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Importance-driven bottleneck model: a multi-stage deep learning approach for analyzing swelling–shrinkage behavior of wood species and their mechanical properties
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| Published in: | Journal of Wood Science |
|---|---|
| Format: | Online Article RSS Article |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1868553007778496514 |
|---|---|
| collection | WordPress RSS FRELIP Feed Integration |
| container_title | Journal of Wood Science |
| description | |
| discipline_display | Forests and Forestry |
| discipline_facet | Forests and Forestry |
| format | Online Article RSS Article |
| genre | Journal Article |
| id | rss_article:71443 |
| institution | FRELIP |
| journal_source_facet | Journal of Wood Science |
| last_indexed | 2026-06-20T21:29:57.061Z |
| publishDate | 2026 |
| publishDateSort | 2026 |
| record_format | rss_article |
| spellingShingle | Importance-driven bottleneck model: a multi-stage deep learning approach for analyzing swelling–shrinkage behavior of wood species and their mechanical properties Forests and Forestry General Forests and Forestry |
| sub_discipline_display | General |
| sub_discipline_facet | General |
| subject_display | Forests and Forestry General Forests and Forestry |
| subject_facet | Forests and Forestry General Forests and Forestry |
| title | Importance-driven bottleneck model: a multi-stage deep learning approach for analyzing swelling–shrinkage behavior of wood species and their mechanical properties |
| title_alt | Modelo de cuello de botella basado en importancia: un enfoque de aprendizaje profundo multi-etapa para analizar el comportamiento de hinchamiento-contracción de especies de madera y sus propiedades mecánicas Modèle de goulot d'étranglement basé sur l'importance : une approche d'apprentissage profond multi-étapes pour analyser le comportement de gonflement-retrait des espèces de bois et leurs propriétés mécaniques Modelo de gargalo baseado em importância: uma abordagem de aprendizado profundo multiestágio para analisar o comportamento de inchamento-retração de espécies de madeira e suas propriedades mecânicas |
| title_auth | Importance-driven bottleneck model: a multi-stage deep learning approach for analyzing swelling–shrinkage behavior of wood species and their mechanical properties |
| title_es_txt | Modelo de cuello de botella basado en importancia: un enfoque de aprendizaje profundo multi-etapa para analizar el comportamiento de hinchamiento-contracción de especies de madera y sus propiedades mecánicas |
| title_fr_txt | Modèle de goulot d'étranglement basé sur l'importance : une approche d'apprentissage profond multi-étapes pour analyser le comportement de gonflement-retrait des espèces de bois et leurs propriétés mécaniques |
| title_full | Importance-driven bottleneck model: a multi-stage deep learning approach for analyzing swelling–shrinkage behavior of wood species and their mechanical properties |
| title_fullStr | Importance-driven bottleneck model: a multi-stage deep learning approach for analyzing swelling–shrinkage behavior of wood species and their mechanical properties |
| title_full_unstemmed | Importance-driven bottleneck model: a multi-stage deep learning approach for analyzing swelling–shrinkage behavior of wood species and their mechanical properties |
| title_pt_txt | Modelo de gargalo baseado em importância: uma abordagem de aprendizado profundo multiestágio para analisar o comportamento de inchamento-retração de espécies de madeira e suas propriedades mecânicas |
| title_short | Importance-driven bottleneck model: a multi-stage deep learning approach for analyzing swelling–shrinkage behavior of wood species and their mechanical properties |
| title_sort | importance-driven bottleneck model: a multi-stage deep learning approach for analyzing swelling–shrinkage behavior of wood species and their mechanical properties |
| topic | Forests and Forestry General Forests and Forestry |
| url | https://link.springer.com/article/10.1186/s10086-026-02266-9 |