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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International C Věra K?rková,Yannis Manolopoulos,Ilias Maglogianni Confe

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21#
發(fā)表于 2025-3-25 07:04:47 | 只看該作者
Classification of Bone Tumor on CT Images Using Deep Convolutional Neural Networkent (ME) strategy for image preprocessing. Owing to the lack of suitable public dataset, we introduce a CT image dataset of bone tumor. Experimental results on this dataset show our SG-CNN and ME strategy improve the classification accuracy obviously.
22#
發(fā)表于 2025-3-25 08:52:49 | 只看該作者
23#
發(fā)表于 2025-3-25 13:21:18 | 只看該作者
Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasoundeach an accuracy substantially superior to previously proposed methods, providing an average reduction of the mean squared error from . (state-of-art) to ., and a relative error reduction from . to .. The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real time clinical use.
24#
發(fā)表于 2025-3-25 19:47:03 | 只看該作者
25#
發(fā)表于 2025-3-25 22:50:41 | 只看該作者
Fernsehgewalt im gesellschaftlichen Kontexttechniques can be used to identify neurons causing collinearity among LMS regression basis. Such neurons may be eliminated or modified to increase the numerical rank of the matrix which is pseudo-inverted while solving LMS regression.
26#
發(fā)表于 2025-3-26 01:26:33 | 只看該作者
https://doi.org/10.1007/978-3-322-97075-6ely independent of the spatial frequency in grating experiments which enables insects to estimate the flight speed in cluttered environments. This also coincides with the behaviour experiments of honeybee flying through tunnels with stripes of different spatial frequencies.
27#
發(fā)表于 2025-3-26 05:03:05 | 只看該作者
28#
發(fā)表于 2025-3-26 11:25:58 | 只看該作者
29#
發(fā)表于 2025-3-26 14:14:04 | 只看該作者
30#
發(fā)表于 2025-3-26 17:46:46 | 只看該作者
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