GRATE
发表于 2025-3-26 22:10:01
Sonja Kinner,Thomas C. Lauensteingruency components. It then uses a machine-learning algorithm to optimally combine the resulting components to differentiate between the neural activity of children with dyslexia and controls. We apply our approach to a real EEG dataset involving children with dyslexia and controls. Our findings dem
尖叫
发表于 2025-3-27 01:31:21
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Phenothiazines
发表于 2025-3-27 06:49:24
https://doi.org/10.1007/978-3-319-08165-6 Imbalanced, undersampled (K-Medoids) and oversampled (SMOTE) datasets were used for training EBMs, obtaining their respective feature importance. RBO score was calculated between ranking pairs incrementally increasing the depth by five features, from 1 to 178. All classifiers reached excellent AUC-
水獭
发表于 2025-3-27 10:09:54
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laparoscopy
发表于 2025-3-27 17:04:49
Diseases of the Pleura and the Chest Wall,deep learning model; (2) bridging the 3T-MRI and the 7T-MRI within the same analysis scale; and (3) systematically comparing multiple evaluation indicators, including Brenner, SMD, SMD2, Variance, Vollath, Entropy, and NIQE. The experimental results suggest that the edge, fineness and texture featur
共同给与
发表于 2025-3-27 19:21:38
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finite
发表于 2025-3-28 01:34:22
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小木槌
发表于 2025-3-28 03:50:58
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松紧带
发表于 2025-3-28 08:41:00
COSLETS: Recognition of Emotions Based on EEG Signalsis Brain computer interfaces (BCI). Recognizing emotions based on physiological signals specifically, Electroencephalography (EEG) signals with advancement of BCI applications, has turn into a very popular research topic. In this paper for effective representation of features the proposed model adop
国家明智
发表于 2025-3-28 13:58:25
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