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Titlebook: Evolution in Computational Intelligence; Proceedings of the 1 Vikrant Bhateja,Xin-She Yang,Ranjita Das Conference proceedings 2023 The Edit

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樓主: VERSE
11#
發(fā)表于 2025-3-23 13:32:33 | 只看該作者
,Prediction of?Air Quality Using Machine Learning,These experiments successfully resolve limitations like data instability, overfitting, and multicollinearity. RFR, XGBoost, and ANN perform better and help to resolve air prediction issues, and specifically, ANN outperforms all. Results and discussion of this paper provide a holistic view of methods
12#
發(fā)表于 2025-3-23 16:03:34 | 只看該作者
13#
發(fā)表于 2025-3-23 19:30:47 | 只看該作者
14#
發(fā)表于 2025-3-24 00:32:10 | 只看該作者
Romy Escher,Melanie Walter-Roggdes two pipelines and an ensemble method. In the first pipeline, YOLOv5 and EfficientNet are used. In second pipeline, the Faster R-CNN model is used. Through the Weighted box fusion method, the fused predictions are created from pipeline results. The final detection results illustrate confidence sc
15#
發(fā)表于 2025-3-24 03:39:00 | 只看該作者
16#
發(fā)表于 2025-3-24 07:22:16 | 只看該作者
Environmental Philosophy of Buddhism,is tested on MURA dataset, a large public dataset provided by Stanford ML Group. Our model achieved a cohen’s kappa score 0.739 with precision of 0.885 and recall of 0.854, which is higher than many existing approaches such as densenet169 and ensemble200 model.
17#
發(fā)表于 2025-3-24 14:19:49 | 只看該作者
18#
發(fā)表于 2025-3-24 18:28:47 | 只看該作者
Gottfried Grabner,Claire Richard methods for Indian vehicle number plates. While the usage of deep learning models for generation of training data has been reported with success, it further limits the interpretability of the overall solution by adding another deep neural network in the ANPR system pipeline (Linardatos et al. in En
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發(fā)表于 2025-3-24 20:22:02 | 只看該作者
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發(fā)表于 2025-3-25 02:59:19 | 只看該作者
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