MLDM 2017 - 4th Int. Workshop on Machine Learning and Data Mining for Sensor Networks (MLDM-SN)

The 4th Int. Workshop on Machine Learning and Data Mining for Sensor Networks (MLDM-SN) - Madeira, Portugal
May 16-19, 2017
http://mldmsn-1084.appspot.com/index.html
MLDM-SN 2017 will be held in conjunction with the 8th International Conference on Ambient Systems, Networks and Technologies (ANT 2017)
http://cs-conferences.acadiau.ca/ant-17/
Madeira, Portugal
May 16-19, 2017
Submission Deadline: February 17, 2017
Author Notification: March 1, 2017
Final Manuscript Due: March 13, 2017
Workshop: May 16-19, 2017
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.
Publication
Only original papers will be considered that have neither been published nor submitted for publication elsewhere, including web publication. All submissions will be handled electronically. The length of the paper is limited to 6 pages. All papers will be reviewed by at least two independent reviewers.
All accepted papers will be printed in the conference proceedings published by Elsevier Science in the open-access Procedia Computer Science series on-line. Procedia Computer Sciences is hosted on www.Elsevier.com and on Elsevier content platform ScienceDirect (www.sciencedirect.com), and will be freely available worldwide. All papers in Procedia will also be indexed by Thomson Reuters' Conference Proceeding Citation Index http://thomsonreuters.com/conference-proceedings-c.... The papers will contain linked references, XML versions and citable DOI numbers. You will be able to provide a hyperlink to all delegates and direct your conference website visitors to your proceedings. All accepted papers will also be indexed in DBLP (http://dblp.uni-trier.de/).
Program Chair
Feras Al-Obeidat, Zayed University, UAE
Program Co-chair
Omar Al-Fandi , Zayed University, UAE
M. Basel Almourad , Zayed University, UAE