Juvenile's delinquent behavior, risk factors, and quantitative assessment approach: A systematic review.
Background: Not only in India but also worldwide, criminal activity has dramatically increasing day by day among youth, and it must be addressed properly to maintain a healthy society. This review is focused on risk factors and quantitative approach to determine delinquent behaviors of juveniles. Ma...
| Publicado en: | Indian Journal of Community Medicine Vol. 47; no. 4; pp. 483 - 491 |
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| Autores principales: | , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Wolters Kluwer India Pvt Ltd
Oct-Dec2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161116912&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161116912 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09700218 1CQJ jtl: Indian Journal of Community Medicine issn: 09700218 maglogo: N pubinfo: dt: Oct-Dec2022 vid: 47 iid: 4 pid: 16919 pub: Wolters Kluwer India Pvt Ltd artinfo: ui: 161116912 161116912 161116912 10.4103/ijcm.ijcm_1061_21 161116912 ppf: 483 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Juvenile's delinquent behavior, risk factors, and quantitative assessment approach: A systematic review. aug: au: Gupta, Madhu Mohapatra, Subrajeet Mahanta, Prakash affil: Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi, Jharkhand sug: subj: Juvenile Delinquency Risk Factors Juvenile Delinquency Prevention and Control Machine Learning Human Male Female Child Adolescence India Systematic Review Crime Models, Statistical Early Diagnosis Public Offenders Computers and Computerization PubMed Child: 6-12 years Adolescent: 13-18 years Male Female ab: Background: Not only in India but also worldwide, criminal activity has dramatically increasing day by day among youth, and it must be addressed properly to maintain a healthy society. This review is focused on risk factors and quantitative approach to determine delinquent behaviors of juveniles. Materials and Methods: A total of 15 research articles were identified through Google search as per inclusion and exclusion criteria, which were based on machine learning (ML) and statistical models to assess the delinquent behavior and risk factors of juveniles. Results: The result found ML is a new route for detecting delinquent behavioral patterns. However, statistical methods have used commonly as the quantitative approach for assessing delinquent behaviors and risk factors among juveniles. Conclusions: In the current scenario, ML is a new approach of computer-assisted techniques have potentiality to predict values of behavioral, psychological/mental, and associated risk factors for early diagnosis in teenagers in short of times, to prevent unwanted, maladaptive behaviors, and to provide appropriate intervention and build a safe peaceful society. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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