CALL FOR PAPERS
Recent advances in the sensor 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 AI again in 2018, the fifth MLSDA workshop will provide a valuable and rewarding 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:
- Anomaly detection
- Online data stream mining
- Concept drift detection and handling
- Feature extraction and dimension reduction
- Time series analysis
- Ensemble learning
- Low-power, in-situ machine learning
- Compressed sensing
- Sensory data modeling and prediction
- Smart environment
- Self-organization
- Distributed signal processing
- Energy efficiency and cloud offloading
- Reinforcement learning in WSNs
- Privacy-preserving data mining
- Multimodal signal processing
- Activity detection and behaviour classification
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 AI workshop proceedings as an ACM ICPS 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.