FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2022105667> ?p ?o ?g. }

• W2022105667 endingPage "311" @default.
• W2022105667 startingPage "304" @default.
• W2022105667 abstract "In this study, the accuracy of mathematical techniques such as multiple linear regression, clustering, decision trees (CART) and neural networks was evaluated to predict Young’s modulus, compressive stress at 30% strain and instantaneous recovery velocity of cork. Physical properties, namely test direction, density, porosity and pore number, as well as test direction were used as input. The better model was achieved when a classification problem was performed. Only compressive stress at 30% strain can be predicted with neural networks with an error rate of about 20%. The prediction of Young’s modulus and instantaneous recovery velocity led to unacceptably high error rates due to the heterogeneity of the material." @default.
• W2022105667 created "2016-06-24" @default.
• W2022105667 creator A5007114572 @default.
• W2022105667 creator A5059302926 @default.
• W2022105667 creator A5066735906 @default.
• W2022105667 creator A5072423626 @default.
• W2022105667 creator A5083001954 @default.
• W2022105667 creator A5006341700 @default.
• W2022105667 date "2015-10-01" @default.
• W2022105667 modified "2023-10-15" @default.
• W2022105667 title "Prediction of mechanical strength of cork under compression using machine learning techniques" @default.
• W2022105667 cites W1599621052 @default.
• W2022105667 cites W1605550977 @default.
• W2022105667 cites W1844204632 @default.
• W2022105667 cites W1966279506 @default.
• W2022105667 cites W1969269752 @default.
• W2022105667 cites W1979367237 @default.
• W2022105667 cites W1985348160 @default.
• W2022105667 cites W1987272966 @default.
• W2022105667 cites W1989255432 @default.
• W2022105667 cites W1992419399 @default.
• W2022105667 cites W1995341919 @default.
• W2022105667 cites W2003986922 @default.
• W2022105667 cites W2011430131 @default.
• W2022105667 cites W2013784169 @default.
• W2022105667 cites W2015867391 @default.
• W2022105667 cites W2016495284 @default.
• W2022105667 cites W2018218250 @default.
• W2022105667 cites W2030278607 @default.
• W2022105667 cites W2036319875 @default.
• W2022105667 cites W2039231387 @default.
• W2022105667 cites W2050223101 @default.
• W2022105667 cites W2058629189 @default.
• W2022105667 cites W2089787175 @default.
• W2022105667 cites W2091877350 @default.
• W2022105667 cites W2093717302 @default.
• W2022105667 cites W2096514774 @default.
• W2022105667 cites W2099191207 @default.
• W2022105667 cites W2118321589 @default.
• W2022105667 cites W2118748485 @default.
• W2022105667 cites W2122342483 @default.
• W2022105667 cites W2160689147 @default.
• W2022105667 doi "https://doi.org/10.1016/j.matdes.2015.03.038" @default.
• W2022105667 hasPublicationYear "2015" @default.
• W2022105667 type Work @default.
• W2022105667 sameAs 2022105667 @default.
• W2022105667 citedByCount "31" @default.
• W2022105667 countsByYear W20221056672016 @default.
• W2022105667 countsByYear W20221056672017 @default.
• W2022105667 countsByYear W20221056672018 @default.
• W2022105667 countsByYear W20221056672019 @default.
• W2022105667 countsByYear W20221056672020 @default.
• W2022105667 countsByYear W20221056672021 @default.
• W2022105667 countsByYear W20221056672022 @default.
• W2022105667 countsByYear W20221056672023 @default.
• W2022105667 crossrefType "journal-article" @default.
• W2022105667 hasAuthorship W2022105667A5006341700 @default.
• W2022105667 hasAuthorship W2022105667A5007114572 @default.
• W2022105667 hasAuthorship W2022105667A5059302926 @default.
• W2022105667 hasAuthorship W2022105667A5066735906 @default.
• W2022105667 hasAuthorship W2022105667A5072423626 @default.
• W2022105667 hasAuthorship W2022105667A5083001954 @default.
• W2022105667 hasConcept C105795698 @default.
• W2022105667 hasConcept C138885662 @default.
• W2022105667 hasConcept C154945302 @default.
• W2022105667 hasConcept C159985019 @default.
• W2022105667 hasConcept C180016635 @default.
• W2022105667 hasConcept C192562407 @default.
• W2022105667 hasConcept C193867417 @default.
• W2022105667 hasConcept C196029304 @default.
• W2022105667 hasConcept C204366326 @default.
• W2022105667 hasConcept C21036866 @default.
• W2022105667 hasConcept C2777899904 @default.
• W2022105667 hasConcept C30407753 @default.
• W2022105667 hasConcept C33923547 @default.
• W2022105667 hasConcept C41008148 @default.
• W2022105667 hasConcept C41895202 @default.
• W2022105667 hasConcept C43486711 @default.
• W2022105667 hasConcept C48921125 @default.
• W2022105667 hasConcept C50644808 @default.
• W2022105667 hasConcept C518456604 @default.
• W2022105667 hasConcept C6648577 @default.
• W2022105667 hasConcept C73555534 @default.
• W2022105667 hasConceptScore W2022105667C105795698 @default.
• W2022105667 hasConceptScore W2022105667C138885662 @default.
• W2022105667 hasConceptScore W2022105667C154945302 @default.
• W2022105667 hasConceptScore W2022105667C159985019 @default.
• W2022105667 hasConceptScore W2022105667C180016635 @default.
• W2022105667 hasConceptScore W2022105667C192562407 @default.
• W2022105667 hasConceptScore W2022105667C193867417 @default.
• W2022105667 hasConceptScore W2022105667C196029304 @default.
• W2022105667 hasConceptScore W2022105667C204366326 @default.
• W2022105667 hasConceptScore W2022105667C21036866 @default.
• W2022105667 hasConceptScore W2022105667C2777899904 @default.
• W2022105667 hasConceptScore W2022105667C30407753 @default.
• W2022105667 hasConceptScore W2022105667C33923547 @default.
• W2022105667 hasConceptScore W2022105667C41008148 @default.
• W2022105667 hasConceptScore W2022105667C41895202 @default.

• next