CFP: TETCI special issue on Data Driven Computational Intelligence for eGovernance, Socio-Political and Economic Systems

I. AIM AND SCOPE

E-Governance is the application of electronic, information and communication technologies in order to facilitate public services, support government administration and democratic processes, and strengthen the relations among citizens, civil society and the private sector. E-governance aims to utilize digital tools and models to promote the interaction between three key partners of modern societies: government, citizens and business. Optimising these interactions can help to facilitate political, social, economic stability and prosperity.

Significant breakthroughs in the development of nature inspired Computational Intelligence (CI) techniques allow for the utilization and analysis of vast amounts of data generated from different sources such as transactions of citizens with government services, human interaction with social networks and the use of interconnected smart pervasive devices. Many political, social and economic systems can be understood through bottom up computational methodologies such as agent based modelling and can equally be driven by various realworld data sources fraught with uncertainties pertaining to human decision making, knowledge perception and agreement models that need to be handled using fuzzy and probabilistic reasoning approaches. Top down machine learning techniques such as deep learning can be used to discover complex patterns and correlations in historical data for modelling and predicting consequences of economic shocks, utility of municipal services or changing political sentiments. Advances in evolutionary technique can provide a means for optimizing e-governance policies and strategies by simulating their impact on aspects such as labour and employability or modelling complex negotiation processes. CI approaches can be more broadly applied to model population mobility, economic growth, social behaviour, public health, security risks, education, welfare, geopolitics and environmental concerns.

II. THEMES

The aim of this Special Issue (SI) is to develop novel
computational tools and design systems to exploit the vast
amounts of data generated continuously through the
interaction of citizens with the government, and the
surrounding environment and provide optimized e-governance
services. The scope of this SI includes, but is not limited to:

•  Simulation models for socio-political economic systems
•  Measuring feedback and effects of new political initiatives
•  Sentiment analyses on utility of governance systems, public and commercial services
•  Facilitating citizen participation in policy decision making
•  Data analysis, visualization and modelling of social political, fiscal and threat associated behaviours.
•  Information retrieval, secure storage and recovery of data. 

III. SUBMISSIONS 

The special issue welcomes high quality contributions in the form of full or short papers. Manuscripts should be prepared according to the “Information for Authors” section of the journal found at http://cis.ieee.org/ieee-transactions-onemerging-topics-in-computational-intelligence.html and submissions should be done through the journal submission website: https://mc.manuscriptcentral.com/tetci-ieee, by selecting the Manuscript Type of “DDCIeGov” and clearly marking “Data Driven Computational Intelligence for eGovernance, Socio-Political and Economic Systems Special Issue Paper” as comments to the Editor-in-Chief. Submitted papers will be reviewed by at least three different expert reviewers. Submission of a manuscript implies that it is the authors’ original unpublished work and is not being submitted for possible publication elsewhere. 

IV. IMPORTANT DATES

5 th June 2017: Submission of Manuscripts

7 th August 2017: Notification of Review Results (R1) 

2 nd October 2017: Submission of Revised Manuscripts 

6 th November 2017: Final Review Results Notification (R2) 

4 th December 2017: Submission of Final Manuscripts 

V. GUEST EDITORS 

Dr Faiyaz Doctor, School of Computing Electronics and Mathematics, Faculty of Engineering, Environment and Computing, Coventry University, Coventry, UK Faiyaz.doctor@coventry.ac.uk 

Dr Edgar Galvan-Lopez, TAO Project, INRIA Saclay & LRI - Univ. Paris-Sud and CNRS, Orsay, France, edgar.galvan@inria.fr 

Professor Edward Tsang, Centre for Computational Finance and Economic Agents (CCFEA), School of Computer Science and Electronic Engineering, University of Essex, Essex, UK edward@essex.ac.uk