Connectionists: CFP: IJCNN 2018/WCCI 2018 Special Session on Data Mining and Knowledge Discovery in Cyber-Physical Systems

[OZAWA, Seiichi]

[Apologies if you receive multiple copies of this CFP]

IJCNN-26 Data Mining and Knowledge Discovery in Cyber-Physical Systems

Organized by Seiichi Ozawa (ozawasei at kobe-u.ac.jp), Bo Tang, Cesare Alippi, Haibo He

Special session website: https://my.ece.msstate.edu/faculty/tang/IJCNN2018CPS.html

The integration of embedded computation, communication, sensors and actuators has led to the emergence and development of Cyber-Physical Systems (CPS). Such systems cover vast application areas such as those referring to power grids, transportation, healthcare, manufacturing, structure health monitoring just to name the few.

Thanks to their ability to interact with the environment they are deployed in, the sensor platform associated with CPS collects a large amounts of data. By embedding intelligence in the application, researchers and engineers can enable new functions not previously possible, leading to many smart-X systems such as smart grid, smart healthcare, smart elderly care, smart agriculture, smart transportation, and smart building, among others. Computational intelligence and machine learning-based data mining techniques constitute the basis of intelligence, by handling the uncertainty coming from the physical world and support automatic decision making such that the smart systems are more robust, adaptive and fault tolerant to the dynamically changing environments.

The large-scale and heterogeneous nature of data in CPS raises a number of challenges for data mining and knowledge discovery. Firstly, the ubiquitous deployment of sensors produces massive amounts of data. In contrast to traditional big data challenges, mining from massive data streams in CPS usually needs be efficient in order to support real-time decision making. Meanwhile, data become easily obsolete, due to the dynamically changing environment (time variance). Learning from limited labeled data –this is mostly the case- requires advanced semi-supervised learning and unsupervised learning techniques for big data analysis. Moreover, heterogeneous data are usually collected from multiple sources or systems, and information fusion at data/feature/model levels is usually needed. The goal of this special session is to unveil these challenges and present the state-of-the-art research activities and results on all facets of data mining and knowledge discovery in CPS.

Scope and Topics

The goal of this special session is to unveil these challenges and present the state-of-the-art research activities and results on all facets of data mining and knowledge discovery in CPS. The special session invites submissions in any of the following areas:

	• Learning with limited or inaccurate supervision
	• Data and information fusion in smart environments
	• Data analytics with distributed computing
	• Data mining in Smart-X environments and the Internet of Things
	• Mobile computing and data mining
	• Intelligent transportation systems
	• Computational intelligence in structure health monitoring
	• CPS in smart city
	• CPS in smart healthcare and elderly care
	• CPS in smart agriculture
	• Big Data model in CPS
	• Reinforcement learning in CPS
	• Reliability and sustainability in CPS
	• Performance optimization in CPS
	•Application and implementation of CPS

Important Dates

15th January 2018 – Paper Submission
15th March 2018 – Paper Acceptance
1st May 2018 – Final Paper Submission
1st May 2018 – Early Registration
8-13 July 2018 – IEEE WCCI 2018, Rio de Janeiro, Brazil