PREDICTING THE RATE OF CRIME USING KNN ALGORITHM.
Crime is a common societal problem that has an effect on people's personal happiness and economic growth. It is regarded as a vital factor in deciding whether or not people should migrate to another city and what places should be avoided while travelling. With the rise of violations, law enforcement...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 1260 - 1265 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
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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=151006092&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006092 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151006092 151006092 151006092 151006092 ppf: 1260 ppct: 5 formats: fmt: @attributes: type: P tig: atl: PREDICTING THE RATE OF CRIME USING KNN ALGORITHM. aug: au: KUMAR, DEWASHISH KUMAR, ABHINASH GOWRI, S. SIVASANGARI, A. VIMALI, J. S. KUMARI, G. ROSLINENESA affil: Sathyabama Institute of Science and Technology, Chennai sug: subj: Crime Evaluation Prediction Models Algorithms Data Mining Crime Prevention and Control Machine Learning Human Descriptive Statistics Police Safety United States Geographic Information Systems Maps Systems Development Public Offenders Database Management Software ab: Crime is a common societal problem that has an effect on people's personal happiness and economic growth. It is regarded as a vital factor in deciding whether or not people should migrate to another city and what places should be avoided while travelling. With the rise of violations, law enforcement agencies are requesting more advanced regional data systems and modern information mining methods to help them better investigate corruption and protect their organisations. Despite the fact that violations can occur anywhere, it is essential that when criminals are looking for opportunities to commit wrongdoing, they search in the most common places. We hope to raise people's awareness of dangerous places in different eras by providing an information mining method for determining the most criminal hotspots and discovering the type, place, and time of committed wrongdoings. As a result, our proposed arrangement could allow people to avoid certain areas at particular times while also saving lives. Getting this kind of knowledge will also assist people in making better choices on where they want to live. Police authorities, on the other hand, may use this solution to increase the degree of wrongdoing prediction and aversion. It will be beneficial for the allocation of police assets. We want to make it possible by making the bulk of this data accessible. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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