Matches in SemOpenAlex for { <https://semopenalex.org/work/W4383109651> ?p ?o ?g. }
Showing items 1 to 70 of
70
with 100 items per page.
- W4383109651 abstract "An act that violates the law is considered a crime. It is detrimental to society, therefore, understanding crime is essential in order to prevent criminal activity. Data-driven research can help in both preventing and combating crime. According to contemporary studies, only a few offenders commit more than half of all crimes. To respond to and solve the spatiotemporal criminal activity, law enforcement officials need information regarding the illicit activities as soon as possible. In this work, techniques for supervised learning are utilized to forecast criminal conduct. Crimes are predicted by the suggested database-driven system by analyzing 12 years of criminal activity data from San Francisco. The study examines various factors, such as population density, socio-economic status, and location-specific characteristics, that may impact crime rates in different regions. This study examines the application of various machine learning techniques for predicting crime rates based on geographical data. To predict crime rates in various geographical locations, the study employs six popular algorithms: K-Nearest Neighbor (KNN), AdaBoost, Random Forests, XGBoost, Stochastic Gradient Descent (SGD), and LightGBM. According to the study's findings, machine learning algorithms can be used to anticipate crime rates and can offer significant information to policymakers and law enforcement organizations that can be used to help prevent and lessen crime. The results of this study have significant ramifications for the formulation of strategies for preventing crime and resource allocation for the eradication of crime in particular geographic areas." @default.
- W4383109651 created "2023-07-05" @default.
- W4383109651 creator A5052187157 @default.
- W4383109651 creator A5092397789 @default.
- W4383109651 date "2023-05-09" @default.
- W4383109651 modified "2023-09-25" @default.
- W4383109651 title "Geographical crime rate prediction" @default.
- W4383109651 cites W2083619093 @default.
- W4383109651 cites W2740333758 @default.
- W4383109651 cites W2930389570 @default.
- W4383109651 cites W2971145443 @default.
- W4383109651 cites W3017210109 @default.
- W4383109651 cites W3017373806 @default.
- W4383109651 cites W3020646952 @default.
- W4383109651 cites W3033117735 @default.
- W4383109651 cites W3158174949 @default.
- W4383109651 cites W4220926084 @default.
- W4383109651 doi "https://doi.org/10.1109/iciem59379.2023.10167307" @default.
- W4383109651 hasPublicationYear "2023" @default.
- W4383109651 type Work @default.
- W4383109651 citedByCount "0" @default.
- W4383109651 crossrefType "proceedings-article" @default.
- W4383109651 hasAuthorship W4383109651A5052187157 @default.
- W4383109651 hasAuthorship W4383109651A5092397789 @default.
- W4383109651 hasConcept C119857082 @default.
- W4383109651 hasConcept C144024400 @default.
- W4383109651 hasConcept C149923435 @default.
- W4383109651 hasConcept C153180980 @default.
- W4383109651 hasConcept C154945302 @default.
- W4383109651 hasConcept C15744967 @default.
- W4383109651 hasConcept C17744445 @default.
- W4383109651 hasConcept C199539241 @default.
- W4383109651 hasConcept C202565627 @default.
- W4383109651 hasConcept C2776876444 @default.
- W4383109651 hasConcept C2780262971 @default.
- W4383109651 hasConcept C2908647359 @default.
- W4383109651 hasConcept C41008148 @default.
- W4383109651 hasConcept C73484699 @default.
- W4383109651 hasConcept C77088390 @default.
- W4383109651 hasConceptScore W4383109651C119857082 @default.
- W4383109651 hasConceptScore W4383109651C144024400 @default.
- W4383109651 hasConceptScore W4383109651C149923435 @default.
- W4383109651 hasConceptScore W4383109651C153180980 @default.
- W4383109651 hasConceptScore W4383109651C154945302 @default.
- W4383109651 hasConceptScore W4383109651C15744967 @default.
- W4383109651 hasConceptScore W4383109651C17744445 @default.
- W4383109651 hasConceptScore W4383109651C199539241 @default.
- W4383109651 hasConceptScore W4383109651C202565627 @default.
- W4383109651 hasConceptScore W4383109651C2776876444 @default.
- W4383109651 hasConceptScore W4383109651C2780262971 @default.
- W4383109651 hasConceptScore W4383109651C2908647359 @default.
- W4383109651 hasConceptScore W4383109651C41008148 @default.
- W4383109651 hasConceptScore W4383109651C73484699 @default.
- W4383109651 hasConceptScore W4383109651C77088390 @default.
- W4383109651 hasLocation W43831096511 @default.
- W4383109651 hasOpenAccess W4383109651 @default.
- W4383109651 hasPrimaryLocation W43831096511 @default.
- W4383109651 hasRelatedWork W2064822140 @default.
- W4383109651 hasRelatedWork W2187813989 @default.
- W4383109651 hasRelatedWork W2512408704 @default.
- W4383109651 hasRelatedWork W2801581279 @default.
- W4383109651 hasRelatedWork W2888787938 @default.
- W4383109651 hasRelatedWork W2980631948 @default.
- W4383109651 hasRelatedWork W2993341046 @default.
- W4383109651 hasRelatedWork W3080686208 @default.
- W4383109651 hasRelatedWork W3199034818 @default.
- W4383109651 hasRelatedWork W3206210205 @default.
- W4383109651 isParatext "false" @default.
- W4383109651 isRetracted "false" @default.
- W4383109651 workType "article" @default.