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Directedness transfer learning resulted in precision of 95.29 ± 1.46 (µ ± σ)% among all networks. Comparison with nine different deep networks pretrained on ImageNet database was included. Considering leave-one-subject-out cross-validation, classification outcomes demonstrated that directedness transfer learning via Alexnet yields a promising performance showing 97.17% precision and outperforming other approaches. EEG data from patients diagnosed with delirium (N = 15) recorded using a 10-channel BrainStatus device were used for this analysis. Transferring knowledge was done using bi-dimensional transformation capitalizing on the direction and propagation pattern of one channel influence on the others. In this respect, this work aimed to encode spectral-phase information into a bi-dimensional map.
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They also provide the possibility of achieving discriminative features by employing transfer learning paradigm in cases dealing with highly small training datasets. These series-to-image transformations have benefits including better noise robustness and more options regarding augmentation.
#ARTIFICIAL ACADEMY 2 LAG DURING CONVO SERIES#
Reformulating time-series data as visual clues and assigning visual patterns to different categories help the classification of time series in a wide range of applications. The problem with processing of multivariate/multichannel signals lies in adapting of existing classifiers on data.
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