FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3215314811> ?p ?o ?g. }

• W3215314811 endingPage "103574" @default.
• W3215314811 startingPage "103574" @default.
• W3215314811 abstract "This paper develops a hybrid neural network architecture named multi-class factorization machine with deep neural network (multi-FMDNN) to fuse multi-source information for the automatic post-earthquake building damage evaluation. The novel algorithm is a combination of the factorization machine (FM) and the deep neural network (DNN), which adopts the one-vs-all strategy to fuse results from multiple base classifiers. 39,352 buildings affected by the 2015 Nepal earthquake are taken as a case study to validate the effectiveness of the proposed multi-FMDNN. Experimental results confirm that the proposed model outperforms over many other popular machine learning methods due to the powerful feature learning ability, ultimately reaching an overall accuracy, macro F1-score, and weighted F1-score in the value of 0.703, 0.737, and 0.702, respectively. Features associated with building structural characteristics are found to contribute more to classifying damage grades precisely. Besides, data preprocessing for data cleaning, encoding, and transformation is a necessary step to bring additional performance enhancement. For significance in the knowledge aspect, a novel multi-FMDNN algorithm is developed, which is superior in extracting both low- and high-order feature representation automatically from large volumes of destroyed buildings-related data and learning the optimal feature interactions simultaneously to pursue more accurate classification. For significance in the application aspect, the predicted results provide deep insights into a better understanding of the building vulnerability in seismic areas and inform data-driven decisions in disaster relief efforts. A promising future scope is to make full use of the available pre-event data along with some post-event data, which is possible to return fairly promising predictions and reduce the burden in earthquake field investigations for rapid responses. In future work, advanced techniques associated with data augment, hyperparameter optimization, and others will be implemented to constantly improve the overall accuracy and generalizability of the prediction model." @default.
• W3215314811 created "2021-12-06" @default.
• W3215314811 creator A5056869509 @default.
• W3215314811 creator A5067890765 @default.
• W3215314811 date "2022-02-01" @default.
• W3215314811 modified "2023-10-14" @default.
• W3215314811 title "Information fusion for automated post-disaster building damage evaluation using deep neural network" @default.
• W3215314811 cites W1558956117 @default.
• W3215314811 cites W1998532807 @default.
• W3215314811 cites W2002029318 @default.
• W3215314811 cites W2004615188 @default.
• W3215314811 cites W2027000042 @default.
• W3215314811 cites W2046194167 @default.
• W3215314811 cites W2050391704 @default.
• W3215314811 cites W2067751945 @default.
• W3215314811 cites W2068259714 @default.
• W3215314811 cites W2070728363 @default.
• W3215314811 cites W2119193974 @default.
• W3215314811 cites W2128880484 @default.
• W3215314811 cites W2146194630 @default.
• W3215314811 cites W2415326922 @default.
• W3215314811 cites W2593771152 @default.
• W3215314811 cites W2604662567 @default.
• W3215314811 cites W2612512196 @default.
• W3215314811 cites W2623632544 @default.
• W3215314811 cites W2756789966 @default.
• W3215314811 cites W2775484290 @default.
• W3215314811 cites W2791918456 @default.
• W3215314811 cites W2795158543 @default.
• W3215314811 cites W2801492038 @default.
• W3215314811 cites W2805257321 @default.
• W3215314811 cites W2900511825 @default.
• W3215314811 cites W2908667960 @default.
• W3215314811 cites W2915981391 @default.
• W3215314811 cites W2946204349 @default.
• W3215314811 cites W2963974674 @default.
• W3215314811 cites W2973489423 @default.
• W3215314811 cites W2974551902 @default.
• W3215314811 cites W2997760649 @default.
• W3215314811 cites W2998158911 @default.
• W3215314811 cites W3005631159 @default.
• W3215314811 cites W3011931516 @default.
• W3215314811 cites W3016453002 @default.
• W3215314811 cites W3019482698 @default.
• W3215314811 cites W3082708850 @default.
• W3215314811 cites W3093744263 @default.
• W3215314811 cites W3113152109 @default.
• W3215314811 cites W3114136449 @default.
• W3215314811 cites W3183217260 @default.
• W3215314811 doi "https://doi.org/10.1016/j.scs.2021.103574" @default.
• W3215314811 hasPublicationYear "2022" @default.
• W3215314811 type Work @default.
• W3215314811 sameAs 3215314811 @default.
• W3215314811 citedByCount "9" @default.
• W3215314811 countsByYear W32153148112022 @default.
• W3215314811 countsByYear W32153148112023 @default.
• W3215314811 crossrefType "journal-article" @default.
• W3215314811 hasAuthorship W3215314811A5056869509 @default.
• W3215314811 hasAuthorship W3215314811A5067890765 @default.
• W3215314811 hasConcept C10551718 @default.
• W3215314811 hasConcept C108583219 @default.
• W3215314811 hasConcept C119599485 @default.
• W3215314811 hasConcept C119857082 @default.
• W3215314811 hasConcept C124101348 @default.
• W3215314811 hasConcept C127413603 @default.
• W3215314811 hasConcept C138885662 @default.
• W3215314811 hasConcept C141353440 @default.
• W3215314811 hasConcept C154945302 @default.
• W3215314811 hasConcept C199360897 @default.
• W3215314811 hasConcept C2776401178 @default.
• W3215314811 hasConcept C2778012447 @default.
• W3215314811 hasConcept C2778827112 @default.
• W3215314811 hasConcept C34736171 @default.
• W3215314811 hasConcept C38652104 @default.
• W3215314811 hasConcept C41008148 @default.
• W3215314811 hasConcept C41895202 @default.
• W3215314811 hasConcept C50644808 @default.
• W3215314811 hasConcept C95713431 @default.
• W3215314811 hasConceptScore W3215314811C10551718 @default.
• W3215314811 hasConceptScore W3215314811C108583219 @default.
• W3215314811 hasConceptScore W3215314811C119599485 @default.
• W3215314811 hasConceptScore W3215314811C119857082 @default.
• W3215314811 hasConceptScore W3215314811C124101348 @default.
• W3215314811 hasConceptScore W3215314811C127413603 @default.
• W3215314811 hasConceptScore W3215314811C138885662 @default.
• W3215314811 hasConceptScore W3215314811C141353440 @default.
• W3215314811 hasConceptScore W3215314811C154945302 @default.
• W3215314811 hasConceptScore W3215314811C199360897 @default.
• W3215314811 hasConceptScore W3215314811C2776401178 @default.
• W3215314811 hasConceptScore W3215314811C2778012447 @default.
• W3215314811 hasConceptScore W3215314811C2778827112 @default.
• W3215314811 hasConceptScore W3215314811C34736171 @default.
• W3215314811 hasConceptScore W3215314811C38652104 @default.
• W3215314811 hasConceptScore W3215314811C41008148 @default.
• W3215314811 hasConceptScore W3215314811C41895202 @default.
• W3215314811 hasConceptScore W3215314811C50644808 @default.
• W3215314811 hasConceptScore W3215314811C95713431 @default.
• W3215314811 hasFunder F4320320751 @default.

• next