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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM23965

CYBER CRIME ESTIMATION AND IDENTIFICATION: A HYBRID KEY INDICATOR USING MACHINE LEARNING AND DATA ANALYTICS

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM23965

Volume:

7 |

6 |

Jun 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MPY

Mrs. Peddaboina Yamuna

SY

Saideep Yamsani

TK

Tejaswini Kachagandi

VC

Vishal Chokkarapu

SP

Sagarika Posina

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Abstract

Social media text analytics involves extracting information from text sources, including social media, through text analysis. Social media plays a significant role in addressing the problem of crime, which is a major issue in society today. The occurrence of crime negatively impacts both quality of life and economic growth. By analyzing historical data, we can identify crime patterns and make predictions about future incidents. However, there are certain unreported and unsolved crimes due to insufficient evidence, making the task of locating criminals challenging. One way to address this is by monitoring social media for criminal activity, as users sometimes share information about their surroundings on these platforms. In this research paper, we propose a Machine Learning approach to detect crimes, analyze their types, and investigate/trace the evidence of the attacker. Initially, we retrieve text messages that contain predefined

KEYWORDS

related to crimes

Cite This Article

Mrs. Peddaboina Yamuna, Saideep Yamsani, Tejaswini Kachagandi, Vishal Chokkarapu, Sagarika Posina, "CYBER CRIME ESTIMATION AND IDENTIFICATION: A HYBRID KEY INDICATOR USING MACHINE LEARNING AND DATA ANALYTICS," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 6, Jun 2023. https://doi.org/10.55041/IJSREM23965

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Publication Date

6/22/2023

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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0

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2

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5

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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