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Titlebook: Advances in Artificial Intelligence-Empowered Decision Support Systems; Papers in Honour of George A. Tsihrintzis,Maria Virvou,Lakhmi C. J

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21#
發(fā)表于 2025-3-25 07:25:12 | 只看該作者
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25#
發(fā)表于 2025-3-25 21:13:19 | 只看該作者
Zeitdiskrete Signale und Signalparameter,ble real-time performance. High Accuracy, mean Average Precision—mAP (84.5%) and Dice-Score (87.22%) quality metrics and frames per second—FPS (>20 FPS) performance metrics are obtained. Suggestions for the application of the obtained results and further research ways are formulated.
26#
發(fā)表于 2025-3-26 02:57:53 | 只看該作者
Extracting Sentiment from Business News Announcements for More Efficient Decision Makingormation. The investment community can benefit from a system that is able to extract sentiment from regulatory news announcements. In this article, we cover this gap by proposing a system that extracts sentiment from regulatory news announcements for more efficient decision making.
27#
發(fā)表于 2025-3-26 04:33:01 | 只看該作者
28#
發(fā)表于 2025-3-26 10:02:35 | 只看該作者
29#
發(fā)表于 2025-3-26 14:22:54 | 只看該作者
https://doi.org/10.1007/978-3-8348-9927-9lishing houses using related keywords and phrases. The review briefly presents the main findings of each paper and classifies them according to their medical topic of specific interest (diagnosis, breast lesion classification, detection, abnormality classification, estimation of cancer risk, etc.).
30#
發(fā)表于 2025-3-26 16:51:56 | 只看該作者
https://doi.org/10.1007/978-3-8348-9927-9er used for model development and evaluation. Four pre-trained CNN models AlexNet, VGG16, Inception-v3, and ResNet50 that follow transfer learning approaches are used to build the ensemble model. The proposed ensemble model outperforms the state-of-the-art CNN architectures in terms of sensitivity,
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