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» An Artificial Chemistry for Networking
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AI
2007
Springer
16 years 1 months ago
Adding Local Constraints to Bayesian Networks
When using Bayesian networks, practitioners often express constraints among variables by conditioning a common child node to induce the desired distribution. For example, an ‘orâ...
Mark Crowley, Brent Boerlage, David Poole
EVOW
2009
Springer
15 years 10 months ago
Efficient Signal Processing and Anomaly Detection in Wireless Sensor Networks
In this paper the node-level decision unit of a self-learning anomaly detection mechanism for office monitoring with wireless sensor nodes is presented. The node-level decision uni...
Markus Wälchli, Torsten Braun
FLAIRS
2004
15 years 8 months ago
Mining Bayesian Networks to Forecast Adverse Outcomes Related to Acute Coronary Syndrome
One fascinating aspect of tool building for datamining is the application of a generalized datamining tool to a specific domain. Often times, this process results in a cross disci...
Andy Novobilski, Francis M. Fesmire, David Sonnema...
AI
2006
Springer
15 years 6 months ago
Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering
This article presents and analyzes algorithms that systematically generate random Bayesian networks of varying difficulty levels, with respect to inference using tree clustering. ...
Ole J. Mengshoel, David C. Wilkins, Dan Roth
TWC
2010
15 years 1 months ago
Impact of mobile node density on detection performance measures in a hybrid sensor network
We investigate the impact of mobile node density on several detection performance measures for stationary target detection by a hybrid sensor network consisting of both static and ...
Thakshila Wimalajeewa, Sudharman K. Jayaweera