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- W2892868838 abstract "A hot-spot mapping is an advanced crime detection technique which helps police personnel to identify high-crime areas and the best way to respond. However identification of crime location with less man power would be a more difficult process. In this work, Social crime data aware kernel density estimation based serial crime detection approach (SAKDESD) is implemented to group the serial and social crime data set in terms of more similarity. Social crime data set consists of various user comments about the crime happening in different locations which can provide the in-depth information about the serial crimes. The unstructured social crime data set is pre-processed to obtain meaningful structured format. This work also adapts the latent semantic approach for finding the similar topics present in the social crime data set which can lead to accurate prediction and efficient grouping of serial crimes. The experimental tests were conducted in matlab simulation environment which proves that the proposed approach SAKDESD provides a better result than the existing approach such as Modified graph cut clustering algorithm (MGCC)." @default.
- W2892868838 created "2018-10-05" @default.
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- W2892868838 date "2018-01-01" @default.
- W2892868838 modified "2023-09-23" @default.
- W2892868838 title "Hot Spot Identification Using Kernel Density Estimation for Serial Crime Detection" @default.
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- W2892868838 doi "https://doi.org/10.1007/978-981-13-1936-5_28" @default.
- W2892868838 hasPublicationYear "2018" @default.
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