Yayın: Assessing household damages using multi-model deep learning pipeline
| dc.contributor.author | KIYIKÇI, Fatih | |
| dc.contributor.author | KOŞAR, Enes | |
| dc.contributor.author | BEKİN, Mehmet Eren | |
| dc.contributor.author | CUNEDİOĞLU, Hilal Onur | |
| dc.contributor.author | Fatih Abut | |
| dc.contributor.author | Fatih AKAY | |
| dc.contributor.orcid | 0000-0003-3949-5680 | |
| dc.contributor.orcid | 0000-0002-4782-1768 | |
| dc.contributor.orcid | 0000-0001-9757-2483 | |
| dc.contributor.orcid | 0000-0002-9024-250X | |
| dc.contributor.orcid | 0000-0001-5876-4116 | |
| dc.date.accessioned | 2025-11-13T12:04:40Z | |
| dc.date.issued | 2022-06-25 | |
| dc.identifier.doi | https://doi.org/10.26701/ems.1031595 | |
| dc.identifier.endpage | 142 | |
| dc.identifier.issn | 2587-1110 | |
| dc.identifier.issue | 2 | |
| dc.identifier.openalex | W4283163016 | |
| dc.identifier.startpage | 138 | |
| dc.identifier.uri | https://hdl.handle.net/11421/8885 | |
| dc.identifier.uri | https://doi.org/10.26701/ems.1031595 | |
| dc.identifier.volume | 6 | |
| dc.language.iso | en | |
| dc.relation.ispartof | European Mechanical Science | |
| dc.rights | openAccess | |
| dc.subject | Damages | |
| dc.subject | Computer science | |
| dc.subject | Segmentation | |
| dc.subject | Pipeline (software) | |
| dc.subject | Convolutional neural network | |
| dc.subject | Task (project management) | |
| dc.subject | Artificial intelligence | |
| dc.subject | Pyramid (geometry) | |
| dc.subject | Deep learning | |
| dc.subject | Intersection (aeronautics) | |
| dc.subject | Binary classification | |
| dc.subject | Market segmentation | |
| dc.subject | Feature (linguistics) | |
| dc.subject | Machine learning | |
| dc.subject | Support vector machine | |
| dc.subject | Engineering | |
| dc.subject | Business | |
| dc.subject | Transport engineering | |
| dc.subject.sdg | 3 | |
| dc.title | Assessing household damages using multi-model deep learning pipeline | |
| dc.type | Article | |
| dspace.entity.type | Publication | |
| local.authorid.openalex | A5085378798 | |
| local.authorid.openalex | A5038922227 | |
| local.authorid.openalex | A5076772916 | |
| local.authorid.openalex | A5089180188 |
