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LSTM models prediction performance based on the inter-subje..:

Abdelrahman Zaroug (11237388) ; Alessandro Garofolini (11237391) ; Daniel T. H. Lai (2954733)..
https://figshare.com/articles/figure/LSTM_models_prediction_performance_based_on_the_inter-subject_test_for_each_feature_vector_at_PWS_only_/15117987. , 2021

Link: https://doi.org/10.1371/journal.pone.0255597.g004

RT Journal
T1 <Title>LSTM models prediction performance based on the inter-subject test for each feature vector at PWS only</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_15117987&Exemplar=1&LAN=DE
				
A1 Abdelrahman Zaroug (11237388)
A1 Alessandro Garofolini (11237391)
A1 Daniel T. H. Lai (2954733)
A1 Kurt Mudie (5166059)
A1 Rezaul Begg (11237394)
YR 2021
K1 Physiology
K1 Biotechnology
K1 Science Policy
K1 Environmental Sciences not elsewhere classified
K1 Biological Sciences not elsewhere classified
K1 Chemical Sciences not elsewhere classified
K1 Information Systems not elsewhere classified
K1 speed
K1 Term Memory
K1 Long
K1 limb kinematics trajectories
K1 balance loss
K1 i.e
K1 LSTM autoencoders
K1 kinematics time-series data
K1 km.h
K1 future
K1 LA
K1 AV
JF https://figshare.com/articles/figure/LSTM_models_prediction_performance_based_on_the_inter-subject_test_for_each_feature_vector_at_PWS_only_/15117987
LK http://dx.doi.org/https://doi.org/10.1371/journal.pone.0255597.g004
DO https://doi.org/10.1371/journal.pone.0255597.g004
SF ELIB - SuUB Bremen

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