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Titlebook: Advances in Computational Collective Intelligence; 16th International C Ngoc-Than Nguyen,Bogdan Franczyk,Adrianna Kozierki Conference proce

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11#
發(fā)表于 2025-3-23 12:32:28 | 只看該作者
12#
發(fā)表于 2025-3-23 15:50:29 | 只看該作者
CNN Classifier for?Helicobacter Pylori Detection in?Immunohistochemically Stained Gastric WSIs-validation on a set of patients with annotated patches, at sample and diagnosis level, and assessed reproducibility of diagnosis prediction on an independent set of patients. In the 5-fold cross-validation, our CNN model has an overall . of accuracy and . sensitivity in classification of . at samp
13#
發(fā)表于 2025-3-23 20:08:29 | 只看該作者
14#
發(fā)表于 2025-3-23 23:06:16 | 只看該作者
Three-Stage Extraction of?Spatial Relationships Using Markersoint of evaluation reliability and reproducibility of the previous approaches. Several issues related to the SpaceEval dataset were identified and their potential impact on results was discussed. Problems with one of the SOTA systems were identified and discussed. In a broader context, we have ident
15#
發(fā)表于 2025-3-24 02:50:13 | 只看該作者
16#
發(fā)表于 2025-3-24 09:08:17 | 只看該作者
17#
發(fā)表于 2025-3-24 12:11:42 | 只看該作者
1865-0929 tworks and social media, anomalies detection, machine learning, deep learning, digital image processing, artificial intelligence, speech communication, IOT applications, natural language processing, innovative applications in data science..978-3-031-70247-1978-3-031-70248-8Series ISSN 1865-0929 Series E-ISSN 1865-0937
18#
發(fā)表于 2025-3-24 16:31:16 | 只看該作者
Grundlagen des Beton- und Eisenbetonbauesnounced future electric vehicles, as well as different levels of charging infrastructure adopted, to look for the point where the driver behavior is not impacted at all, or only slightly impacted. The move to a larger scale requires adoption of some modification to the agent model, in order to decrease the computational requirements.
19#
發(fā)表于 2025-3-24 20:32:54 | 只看該作者
,Einführung in das Rechtssystem,-based architectures, like Long Short-Term Memory (LSTM), can enhance performance in energy time-series forecasting tasks. To mitigate overfitting, the proposed model incorporates dropout layers to enhance its generalization capability.
20#
發(fā)表于 2025-3-24 23:32:20 | 只看該作者
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