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Prior work on criminal incident prediction has relied primarily on the historical crime record and various geospatial and demographic information sources. Although promising, these models do not take into account the rich and rapidly expanding social media context that surrounds incidents of interest. This paper presents a preliminary investigation of(More)
Law enforcement agencies need crime forecasts to support their tactical operations; namely, predicted crime locations for next week based on data from the previous week. Current practice simply assumes that spatial clusters of crimes or ''hot spots'' observed in the previous week will persist to the next week. This paper introduces a multivariate prediction(More)
Some target tracking filters ignore the radar range rate measurements because they are highly nonlinear in Cartesian space. A linearized measurement equation, composed of range rate's partial derivatives with respect to the track state, is sometimes used in an extended Kalman filter. Unfortunately, this naive linearization biases the posterior estimates.(More)
Personal and property crimes create large economic losses within the United States. To prevent crimes, law enforcement agencies model the spatio-temporal pattern of criminal incidents. In this paper, we present a new modeling process that combines two of our recently developed approaches for modeling criminal incidents. The first component of the process is(More)
With the rapid growth of the elderly population, there is a need to assess the ability of elders to maintain an independent and healthy lifestyle. One possible method is to employ the concepts of ambient intelligence to remotely monitor an elder's activity. The SmartHouse project uses a system of basic sensors to monitor a person's in-home activity, and a(More)
The authors describe experiments using a genetic algorithm for feature selection in the context of neural network classifiers, specifically, counterpropagation networks. They present the novel techniques used in the application of genetic algorithms. First, the genetic algorithm is configured to use an approximate evaluation in order to reduce significantly(More)