Special Issue "Recent Advances in Social Data and Artificial Intelligence II"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer Science and Symmetry/Asymmetry".

Deadline for manuscript submissions: 31 December 2022 | Viewed by 448

Special Issue Editors

Prof. Dr. Gautam Srivastava
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Guest Editor
Prof. Dr. Hari Mohan Srivastava
grade E-Mail Website
Guest Editor
Department of Mathematics and Statistics, University of Victoria, Victoria, BC V8W 3R4, Canada
Interests: real and complex analysis; fractional calculus and its applications; integral equations and transforms; higher transcendental functions and their applications; q-series and q-polynomials; analytic number theory; analytic and geometric Inequalities; probability and statistics; inventory modelling and optimization
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Special Issue Information

Dear colleagues,

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized.

In this Special Issue, we cordially invite and welcome review, expository, and original research articles dealing with the recent advances in the subjects of social data and artificial intelligence, as well as their links potential to cyberspace.

Cyberspace, the seamless integration of physical, social, and mental spaces, is an integral part of our society, ranging from learning and entertainment to business and cultural activities, and so on. However, there are a number of pressing challenges associated with cyberspace. For example, how do we strike a balance between the need for strong cybersecurity and preserving the privacy of ordinary citizens?

This Special Issue has emerged from the International Conference on Social Data and Artificial Intelligence (SDAI 2020) held in Toronto, Canada on 26–27 May 2020 and the IEEE Cyber Science and Technology Congress (CyberSciTech 2020), which will also be held in Canada (CyberSciTech 2020, Calgary, Canada, 22–26 June 2020).

To address the challenges described for both conferences, there is a need to establish new science and research portfolios that incorporate social data and artificial intelligence alone or in combination with cyber-physical, cyber-social, cyber-intelligent, and cyber-life technologies in a cohesive and efficient manner.

Prof. Dr. Gautam Srivastava
Prof. Dr. Hari Mohan Srivastava
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. Symmetry 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 1800 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

  • social data inadequacies and inconsistencies
  • predictive models of social behaviors
  • infrastructure and architecture for testing social theories
  • data collection and analysis platforms
  • relevance of IoT for social science theories
  • building capacity to continuously collect data across a range of social media networks
  • designing efficient parsers to deal with noisy social media data-sets for real-time tracking of health issues, diseases, and wellness
  • designing tools to map and measure the effectiveness of health campaigns by healthcare organizations
  • cross-validating the predictive models of social media data-sets with ground truth data
  • developing frameworks and algorithms to perform real-time analysis of social media data-sets
  • cyberspace theory and technology
  • cyber social computing and networks
  • cyber life and wellbeing
  • cyber intelligence and cognitive science

Published Papers (1 paper)

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Research

Article
Prediction of Spread Trend of Epidemic Based on Spatial-Temporal Sequence
Symmetry 2022, 14(5), 1064; https://doi.org/10.3390/sym14051064 - 23 May 2022
Viewed by 229
Abstract
Coronavirus Disease 2019 (COVID-19) continues to spread throughout the world, and it is necessary for us to implement effective methods to prevent and control the spread of the epidemic. In this paper, we propose a new model called Spatial–Temporal Attention Graph Convolutional Networks [...] Read more.
Coronavirus Disease 2019 (COVID-19) continues to spread throughout the world, and it is necessary for us to implement effective methods to prevent and control the spread of the epidemic. In this paper, we propose a new model called Spatial–Temporal Attention Graph Convolutional Networks (STAGCN) that can analyze the long-term trend of the COVID-19 epidemic with high accuracy. The STAGCN employs a spatial graph attention network layer and a temporal gated attention convolutional network layer to capture the spatial and temporal features of infectious disease data, respectively. While the new model inherits the symmetric “space-time space” structure of Spatial–Temporal Graph Convolutional Networks (STGCN), it enhances its ability to identify infectious diseases using spatial–temporal correlation features by replacing the graph convolutional network layer with a graph attention network layer that can pay more attention to important features based on adaptively adjusted feature weights at different time points. The experimental results show that our model has the lowest error rate compared to other models. The paper also analyzes the prediction results of the model using interpretable analysis methods to provide a more reliable guide for the decision-making process during epidemic prevention and control. Full article
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence II)
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