CALL FOR PAPERS
Recent advances in the sensor and wireless technologies are giving enormous new opportunities as well plentiful new challenges to data mining. Big stream data are being generated constantly and everywhere, in various domains and IoT applications, such as biomedicine, civil engineering, manufacturing, smart-homes, telecommunications, robots, environment monitoring, and video surveillance, to name but a few. These all require adaptive and highly performing solutions to a wide range of technical issues, from feature extraction, visualization, online learning, anomaly detection, deep-learning, to implementations and distributed data mining.
Joining PAKDD again in 2017, the fourth MLSDA workshop will provide a valuable and informative platform for machine learning and data mining researchers and practitioners working in various sensor data domains.
We call for papers of relevant topics as follows:
- Time series analysis
- Motif detection
- Anomaly detection
- Online data stream mining
- Concept drift detection and handling
- Feature extraction and dimension reduction
- Ensemble learning
- Compressed sensing
- Sensory data modeling and prediction
- Smart environment
- Self-organization
- Distributed signal processing
- Resource management and task scheduling
- Energy efficiency and cloud offloading
- Reinforcement learning in WSNs
- Privacy-preserving data mining
- Multimodal signal processing
- Activity detection and behaviour classification
- Intrusion detection
All submissions will go through a rigorous double-blind peer review process conducted by the program committee. Accepted papers will to be published in the PAKDD workshop proceedings as an LNCS/LNAI volume. Selected papers will be invited for submission to special issues of SCI/SCIE-indexed journals.
Details about submission can be found on the Paper Submission page.