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Special Sessions

Special sessions are very small and specialized events to be held during the conference as a set of oral and poster presentations that are highly specialized in some particular theme or consisting of the works of some particular international project. The goal of special sessions (minimum 4 papers; maximum 9) is to provide a focused discussion on innovative topics. All accepted papers will be published in a special section of the conference proceedings book, under an ISBN reference, and on digital support. All papers presented at the conference venue will be available at the SCITEPRESS Digital Library. SCITEPRESS is a member of CrossRef and every paper is given a DOI (Digital Object Identifier). The proceedings are submitted for indexation by SCOPUS, Google Scholar, DBLP, Semantic Scholar, EI and Web of Science / Conference Proceedings Citation Index.


SPECIAL SESSIONS LIST

SEC_SCIS 2024Special Session on Soft Computing in Ethicity and Smart Cities Services
Chair(s): Allel Hadjali, Miroslav Hudec and Edy Portmann

Special Session on Soft Computing in Ethicity and Smart Cities Services - SEC_SCIS 2024

Paper Submission: March 5, 2024
Authors Notification: March 14, 2024
Camera Ready and Registration: March 22, 2024


Co-chairs

Allel Hadjali
LIAS, LIAS/ENSMA, Poitiers
France
e-mail
 
Miroslav Hudec
Department of Systems Engineering, VSB - Technical University of Ostrava
Czech Republic
e-mail
 
Edy Portmann
University of Fribourg
Switzerland
e-mail
 
Scope

Fuzzy systems can deal with natural-language data and are therefore very suitable when it comes to characterising the expressions of people for example in social media and to test ethical maturity. In this context, ethicity means a fuzzy measuring of an ethical value, which may vary from 0 (non-ethical) to 1 (fully ethical).
Next, soft computing solutions allows smart participation, efficient data collection, data analyses and mining knowledge in an explainable and understandable way for all urban stakeholders. In addition, emerging soft computing approaches also allow for interactive machine learning in a collaborative manner with citizens able to interact with their data. This results in services that are based on citizens' needs and thus sustainably improve the quality of coexistence.



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