NDLI: Application of a brain inspired model for profiling multi-view crime patterns

Content Provider	IEEE Xplore Digital Library
Author	Yee Ling Boo Alahakoon, D.
Copyright Year	2010
Description	Author affiliation: Cognitive and Connectionist Systems Lab (CCSL), Clayton School of Information Technology, Faculty of Information Technology, Monash University, Australia (Yee Ling Boo; Alahakoon, D.)
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.
Starting Page	493
Ending Page	498
File Size	249136
Page Count	6
File Format	PDF
ISBN	9781424485499
e-ISBN	9781424485529
DOI	10.1109/ICIAFS.2010.5715712
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-17
Publisher Place	Sri Lanka
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Law enforcement Growing Self Organising Maps Weapons Instruments Biological system modeling Adaptation model Fires Crime Profiling Brain modeling Brain Inspired Model
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A Brain Inspired Approach for Multi-View Patterns Identification

Clusters driven implementation of a brain inspired model for multi-view pattern identifications

A biologically inspired neural clustering model for capturing patterns from incomplete data

Identifying multi-view patterns with hierarchy and granularity based multimodal (HGM) cogntive model

Mining Multi-modal Crime Patterns at Different Levels of Granularity Using Hierarchical Clustering

A Hippocampal-inspired Self-Organising Learning Memory Model with Analogical Reasoning for Decision Support

Exploratory data analysis with Multi-Layer Growing Self-Organizing Maps

Multi-view and network modeling for technology system of systems

A self-organising spiking neural network trained using delay adaptation

Application of a brain inspired model for profiling multi-view crime patterns

Similar Documents

A Brain Inspired Approach for Multi-View Patterns Identification

Clusters driven implementation of a brain inspired model for multi-view pattern identifications

A biologically inspired neural clustering model for capturing patterns from incomplete data

Identifying multi-view patterns with hierarchy and granularity based multimodal (HGM) cogntive model

Mining Multi-modal Crime Patterns at Different Levels of Granularity Using Hierarchical Clustering

A Hippocampal-inspired Self-Organising Learning Memory Model with Analogical Reasoning for Decision Support

Exploratory data analysis with Multi-Layer Growing Self-Organizing Maps

Multi-view and network modeling for technology system of systems

A self-organising spiking neural network trained using delay adaptation

Application of a brain inspired model for profiling multi-view crime patterns