Method / resourcemodel-written summary

Hybrid soft voting had much shorter inference time than the strong individual transfer learning models.

A claim from Hybrid ensemble framework combining hard and soft voting of deep transfer learning models · 2026

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Hybrid soft voting had much shorter inference time than the strong individual transfer learning models.

Taxon
CucumberCucumis sativuse0
Outcome
inference timee1
Direction
much lesse1
Comparator
individual modele1

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Cucumis sativus
Cucumis sativus · TaxonH. Zell · CC BY-SA 3.0 · Wikimedia Commons ↗

Verbatim

e0

“the Cucumber dataset with multiple diseases”

✓ located verbatim in the source · abstract, characters 822–865
e1

“Hybrid soft voting evaluated similar accuracy to an individual model that is strong accuracy transfer learning trained model, but inference time is much less than that of these strong models”

✓ located verbatim in the source · abstract, characters 1007–1197

Title

Hybrid ensemble framework combining hard and soft voting of deep transfer learning models

Manisha Dumbre; Hiren Dand IET conference proceedings. 2026. doi:10.1049/icp.2025.4754

Other claims from this paper (2)

Record rev-aff670244b8fac6c42b2322af511c7cd4d24a1a10db4a623f4af78436bd43f82 · schema claims-v0.9 · position 2 in the model’s answer