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Titlebook: Artificial Intelligence and Soft Computing; 18th International C Leszek Rutkowski,Rafa? Scherer,Jacek M. Zurada Conference proceedings 2019

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發(fā)表于 2025-3-26 21:32:23 | 只看該作者
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發(fā)表于 2025-3-27 03:04:54 | 只看該作者
Control of Dissolving Cytostatics Using Computer Image Analysissystem for the automatic preparation of cytostatics. The idea consists in rotating the vessel, stopping it, and analysing a sequence of images while the particles in the solution are still moving. We proposed 30 descriptors of the variability of statistical properties of image noise. We used PCA for
33#
發(fā)表于 2025-3-27 05:36:06 | 只看該作者
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發(fā)表于 2025-3-27 10:51:44 | 只看該作者
Ensemble of Classifiers Using CNN and Hand-Crafted Features for Depth-Based Action Recognitionape in single depth maps. For each class we train a separate one-against-all convolutional neural network to extract class-specific features. The actions are represented by multivariate time-series of such CNN-based frame features for which we calculate statistical features. For the non-zero pixels
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發(fā)表于 2025-3-27 17:00:07 | 只看該作者
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發(fā)表于 2025-3-27 19:11:11 | 只看該作者
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發(fā)表于 2025-3-27 23:11:16 | 只看該作者
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發(fā)表于 2025-3-28 04:42:38 | 只看該作者
Narrow Band-Based Detection of Basal Ganglia Subterritoriesrmanent stimulating electrode is placed within selected part of the BG. One of the methods of localization of the selected part of the BG is based upon analysis of recordings obtained from BG using thin neurosurgical microelectrodes. This paper shows method for obtaining the minimal frequency ranges
39#
發(fā)表于 2025-3-28 07:36:49 | 只看該作者
Realizations of the Statistical Reconstruction Method Based on the Continuous-to-Continuous Data Moddel using both CPU and GPU hardware approaches. Data were obtained from a commercial computer tomography device which were saved in DICOM standard file. The implemented reconstruction algorithm is formulated taking in two consideration the statistical properties of signals obtained by x-ray CT and t
40#
發(fā)表于 2025-3-28 11:57:31 | 只看該作者
Classification of Transposable Elements by Convolutional Neural Networkselements on the organisms. Here we present a method that classifies TEs by training a CNN to label them in classes, orders and superfamilies. Unlike previous works in the literature, the proposed method does not search for similarities to classify the sequences or use traditional machine learning cl
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