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Reducing the impact of seasonal influenza epidemics and other pandemics such as the H1N1 is of paramount importance for public health authorities. Studies have shown that effective interventions can be taken to contain the epidemics if early detection can be made. Traditional approach employed by the Centers for Disease Control and Prevention (CDC) includes(More)
Seasonal influenza epidemics causes severe illnesses and 250,000 to 500,000 deaths worldwide each year. Other pandemics like the 1918 “Spanish Flu” may change into a devastating one. Reducing the impact of these threats is of paramount importance for health authorities, and studies have shown that effective interventions can be taken to contain the(More)
Seasonal influenza epidemics cause several million cases of illnesses cases and about 250,000 to 500,000 deaths worldwide each year. Other pandemics like the 1918 “Spanish Flu” may change into devastating event. Reducing the impact of these threats is of paramount importance for health authorities, and studies have shown that effective interventions can be(More)
We present our most recent efforts in applying XCS to automatic target recognition (ATR). We place particular emphasis on ATR as a series of linked problems, which include pre-processing of multi-spectral data, detection of objects (in this case, vehicles) in that data, and identification (classification) of those objects. Multi-spectral data contains(More)
In this work, we have aimed to compare both a Markov Tree statistical modeling and estimation method and Markov Random Field modeling and estimation methods with each other and with ordinary kriging, an estimation technique commonly used in geostatistical modeling and data interpolation. Our goal has been to identify robust statistical estimation methods(More)
With the growing use of social media networks, trends are being discussed and talked about everywhere. Trend Analysis is a skeletal mapping of expected changes or activities occurring in the societies, markets, organizations and the consumers who drive them. Past trends and patterns in the data can be studied and used, to make predictions for future.(More)