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Titlebook: Biomedical Applications Based on Natural and Artificial Computing; International Work-C José Manuel Ferrández Vicente,José Ramón álvarez-S

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11#
發(fā)表于 2025-3-23 09:47:55 | 只看該作者
12#
發(fā)表于 2025-3-23 17:18:05 | 只看該作者
K. J. Hamelynck,J.-L. Briard,M. J. Pappascomes compared to a task-free interval during which users are instructed to relax. We report that cortical oscillations of . in delta and theta frequencies clearly synchronize at the onset and at the end of a . task, what might be a physiological marker for task switching that could be useful for im
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發(fā)表于 2025-3-23 18:59:23 | 只看該作者
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發(fā)表于 2025-3-23 22:35:14 | 只看該作者
Non-stationary Turbulent Flows,s and afterwards we discuss the results obtained in the above-mentioned scenario. We would like to emphasize that for this scenario the human agents and the robot use limited linguistic words to facilitate coordination. These verbal exchanges are based on a common language (a lexicon plus grammar ru
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發(fā)表于 2025-3-24 06:02:54 | 只看該作者
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發(fā)表于 2025-3-24 09:37:40 | 只看該作者
Biomedical Applications Based on Natural and Artificial ComputingInternational Work-C
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發(fā)表于 2025-3-24 11:22:19 | 只看該作者
18#
發(fā)表于 2025-3-24 17:17:57 | 只看該作者
Koniocortex-Like Network Unsupervised Learning Surpasses Supervised Results on WBCD Breast Cancer Daential for complex tasks with unsupervised learning. Now for the first time, its competitive results are proved in a relevant standard real application that is the objective of state-of-the-art research: the diagnosis of breast cancer data from the Wisconsin Breast Cancer Database.
19#
發(fā)表于 2025-3-24 22:46:43 | 只看該作者
Ongoing Work on Deep Learning for Lung Cancer Predictionlti-center CTA data. First we have normalized in intensity the images. Then we have devised an auto encoder architecture with convolutional layers to obtain a compressed representation of the lung images. These representations are fed as features to a random forest classifier.
20#
發(fā)表于 2025-3-25 02:23:57 | 只看該作者
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