JOURNAL ARTICLE

A GROWTH OF ARTIFICIAL INTELLIGENCE IN CRIME DETECTION USAGES IN LAW ENFORCEMENT.

  • Published In: i-Manager's Journal on Digital Forensics & Cyber Security (JDF), 2024, v. 2, n. 2. P. 30 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: A., ANUSYA; V., CHITRA 3 of 3

Abstract

Artificial intelligence (AI) is increasingly becoming a valuable tool for detecting illegal activities and holding offenders accountable. Among the most advanced AI technologies, facial recognition is widely used by law enforcement agencies for crime detection and prevention. This study aims to (i) analyze the use of artificial intelligence in law enforcement and (ii) identify the drawbacks of AI. A total of 75 respondents were selected from Kanyakumari and Tirunelveli districts, using simple random sampling due to time constraints. Among the respondents, 38 were from Tirunelveli and 37 from Kanyakumari. The Garrett ranking method was employed to analyze the data. The study found that facial recognition ranked first with a Garrett score of 62.1, followed by language processing with a score of 58.97. The study concluded that facial recognition technology significantly improves the accuracy and efficiency of crime profiling, while language processing aids in overcoming language barriers during investigations. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:i-Manager's Journal on Digital Forensics & Cyber Security (JDF). 2024/12, Vol. 2, Issue 2, p30
  • Document Type:Article
  • Subject Area:Communication and Mass Media
  • Publication Date:2024
  • ISSN:2583911X
  • DOI:10.26634/jdf.2.2.21271
  • Accession Number:181721643
  • Copyright Statement:Copyright of i-Manager's Journal on Digital Forensics & Cyber Security (JDF) is the property of i-manager Publications and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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