Matches in Nanopublications for { ?s ?p <https://www.wikidata.org/wiki/Q2539> ?g. }
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- The%20BiAU-Net%20model%20demonstrated%20good%20generalizability%20across%20five%20testing%20areas%20in%20different%20continents%20%28United%20States%2C%20Spain%2C%20Australia%2C%20Indonesia%2C%20and%20Kenya%29%20when%20trained%20on%20a%20single%20wildfire%20event%20in%20California. about Q2539 assertion.
- The%20combination%20of%20Dice%20Loss%20and%20Focal%20Loss%20as%20the%20loss%20function%20significantly%20improved%20model%20performance%20in%20handling%20imbalanced%20datasets%2C%20with%20BiAU-Net%20achieving%20a%20Balanced%20Accuracy%20score%20of%200.965%20compared%20to%200.930%20for%20BiU-Net%20in%20the%20Chico%20wildfire%20case. about Q2539 assertion.
- The%20BiAU-Net%20model%20achieved%20the%20highest%20overall%20performance%20compared%20to%20state-of-the-art%20wildfire%20burnt%20area%20detection%20models%2C%20as%20evidenced%20by%20the%20highest%20F1-score%20and%20Matthews%20Correlation%20Coefficient%20values. about Q2539 assertion.
- The%20model%20training%20utilized%20a%20batch%20size%20of%206%2C%20conducted%2050%20epochs%2C%20initialized%20the%20learning%20rate%20at%200.1%20with%20scheduled%20decrease%20by%20a%20factor%20of%200.5%20between%20the%2030th%20to%2050th%20epochs%2C%20and%20used%20Stochastic%20Gradient%20Descent%20optimizer. about Q2539 assertion.
- BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments. about Q2539 assertion.
- Focal%20Loss%20addresses%20class%20imbalance%20by%20down-weighting%20easy-to-classify%20examples%20and%20placing%20more%20emphasis%20on%20hard-to-classify%20examples%20through%20tunable%20parameters%20%CE%B1%20%28weight%20between%20positive%20and%20negative%20samples%29%20and%20%CE%B3%20%28focusing%20parameter%20for%20adjusting%20loss%20magnitude%29. about Q2539 assertion.
- Data%20augmentation%20methods%20including%20rotating%20from%2010%C2%B0%20left%20to%2010%C2%B0%20right%20with%20mirroring%2C%20reversing%20left%20and%20right%2C%20and%20zooming%20by%20a%20factor%20of%200.85%20were%20implemented%20to%20enhance%20model%20training. about Q2539 assertion.