Clinical practice and research are facing a new challenge created by the rapid growth of health information science and technology, and the complexity and volume of biomedical data. Machine learning from medical data streams is a recent area of research that aims to provide better knowledge extraction and evidence-based clinical decision support in scenarios where data are produced as a continu...

In this paper we report the results of a pilot project we conduct to develop a semi-automated approach for tidal stream network extraction from remote sensor data. Our test site is part of the North Inlet-Winyah Bay National Estuarine Research Reserve in Georgetown, South Carolina, U.S.A. Largely lower than 1m above sea level, the study site consists of salt marshes behind barrier islands with ...

Finding Outlier detection in data streams has gained broad importance presently due to the increasing cases of fraud in various applications of data streams, data cleaning, network monitoring, invasive species monitoring, stock market analysis, detecting outlying cases inmedical data etc. Finding outliers in a collection of patterns is a very well-known problem in the data mining field. An outl...