NDLI: Hybrid approach to crime prediction using deep learning

Content Provider	IEEE Xplore Digital Library
Author	Azeez, J. Aravindhar, D.J.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Hindustan Inst. of Technol. & Sci., Chennai, India (Azeez, J.; Aravindhar, D.J.)
Abstract	Prevention is better that Cure. Preventing a crime from occurring is better than investigating what or how the crime had occurred. Just like vaccination is given to a child to prevent disease, in today's world with such higher crime rate and brutal crime happenings, it have become necessary to have a vaccination systems that prevents from crimes happening. By vaccinating society against crime it refers to various methods such as educating peoples, creating awareness, increasing efficiency and proactive policing methods and other deterrent techniques. Inspired by two different existing approach to crime prediction, the first one present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. Crime incident prediction has depends mainly on the historical crime record and various geospatial and demographic information $^{[1]}.$ Even though it's promising, they do not take into account the rich and rapidly expanding social & web media context that surrounds incidents of interest. Next approach is based on the semantic analysis and natural language processing of Twitter posts via latent Dirichlet allocation, Topic detection Sentiment $analysis^{[3[4]]}.$ But both the techniques faces inherent limitations. Crime that happens these days are have following key characteristics such as crimes repeating in a periodic fashion, crimes occurring as a result of some other activity and occurrence of crimes pre indicated by some other information.
Starting Page	1701
Ending Page	1710
File Size	206379
Page Count	10
File Format	PDF
ISBN	9781479987900
e-ISBN	9781479987924
DOI	10.1109/ICACCI.2015.7275858
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-10
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Computational modeling LDA STL Sentiment Analysis Deep Learning RNN Undirected Probabilistic Graph Machine learning Prediction algorithms Market research Data models Informatics
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