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Emotional AI and its Applications in Communications Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 4010

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, Alfaisal University, Riyadh, Saudi Arabia
Interests: wireless networks; Internet of Things; cyber-physical systems; affective sensing

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Guest Editor
Faculty of Information Technology; United Arab Emirates University, Al Ain, United Arab Emirates
Interests: IoT; IoNT; radio resource management; network modeling

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Guest Editor
Department of Computer Science, Ryerson University, Toronto M5B 2K3, Canada
Interests: blockchain; Internet of Things; network security; wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Emotional AI (EAI) continues to demonstrate increasing utility in various applications. In the context of communications networks, EAI is showing great potential in automating quality of experience (QoE) measurements and enriching mixed-reality communications.

The aim of this Special Issue is to capture the state of EAI in the context of communication networks. This spans research and commercial efforts as well as standardization. It also includes emotional sensing setups, processing, and representation.

Topics of interest include but are not limited to the following:

  • Affect discretization and modeling
  • Affect emulation
  • Affect synthesis
  • Affective sensing for crowds
  • Applications of affective sensing in
  • AR/VR communications
  • Connected vehicles
  • Crowd control and management
  • Education
  • eHealth
  • Network management
  • Smart cities
  • AI/ML applications in affective sensing
  • Architectures for affective sensing
  • Fusion in multi-modal sensing techniques
  • Generating affective sensing datasets
  • Qualifying affective sensing solutions

Some of the submissions published in this Special Issue will be extended versions of works presented at the workshop on Applications of Affective Sensing in Communication Networks (AffectiCom’21, https://www.affecticom.net/), which is co-located with the IEEE International Conference on Communications (ICC, https://icc2021.ieee-icc.org/), Montreal, Canada held on June 14-18, 2021. Papers will also be accepted through open call.

Dr. Abd-Elhamid M. Taha
Prof. Najah Abu Ali
Prof. Vojislav B Mišić
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 submissions that pass pre-check are 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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • emotional sensing
  • emotional artificial intelligence
  • affective sensing
  • quality of experience
  • network management

Published Papers (1 paper)

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Research

21 pages, 10628 KiB  
Article
User Experience Estimation in Multi-Service Scenario of Cellular Network
by Kaisa Zhang, Gang Chuai, Saidiwaerdi Maimaiti and Qian Liu
Sensors 2022, 22(1), 89; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010089 - 23 Dec 2021
Cited by 1 | Viewed by 2448
Abstract
The estimation of user experience in a wireless network has always been a research hotspot, especially for the realization of network automation. In order to solve the problem of user experience estimation in wireless networks, we propose a two-step optimization method for the [...] Read more.
The estimation of user experience in a wireless network has always been a research hotspot, especially for the realization of network automation. In order to solve the problem of user experience estimation in wireless networks, we propose a two-step optimization method for the selection of the kernel function and bandwidth in a naive Bayesian classifier based on kernel density estimation. This optimization method can effectively improve the accuracy of estimation. At present, research on user experience estimation in wireless networks does not include an in-depth analysis of the reasons for the decline of user experience. We established a scheme integrating user experience prediction and network fault diagnosis. Key performance indicator (KPI) data collected from an actual network were divided into five categories, which were used to estimate user experience. The results of these five estimates were counted through the voting mechanism, and the final estimation results could be obtained. At the same time, this voting mechanism can also feed back to us which KPIs lead to the reduction of user experience. In addition, this paper also puts forward the evaluation standard of the multi-service perception capability of cell-level wireless networks. We summarize the user experience estimation for three main services in a cell to obtain a cell-level user experience evaluation. The results showed that the proposed method can accurately estimate user experience and diagnosis abnormal values in a timely manner. This method can improve the efficiency of network management. Full article
(This article belongs to the Special Issue Emotional AI and its Applications in Communications Networks)
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