FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220732194> ?p ?o ?g. }

• W4220732194 endingPage "24" @default.
• W4220732194 startingPage "1" @default.
• W4220732194 abstract "Crime poses a major threat to human life and property, which has been recognized as one of the most crucial problems in our society. Predicting the number of crime incidents in each region of a city before they happen is of great importance to fight against crime. There has been a great deal of research focused on crime prediction, ranging from introducing diversified data sources to exploring various prediction models. However, most of the existing approaches fail to offer fine-scale prediction results and take little notice of the intricate spatial-temporal-categorical correlations contained in crime incidents. In this article, we propose a tailor-made framework called CrimeTensor to predict the number of crime incidents belonging to different categories within each target region via tensor learning with spatiotemporal consistency. In particular, we model the crime data as a tensor and present an objective function which tries to take full advantage of the spatial, temporal, and categorical correlations contained in crime incidents. Moreover, a well-designed optimization algorithm which transforms the objective into a compact form and then applies CP decomposition to find the optimal solution is elaborated to solve the objective function. Furthermore, we develop an enhanced framework which takes a set of pre-selected regions to conduct prediction so as to further improve the computational efficiency of the optimization algorithm. Finally, extensive experiments are performed on both proprietary and public datasets and our framework significantly outperforms all the baselines in terms of each evaluation metric." @default.
• W4220732194 created "2022-04-03" @default.
• W4220732194 creator A5027857708 @default.
• W4220732194 creator A5050609316 @default.
• W4220732194 creator A5063565261 @default.
• W4220732194 creator A5081342921 @default.
• W4220732194 date "2022-03-25" @default.
• W4220732194 modified "2023-09-29" @default.
• W4220732194 title "<scp>CrimeTensor</scp> : Fine-Scale Crime Prediction via Tensor Learning with Spatiotemporal Consistency" @default.
• W4220732194 cites W1544274373 @default.
• W4220732194 cites W1582826279 @default.
• W4220732194 cites W1987971958 @default.
• W4220732194 cites W1999136078 @default.
• W4220732194 cites W2024165284 @default.
• W4220732194 cites W2035770620 @default.
• W4220732194 cites W2038420726 @default.
• W4220732194 cites W2045377163 @default.
• W4220732194 cites W2123906784 @default.
• W4220732194 cites W2128338591 @default.
• W4220732194 cites W2147876157 @default.
• W4220732194 cites W2607541615 @default.
• W4220732194 cites W2767949765 @default.
• W4220732194 cites W2774459974 @default.
• W4220732194 cites W2775402959 @default.
• W4220732194 cites W2779342116 @default.
• W4220732194 cites W2780029216 @default.
• W4220732194 cites W2796062295 @default.
• W4220732194 cites W2802119699 @default.
• W4220732194 cites W2864767203 @default.
• W4220732194 cites W2884502219 @default.
• W4220732194 cites W2885650385 @default.
• W4220732194 cites W2895806569 @default.
• W4220732194 cites W2901339881 @default.
• W4220732194 cites W2907002026 @default.
• W4220732194 cites W2908786777 @default.
• W4220732194 cites W2911535719 @default.
• W4220732194 cites W2914236597 @default.
• W4220732194 cites W2918050465 @default.
• W4220732194 cites W2920329036 @default.
• W4220732194 cites W2942843559 @default.
• W4220732194 cites W2944187290 @default.
• W4220732194 cites W2945490408 @default.
• W4220732194 cites W2963909158 @default.
• W4220732194 cites W2980455371 @default.
• W4220732194 cites W2995220619 @default.
• W4220732194 cites W3012735076 @default.
• W4220732194 cites W3088611441 @default.
• W4220732194 cites W3141797743 @default.
• W4220732194 cites W3157597645 @default.
• W4220732194 cites W4232533539 @default.
• W4220732194 cites W4292363360 @default.
• W4220732194 doi "https://doi.org/10.1145/3501807" @default.
• W4220732194 hasPublicationYear "2022" @default.
• W4220732194 type Work @default.
• W4220732194 citedByCount "3" @default.
• W4220732194 countsByYear W42207321942022 @default.
• W4220732194 countsByYear W42207321942023 @default.
• W4220732194 crossrefType "journal-article" @default.
• W4220732194 hasAuthorship W4220732194A5027857708 @default.
• W4220732194 hasAuthorship W4220732194A5050609316 @default.
• W4220732194 hasAuthorship W4220732194A5063565261 @default.
• W4220732194 hasAuthorship W4220732194A5081342921 @default.
• W4220732194 hasConcept C111472728 @default.
• W4220732194 hasConcept C119857082 @default.
• W4220732194 hasConcept C124101348 @default.
• W4220732194 hasConcept C138885662 @default.
• W4220732194 hasConcept C14036430 @default.
• W4220732194 hasConcept C154945302 @default.
• W4220732194 hasConcept C155281189 @default.
• W4220732194 hasConcept C162324750 @default.
• W4220732194 hasConcept C176217482 @default.
• W4220732194 hasConcept C177264268 @default.
• W4220732194 hasConcept C17744445 @default.
• W4220732194 hasConcept C189950617 @default.
• W4220732194 hasConcept C199360897 @default.
• W4220732194 hasConcept C199539241 @default.
• W4220732194 hasConcept C202444582 @default.
• W4220732194 hasConcept C205649164 @default.
• W4220732194 hasConcept C21547014 @default.
• W4220732194 hasConcept C2776436953 @default.
• W4220732194 hasConcept C2778755073 @default.
• W4220732194 hasConcept C2779913896 @default.
• W4220732194 hasConcept C33923547 @default.
• W4220732194 hasConcept C41008148 @default.
• W4220732194 hasConcept C5274069 @default.
• W4220732194 hasConcept C58640448 @default.
• W4220732194 hasConcept C78458016 @default.
• W4220732194 hasConcept C86803240 @default.
• W4220732194 hasConceptScore W4220732194C111472728 @default.
• W4220732194 hasConceptScore W4220732194C119857082 @default.
• W4220732194 hasConceptScore W4220732194C124101348 @default.
• W4220732194 hasConceptScore W4220732194C138885662 @default.
• W4220732194 hasConceptScore W4220732194C14036430 @default.
• W4220732194 hasConceptScore W4220732194C154945302 @default.
• W4220732194 hasConceptScore W4220732194C155281189 @default.
• W4220732194 hasConceptScore W4220732194C162324750 @default.
• W4220732194 hasConceptScore W4220732194C176217482 @default.
• W4220732194 hasConceptScore W4220732194C177264268 @default.

• next