Machine Learning for Big Data track of the 34th International ECMS Conference on Modelling and Simulation
MLBD 2020
- URL: http://www.scs-europe.net/conf/ecms2020/mlbd.html
- Event Date: 2020-06-09 ~ 2020-06-12
- Submission Date: 2020-02-24
This Track’s area of interest is on the Volume and Velocity dimensions of Big Data. Volume, referring to the size of the data, Velocity, referring to the data that is generated rapidly and needs to be analysed in real-time. The workshop accepts papers on the application of Machine Learning and Data Mining Algorithms on large, complex or data generated in real-time. Applications are for example Simulation monitoring, Time Series Analysis, Network Intrusion Detection, Health Monitoring, Trend Detection in Twitter, Financial Monitoring, etc. The conference track also encourages the submission of papers that introduce new techniques, algorithms, systems and workflows, for large quantities of data, data streams and/or Time Series Analysis.
Track-Chair:
Dr. Frederic Stahl, (German Research Center for Artificial Intelligence (DFKI), University of Reading UK)
Track-Co-Chairs:
Professor Dr. Mohamed Gaber (Birmingham City University, UK)
Dr. Marwan Hassani (Eindhoven University of Technology, Netherlands)
Topics of interest include but are not limited to:
* Data Mining and Machine Learning algorithms, models and techniques.
* Data Mining and Machine Learning applications.
* Data Mining and Machine Learning on simulation data.
* Data Mining of Big Data streams
* Data Mining of large quantities of data
* Explainable Data Mining models
* Scalability of Data Mining techniques
* Real-time data stream analytics of simulation data
* Data stream analytics systems
* Concept Drift Detection techniques
* Outlier Detection
* Signal Processing and Analytics
* Visualisation of real-time data streams
* Analytics of IoT data streams
Track-Chair:
Dr. Frederic Stahl, (German Research Center for Artificial Intelligence (DFKI), University of Reading UK)
Track-Co-Chairs:
Professor Dr. Mohamed Gaber (Birmingham City University, UK)
Dr. Marwan Hassani (Eindhoven University of Technology, Netherlands)
Topics of interest include but are not limited to:
* Data Mining and Machine Learning algorithms, models and techniques.
* Data Mining and Machine Learning applications.
* Data Mining and Machine Learning on simulation data.
* Data Mining of Big Data streams
* Data Mining of large quantities of data
* Explainable Data Mining models
* Scalability of Data Mining techniques
* Real-time data stream analytics of simulation data
* Data stream analytics systems
* Concept Drift Detection techniques
* Outlier Detection
* Signal Processing and Analytics
* Visualisation of real-time data streams
* Analytics of IoT data streams
Journal Promotion
|
| Acta Koreana (Acta Koreana) |
|
| Acta Via Serica (AVS) |
|
| Computers and Concrete (CAC) |
|
| Journal of Information Processing Systems (JIPS) |
|
| Journal of Internet Computing and Services (KSII - JICS) |
|
| Journal of Smart Tourism (Smart Tourism) |
|
| Membrane and Water Treatment (MWT) |
|
| Wind and Structures, An International Journal (Wind and Structures) |
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