NEURAL NETWORK FOR PSYCHOLOGICAL CRIMINAL PROFILING

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The Neural Network for Psychological Criminal Profiling (NNPCP) is focusing on a neural network applied to violent crime analysis. Although many of these crimes are committed by an offender who suffers from some type of psychopathology, this system can find valid application in other forms of crime without such underlying serious pathology/mental illness. The NNPCP research project, which began in 2002, involves multidisciplinary groups in Italy and the United States. The elaboration system is based on a neural network and an evolved data-mining program that contains data on various types of serious crimes, notably homicides, and relative patterns of motivation. The primary inputs include the crime scene analysis, victimology, forensic and autopsy reports, and preliminary police reports. The secondary inputs include a structured criminological interview and a battery of psychological diagnostic tests of offenders apprehended for a crime as well as an interview with the crime scene investigators. A relationship between psychopathology or psychology and profiling is explored in each case. The outputs of the system (the investigative information) consist of 24 investigative areas and 200 specific types of information on the offender’s psychological, psychopathological, and motivational profile. The model for profiling an offender based on one crime thus relies on the fact that human behavior in the commission of a certain type of crime falls within parameters evidenced in data obtained from previous similar crimes committed by known offenders whose psychological characteristics and behaviors have been studied. The validity of this method depends on the broad and constant input of data into the NNPCP system, which makes the parameters of behaviors and psychological profiles for those who commit various types of crimes more valid. Criminal investigative departments around the world will be asked to contribute crime data for the NNPCP database. 

http://www.ncjrs.gov/App/Publications/abstract.aspx?ID=206520 

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