https://open.ieee.org/for-institutions/institutional-partners/

IEEE Communications Magazine (ComMag) is a flagship publication of the IEEE Communications Society and the world’s most recognized magazine in Telecommunications with a top-ranking Impact Factor.

ComMag serves its broad readership by publishing highest-quality, accessible and tutorial papers in three main tracks:
1) regularly scheduled Series addressing selected areas in the telecommunications field,
2) individually from open call on an ongoing basis, and
3) as part of very selective Feature Topics (FTs) which focus on emerging trends and hot subjects.

We would like to invite you to submit your manuscript to
IEEE Communications Magazine Series on
ARTIFICIAL INTELLIGENCE AND DATA SCIENCE FOR COMMUNICATIONS

https://www.comsoc.org/publications/magazines/ieee-communications-magazine/cfp/artificial-intelligence-and-data-science

Please use this link to submit your new manuscript to the Artificial Intelligence and Data Science for Communications Series:

https://ieee.atyponrex.com/journal/COMMAG-IEEE


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

Advances of the Internet, mobile and fixed communications, and computing have opened new frontiers for tomorrow’s data-centric society. New applications are increasingly depending on machine-to-machine communications, in turn creating untraditional workloads and demanding more efficient and reliable infrastructures. Such immensely diverse traffic workloads and applications will require dynamic and highly adaptive network environments that are capable of self-optimization for the task at hand while guaranteeing high reliability and ultra-low latency.

Networking devices, sensors, agents, meters, smart vehicles/systems generate tremendous amounts of data while requiring new levels of security, performance, and reliability. Such complexities demand new tools and methodologies for effective services, management, and operation. Predictive network analytics will have an important role in insight generation, process automation required for adapting and scaling to new demands, resolving issues before they impact operational performance (e.g. prevent network failures, anticipate capacity requirements), and overall decision making throughout the network. Data mining and analytic tools for inferring quality of experience (QoE) signals are needed to improve user experience and service quality.

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 should conform to the standard format as indicated in the Manuscript Submission Guidelines in the IEEE Communications Magazine website. Please, check these guidelines carefully before submitting since submissions not complying with them will be administratively rejected without review.

All manuscripts to be considered for publication must be submitted through the Author Portal. Select "Series Artificial Intelligence and Data Science for Communications" from the dropdown menu.

Kindly note that we are now accepting all submissions through our Author Portal: https://ieee.atyponrex.com/journal/COMMAG-IEEE

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.

Sincerely,
the Artificial Intelligence and Data Science for Communications Series Editors

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