Q2539 (explore) | nanodash

Q2539

Full identifier: https://www.wikidata.org/wiki/Q2539

References

Nanopublication	Part	Subject	Predicate	Object	Published By	Published On
https://w3id.org/sciencelive/np/RA50Jdjkbj... RA50Jdjkbj	links a nanopublication to its pubinfo http://www.nanopub.org/nschema#hasPublicationInfo pubinfo	Q2539	https://w3id.org/np/o/ntemplate/hasLabelFromApi hasLabelFromApi	machine learning	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RADZdyAENx... RADZdyAENx	links a nanopublication to its pubinfo http://www.nanopub.org/nschema#hasPublicationInfo pubinfo	Q2539	https://w3id.org/np/o/ntemplate/hasLabelFromApi hasLabelFromApi	machine learning	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RAxXRIhlur... RAxXRIhlur	links a nanopublication to its pubinfo http://www.nanopub.org/nschema#hasPublicationInfo pubinfo	Q2539	https://w3id.org/np/o/ntemplate/hasLabelFromApi hasLabelFromApi	machine learning	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RAA9LiLSQs... RAA9LiLSQs	links a nanopublication to its pubinfo http://www.nanopub.org/nschema#hasPublicationInfo pubinfo	Q2539	https://w3id.org/np/o/ntemplate/hasLabelFromApi hasLabelFromApi	machine learning	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RA7khdu9Am... RA7khdu9Am	links a nanopublication to its pubinfo http://www.nanopub.org/nschema#hasPublicationInfo pubinfo	Q2539	https://w3id.org/np/o/ntemplate/hasLabelFromApi hasLabelFromApi	machine learning	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RAM3iyQJdh... RAM3iyQJdh	links a nanopublication to its pubinfo http://www.nanopub.org/nschema#hasPublicationInfo pubinfo	Q2539	https://w3id.org/np/o/ntemplate/hasLabelFromApi hasLabelFromApi	machine learning	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RA50Jdjkbj... RA50Jdjkbj	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	http://purl.org/aida/The%20com...the%20Chico%20wildfire%20case. The combination of Dice Loss and Focal Loss as the loss function significantly improved model performance in handling imbalanced datasets, with BiAU-Net achieving a Balanced Accuracy score of 0.965 compared to 0.930 for BiU-Net in the Chico wildfire case.	http://schema.org/about about	Q2539	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RADZdyAENx... RADZdyAENx	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	http://purl.org/aida/The%20BiA...lation%20Coefficient%20values. The BiAU-Net model achieved the highest overall performance compared to state-of-the-art wildfire burnt area detection models, as evidenced by the highest F1-score and Matthews Correlation Coefficient values.	http://schema.org/about about	Q2539	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RAxXRIhlur... RAxXRIhlur	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	http://purl.org/aida/The%20mod...radient%20Descent%20optimizer. The model training utilized a batch size of 6, conducted 50 epochs, initialized the learning rate at 0.1 with scheduled decrease by a factor of 0.5 between the 30th to 50th epochs, and used Stochastic Gradient Descent optimizer.	http://schema.org/about about	Q2539	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RAA9LiLSQs... RAA9LiLSQs	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	http://purl.org/aida/BiAU-Net%...20to%20varying%20environments. BiAU-Net outperformed traditional machine learning models including Support Vector Machines and Random Forest, which require handcrafted engineering and often perform well in training areas but poorly when transferred to different areas due to varying environments.	http://schema.org/about about	Q2539	claude-ai-agent	2026-03-01T17:22:03.000Z
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