This project introduces a comprehensive web application, "Crime Prediction and Reporting," leveraging geospatial data and machine learning algorithms to elevate crime analysis capabilities. By offering intuitive visualization of historical crime data, real-time incident reporting features, and predictive hotspot mapping, the application empowers law enforcement agencies with proactive policing strategies and optimized resource allocation. Emphasizing ethical considerations such as fairness and transparency, the platform ensures community engagement through SMS-based incident reporting, facilitating swift emergency responses. Ultimately, this initiative fosters collaboration between communities and authorities, promoting public safety through data-driven decision-making.

Topics
Machine learning, Careers and professions
1.
Eck
,
J. E.
 (
2006
).
Crime places in crime theory
. In
Weisburd
,
D.
, &
Eck
,
J. E.
 (Eds.),
Crime and Place: Crime Prevention Studies
(Vol.
4
, pp.
1
–
33
).
Google Scholar
 
2.
Chainey
,
S.
, &
Ratcliffe
,
J.
 (
2005
).
GIS and Crime Mapping
. In
D.
Weisburd
 &
J. E.
Eck
 (Eds.),
Crime and Place: Crime Prevention Studies
(Vol.
12
, pp.
1
–
30
)
Google Scholar
 
3.
Mohler
,
G.
,
Short
,
M. B.
,
Brantingham
,
P. J.
,
Schoenberg
,
F. P.
, &
Tita
,
G. E.
 (
2011
).
Self-exciting point process modeling of crime. Journal of the American Statistical Association
,
106
(
493
),
100
–
108
.
Google Scholar
 
4.
Braga
,
A. A.
, &
Weisburd
,
D.
 (
2012
).
The effects of focused deterrence strategies on crime : A systematic review and meta-analysis of the empirical evidence
.
Journal of Research in Crime and Delinquency
,
49
(
3
),
323
–
358
.
https://doi.org/10.1177/0022427811419368
Google Scholar
Crossref
Search ADS
 
5.
Ratcliffe
,
J. H.
 (
2010
). Crime mapping: Spatial and temporal challenges. In
Bruinsma
,
G.
, &
Weisburd
,
D.
 (Eds.),
Encyclopedia of Criminology and Criminal Justice
(pp.
377
–
387
).
Springer
.
Google Scholar
 
6.
Sherman
,
L. W.
, &
Weisburd
,
D.
 (
1995
).
General deterrent effects of police patrol in crime “hot spots
”:
A randomized, controlled trial. Justice Quarterly
,
12
(
4
),
625
–
648
.
Google Scholar
 
7.
Ashby
,
M. P. J.
, &
Bowers
,
K. J.
 (
2012
).
Prospective Hot-Spotting: The Future of Crime Mapping? British Journal of Criminology
,
52
(
2
),
285
–
303
.
Google Scholar
 
8.
Rengert
,
G. F.
, &
Lockwood
,
B.
 (
2009
). The geography of illegal drugs. In
Smith
,
M. J.
, &
Tilley
,
N.
 (Eds.),
Crime Science: New Approaches to Preventing and Detecting Crime
(pp.
143
–
161
).
Willan Publishing
.
Google Scholar
 
9.
J.
Deepika
,
Dr.
J.
Akilandeswari
.,
Fall Detection Technique for Older Individuals based on Deep Layered Neural Networks embedded with Transfer Learning.
, DOI:
https://doi.org/10.1109/ICSSIT55814.2023.10061005
, March
2023
.
Google Scholar
 
10.
Thangaraj
,
K.
Sakthivel
,
M.
Balasamy
,
Suganyadevi
,
S.
Computer-aided cluster formation in wireless sensor networks using machine learning-Journal of Intelligent and Fuzzy Systems.
-
2023
,
45
(
5
), pp.
7415
–
7428
.
Google Scholar
 
11.
Anitha Elavarasi
S
 &
Akilandeswari J.
Cosine
Based Partition Algorithm For Clustering Breast Cancer Data
, vol.
10
, no.
7
, pp.
16769
–
16789
, ISSN : 0973-4562,
2015
.
Google Scholar
 
12.
Vishwa Kiran
,
S.
,
Kaur
,
I.
,
Thangaraj
,
K.
,
Kingsy Grace
,
R.
,
Arulkumar
,
N.
Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images
,
2023
,
23
(
3
),
2240002
.
Google Scholar
 
13.
Akilandeswari
,
J.
,
Naveenkumar
,
A.
,
Sabeenian
,
R. S.
,
Iyyanar
,
P.
,
Paramasivam
,
M. E.
, &
Jothi
,
G.
,
An investigation on indoor navigation systems
1
(pp.
115
–
124
).
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