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Turbofan engine health status prediction with heterogeneous ensemble deep neural networks
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| Published in: | JDSA |
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| Format: | Online Article RSS Article |
| Published: |
2026
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| _version_ | 1864030190304231424 |
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| collection | WordPress RSS FRELIP Feed Integration |
| container_title | JDSA |
| description | |
| discipline_display | Engineering & Technology |
| discipline_facet | Engineering & Technology |
| format | Online Article RSS Article |
| genre | Journal Article |
| id | rss_article:7090 |
| institution | FRELIP |
| journal_source_facet | JDSA |
| publishDate | 2026 |
| publishDateSort | 2026 |
| record_format | rss_article |
| spellingShingle | Turbofan engine health status prediction with heterogeneous ensemble deep neural networks Big data and Data science Computer Science & IT Engineering & Technology |
| sub_discipline_display | Computer Science & IT |
| sub_discipline_facet | Computer Science & IT |
| subject_display | Big data and Data science Computer Science & IT Engineering & Technology Big data and Data science Computer Science & IT Engineering & Technology |
| subject_facet | Big data and Data science Computer Science & IT Engineering & Technology |
| title | Turbofan engine health status prediction with heterogeneous ensemble deep neural networks |
| title_auth | Turbofan engine health status prediction with heterogeneous ensemble deep neural networks |
| title_full | Turbofan engine health status prediction with heterogeneous ensemble deep neural networks |
| title_fullStr | Turbofan engine health status prediction with heterogeneous ensemble deep neural networks |
| title_full_unstemmed | Turbofan engine health status prediction with heterogeneous ensemble deep neural networks |
| title_short | Turbofan engine health status prediction with heterogeneous ensemble deep neural networks |
| title_sort | turbofan engine health status prediction with heterogeneous ensemble deep neural networks |
| topic | Big data and Data science Computer Science & IT Engineering & Technology |
| url | https://link.springer.com/article/10.1007/s41060-025-00989-4 |