FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2944147810> ?p ?o ?g. }

• W2944147810 endingPage "15" @default.
• W2944147810 startingPage "1" @default.
• W2944147810 abstract "An accurate demand prediction of emergency supplies according to disaster information and historical data is an important research subject in emergency rescue. This study aims at improving supplies demand prediction accuracy under partial data fuzziness and missing. The main contributions of this study are summarized as follows.<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M1><mml:mo stretchy=false>(</mml:mo><mml:mn fontstyle=italic>1</mml:mn><mml:mo stretchy=false>)</mml:mo></mml:math>In view that it is difficult for the turning point of the whitenization weight function to determine fuzzy data, two computational formulas solving “core” of fuzzy interval grey numbers were proposed, and the obtained “core” replaced primary fuzzy information so as to reach the goal of transforming uncertain information into certain information.<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M2><mml:mo stretchy=false>(</mml:mo><mml:mn fontstyle=italic>2</mml:mn><mml:mo stretchy=false>)</mml:mo></mml:math>For partial data missing, the improved grey k-nearest neighbor (GKNN) algorithm was put forward based on grey relation degree and K-nearest neighbor (KNN) algorithm. Weights were introduced in the filling and logic test conditions were added after filling so that filling results were of higher truthfulness and accuracy.<mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M3><mml:mo stretchy=false>(</mml:mo><mml:mn fontstyle=italic>3</mml:mn><mml:mo stretchy=false>)</mml:mo></mml:math>The preprocessed data are input into the improved algorithm based on the genetic algorithm and BP neural networks (GABP) to obtain the demand prediction model. Finally the calculation presents that the prediction accuracy and its stability are improved at the five-group comparative tests of calculated examples of actual disasters. The experiments indicated that the supplies demand prediction model under data fuzziness and missing proposed in this study was of higher prediction accuracy." @default.
• W2944147810 created "2019-05-16" @default.
• W2944147810 creator A5008389208 @default.
• W2944147810 creator A5013747973 @default.
• W2944147810 creator A5025546736 @default.
• W2944147810 creator A5036658104 @default.
• W2944147810 creator A5067703101 @default.
• W2944147810 date "2019-05-09" @default.
• W2944147810 modified "2023-10-14" @default.
• W2944147810 title "Demand Prediction of Emergency Supplies under Fuzzy and Missing Partial Data" @default.
• W2944147810 cites W1515988900 @default.
• W2944147810 cites W1966633870 @default.
• W2944147810 cites W1976750981 @default.
• W2944147810 cites W1985712420 @default.
• W2944147810 cites W1987422606 @default.
• W2944147810 cites W1996538888 @default.
• W2944147810 cites W2042300034 @default.
• W2944147810 cites W2050767557 @default.
• W2944147810 cites W2069134944 @default.
• W2944147810 cites W2091422494 @default.
• W2944147810 cites W2094482313 @default.
• W2944147810 cites W2112702040 @default.
• W2944147810 cites W2183426671 @default.
• W2944147810 cites W2215029138 @default.
• W2944147810 cites W2554897331 @default.
• W2944147810 cites W2606076173 @default.
• W2944147810 cites W2730979008 @default.
• W2944147810 cites W2793743969 @default.
• W2944147810 cites W2795788966 @default.
• W2944147810 cites W2901903463 @default.
• W2944147810 cites W2908069682 @default.
• W2944147810 cites W2911598671 @default.
• W2944147810 cites W773919218 @default.
• W2944147810 doi "https://doi.org/10.1155/2019/6823921" @default.
• W2944147810 hasPublicationYear "2019" @default.
• W2944147810 type Work @default.
• W2944147810 sameAs 2944147810 @default.
• W2944147810 citedByCount "4" @default.
• W2944147810 countsByYear W29441478102021 @default.
• W2944147810 countsByYear W29441478102022 @default.
• W2944147810 countsByYear W29441478102023 @default.
• W2944147810 crossrefType "journal-article" @default.
• W2944147810 hasAuthorship W2944147810A5008389208 @default.
• W2944147810 hasAuthorship W2944147810A5013747973 @default.
• W2944147810 hasAuthorship W2944147810A5025546736 @default.
• W2944147810 hasAuthorship W2944147810A5036658104 @default.
• W2944147810 hasAuthorship W2944147810A5067703101 @default.
• W2944147810 hasBestOaLocation W29441478101 @default.
• W2944147810 hasConcept C11413529 @default.
• W2944147810 hasConcept C119857082 @default.
• W2944147810 hasConcept C124101348 @default.
• W2944147810 hasConcept C154945302 @default.
• W2944147810 hasConcept C41008148 @default.
• W2944147810 hasConcept C58166 @default.
• W2944147810 hasConceptScore W2944147810C11413529 @default.
• W2944147810 hasConceptScore W2944147810C119857082 @default.
• W2944147810 hasConceptScore W2944147810C124101348 @default.
• W2944147810 hasConceptScore W2944147810C154945302 @default.
• W2944147810 hasConceptScore W2944147810C41008148 @default.
• W2944147810 hasConceptScore W2944147810C58166 @default.
• W2944147810 hasFunder F4320321001 @default.
• W2944147810 hasLocation W29441478101 @default.
• W2944147810 hasOpenAccess W2944147810 @default.
• W2944147810 hasPrimaryLocation W29441478101 @default.
• W2944147810 hasRelatedWork W2961085424 @default.
• W2944147810 hasRelatedWork W3046775127 @default.
• W2944147810 hasRelatedWork W3170094116 @default.
• W2944147810 hasRelatedWork W4205958290 @default.
• W2944147810 hasRelatedWork W4285260836 @default.
• W2944147810 hasRelatedWork W4286629047 @default.
• W2944147810 hasRelatedWork W4306321456 @default.
• W2944147810 hasRelatedWork W4306674287 @default.
• W2944147810 hasRelatedWork W4386462264 @default.
• W2944147810 hasRelatedWork W4224009465 @default.
• W2944147810 hasVolume "2019" @default.
• W2944147810 isParatext "false" @default.
• W2944147810 isRetracted "false" @default.
• W2944147810 magId "2944147810" @default.
• W2944147810 workType "article" @default.