[ecoop-info] Call for papers: Machine Learning and Data Mining for Sensor Networks (MLDM-SN), June 2-5, 2015 in London, UK

[Feras Obeidat]

*Call for papers*


*You are invited you to submit an article to the 2nd International Workshop
on Machine Learning and Data Mining for Sensor Networks (MLDM-SN), June
2-5, 2015 in London, UKThe website of MLDM-SN 2015 is available
at http://cs-conferences.acadiau.ca/MLDM-SN15/
<http://cs-conferences.acadiau.ca/MLDM-SN15/>*



The 2nd International Workshop on Machine Learning and Data Mining for
Sensor Networks (MLDM-SN)

MLDM-SN 2015 will be held in conjunction with the 6th International
Conference on Ambient Systems, Networks and Technologies(ANT 2015
<http://cs-conferences.acadiau.ca/ant-15/>)

London, UK
June 2-5, 2015



This workshop aims to bring together researchers and practitioners working
on different aspects of machine learning, data mining and sensor networks
technologies in an effort to highlight the state-of-the-art and discuss the
challenges and opportunities to explore new research directions.

The main topics to be addressed include (but not limited to):

   - Software agents approaches.
   - Data mining processes including data selection, sampling, cleaning,
   reduction, transformation, integration and aggregation, as well as model
   development, validation and deployment.
   - Data mining approaches to overcome sensor limitations such as
   available energy for transmission, computational power, memory, and
   communications bandwidth.
   - Distributed Bayesian learning (belief networks, decision networks)
   - Distributed clustering methods (distributed k-Means, dynamic neural
   networks)
   - Distributed machine learning (neural networks, support vector
   machines, decisions trees and rules, genetic algorithms) in sensor networks
   - Distributed Principal Component Analysis (PCA) and Independent
   Component Analysis (ICA)
   - Distributed statistical regression methods in sensor networks.
   - Efficient, scalable and distributed algorithms for large-scale DDM
   tasks such as classification, prediction, link analysis, time series
   analysis, clustering, and anomaly detection.
   - Incremental, exploratory and interactive mining.
   - Mining of data streams.
   - Power consumption characteristics of distributed data mining
   algorithms and developing data mining algorithms to minimize power
   consumption.
   - Privacy sensitive data mining.
   - Applications of data mining for senor networks in business, science,
   engineering, medicine, and other disciplines with particular attention to
   lessons learned.
   - Theoretical foundations in data mining and sensor network; extensions
   of computational learning theory to sensor networks.
   - Visual data mining.




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