Title :
Application of a brain inspired model for profiling multi-view crime patterns
Author :
Boo, Yee Ling ; Alahakoon, Damminda
Author_Institution :
Cognitive & Connectionist Syst. Lab. (CCSL), Monash Univ., Clayton, VIC, Australia
Abstract :
With the massive amount of crime data generated daily, this has put law enforcement under intensive stress. This means that law enforcement has to compete against the time to solve crime. In addition, the focus of crime investigation has been expanded from the ability to catch the criminals towards the ability to act before a crime happens (i.e pre-crime). Given such situation, creation of crime profiles is very important to law enforcement, especially in understanding the behaviours of criminals and identifying the characteristics of similar crimes. In fact, crime profiles could be used to solve similar crimes and thus pre-crime action could be conducted. In this paper, a brain inspired conceptual model is proposed and a structurally adaptive neural network is deployed for its implementation. Subsequently, the proposed model is applied for the identification and presentation of multi-view crime patterns. Such multi-view crime patterns could be useful for the construction of crime profiles. Moreover, the suitability of the proposed model in crime profiling is discussed and demonstrated through some experimental results.
Keywords :
criminal law; law administration; neural nets; police data processing; adaptive neural network; brain inspired conceptual model; crime data; crime investigation; crime profile; crime profiling; criminal behaviour; law enforcement; multiview crime pattern; precrime action; Adaptation model; Biological system modeling; Brain modeling; Fires; Instruments; Law enforcement; Weapons; Brain Inspired Model; Crime Profiling; Growing Self Organising Maps;
Conference_Titel :
Information and Automation for Sustainability (ICIAFs), 2010 5th International Conference on
Conference_Location :
Colombo
Print_ISBN :
978-1-4244-8549-9
DOI :
10.1109/ICIAFS.2010.5715712