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IEEE SMC 2019 - Special Session - Actionable Pattern-Driven Analytics and Artificial Intelligence Techniques - Deadline: March 31
Posted by:
webmasterphilfv
Date: March 01, 2019 07:15AM
IEEE SMC’19 October 6-9, 2019 – Bari, Italy
SPECIAL SESSION ON SPECIAL SESSION CODE
“Actionable Pattern-Driven Analytics and Artificial Intelligence
Techniques”
5yppw
Session Organizers:
Jerry Chun-Wei Lin, Western Norway University of Applied Sciences, Norway, jerrylin@ieee.org
Philippe Fournier-Viger, Harbin Institute of Technology (Shenzhen), China, philfv8 AT yahoo.com
Chun-Hao Chen, Tamkang University, Taiwan, chchen6814@gmail.com
Tzung-Pei Hong, National University of Kaohsiung, Taiwan, tphong@nuk.edu.tw
Session area: Cybernetics
Pattern-driven Analytics and Mining has received a lot of attention in the last two decades since information discovered in data can be used to support decision and strategy making. Various types of patterns and knowledge can be discovered for different domains and applications. Moreover, many techniques have been designed and implemented to handle rich constraints on different aspects such as weight, frequency, utility, interestingness, and importantness. Besides traditional methods for mining interesting patterns, several machine learning and optimization methods have been proposed in artificial intelligence to find the interesting patterns and retrieve that information in a reasonable time, or in a big data environment. This invited session focuses on the topic of discovering the actionable knowledge in realistic situations and enterprise applications. Actionable knowledge discovery requires to propose innovative methodologies, principles, methods, techniques, framework, theory, and application to handle the above challenges. We thus welcome original, creative, innovative, cutting-edge and state-of-the-art theoretical and applied contributions on this topic, including on the following aspects: (1) next- generation data analytics and prediction theories, methodologies, frameworks and processes to support actionable pattern-driven analytics and prediction; (2) develop new machine learning and optimization algorithms and methods for handling the big data environment to retrieve actionable patterns in a reasonable and acceptable time; (3) investigate effective and efficiency principles and techniques for acquiring, representing, modelling and utilizing intelligence in realistic applications and domains; (4) design operational tools and systems to address business concerns and deliver actionable patterns for business purposes and processes; (5) investigate novel trends in patterndriven analytics using AI techniques for different domains and and applications, and (6) studies on the security and privacy of actionable knowledge discovery and related organizational and social issues.
Keywords
• Pattern-driven analytics and prediction
• Machine learning and optimization
• Artificial intelligences
• Actional knowedlge discovery
SUBMISSION
Papers must be submitted electronically for peer review through PaperCept by March 31, 2019:
http://controls.papercept.net/conferences/scripts/start.pl
In PaperCept, click on the SMC 2019 link
“Submit a Contribution to SMC'19” and follow the steps.
All papers must be written in English and should describe original work. For guidelines, please follow the
the SMC website link http://smc2019.org/information_for_authors.html
DEADLINES
March 31, 2019: deadline for paper submission
June 7, 2019: notification of paper acceptance/rejection
July 7, 2019: deadline for final camera-ready papers.
Edited 3 time(s). Last edit at 03/01/2019 07:19AM by webmasterphilfv.
SPECIAL SESSION ON SPECIAL SESSION CODE
“Actionable Pattern-Driven Analytics and Artificial Intelligence
Techniques”
5yppw
Session Organizers:
Jerry Chun-Wei Lin, Western Norway University of Applied Sciences, Norway, jerrylin@ieee.org
Philippe Fournier-Viger, Harbin Institute of Technology (Shenzhen), China, philfv8 AT yahoo.com
Chun-Hao Chen, Tamkang University, Taiwan, chchen6814@gmail.com
Tzung-Pei Hong, National University of Kaohsiung, Taiwan, tphong@nuk.edu.tw
Session area: Cybernetics
Pattern-driven Analytics and Mining has received a lot of attention in the last two decades since information discovered in data can be used to support decision and strategy making. Various types of patterns and knowledge can be discovered for different domains and applications. Moreover, many techniques have been designed and implemented to handle rich constraints on different aspects such as weight, frequency, utility, interestingness, and importantness. Besides traditional methods for mining interesting patterns, several machine learning and optimization methods have been proposed in artificial intelligence to find the interesting patterns and retrieve that information in a reasonable time, or in a big data environment. This invited session focuses on the topic of discovering the actionable knowledge in realistic situations and enterprise applications. Actionable knowledge discovery requires to propose innovative methodologies, principles, methods, techniques, framework, theory, and application to handle the above challenges. We thus welcome original, creative, innovative, cutting-edge and state-of-the-art theoretical and applied contributions on this topic, including on the following aspects: (1) next- generation data analytics and prediction theories, methodologies, frameworks and processes to support actionable pattern-driven analytics and prediction; (2) develop new machine learning and optimization algorithms and methods for handling the big data environment to retrieve actionable patterns in a reasonable and acceptable time; (3) investigate effective and efficiency principles and techniques for acquiring, representing, modelling and utilizing intelligence in realistic applications and domains; (4) design operational tools and systems to address business concerns and deliver actionable patterns for business purposes and processes; (5) investigate novel trends in patterndriven analytics using AI techniques for different domains and and applications, and (6) studies on the security and privacy of actionable knowledge discovery and related organizational and social issues.
Keywords
• Pattern-driven analytics and prediction
• Machine learning and optimization
• Artificial intelligences
• Actional knowedlge discovery
SUBMISSION
Papers must be submitted electronically for peer review through PaperCept by March 31, 2019:
http://controls.papercept.net/conferences/scripts/start.pl
In PaperCept, click on the SMC 2019 link
“Submit a Contribution to SMC'19” and follow the steps.
All papers must be written in English and should describe original work. For guidelines, please follow the
the SMC website link http://smc2019.org/information_for_authors.html
DEADLINES
March 31, 2019: deadline for paper submission
June 7, 2019: notification of paper acceptance/rejection
July 7, 2019: deadline for final camera-ready papers.
Edited 3 time(s). Last edit at 03/01/2019 07:19AM by webmasterphilfv.