Matches in SemOpenAlex for { <https://semopenalex.org/work/W2973102739> ?p ?o ?g. }
Showing items 1 to 91 of
91
with 100 items per page.
- W2973102739 abstract "Over the past few years, Generative Adversarial Networks (GANs) have been receiving attention from image and time series domain. In this work, we propose a novel sequence based generative model to generate ECG samples for enhancing emotion state classification. Firstly, emotional related features are extracted to represent emotion state in ECG record. Secondly, random forest and support vector machine are trained to classify arousal and valence states. Then proposed generative model is applied to generate ECG sample with the corresponding emotion state label. Finally, synthetic data is used to augment the original training set for another classification. Our proposed model classifying emotion state in both arousal and valence domain. With synthetic augmented dataset, the average classification accuracy increases around 5% compared with using only original data. The result demonstrates the notable effectiveness of our generative model for enhancing emotion state classification." @default.
- W2973102739 created "2019-09-19" @default.
- W2973102739 creator A5016467822 @default.
- W2973102739 creator A5026303573 @default.
- W2973102739 creator A5056988611 @default.
- W2973102739 creator A5086905984 @default.
- W2973102739 date "2019-07-12" @default.
- W2973102739 modified "2023-10-03" @default.
- W2973102739 title "EmotionalGAN" @default.
- W2973102739 cites W1990527621 @default.
- W2973102739 cites W1993368541 @default.
- W2973102739 cites W2036309320 @default.
- W2973102739 cites W2077697924 @default.
- W2973102739 cites W2122098299 @default.
- W2973102739 cites W2125011751 @default.
- W2973102739 cites W2144938397 @default.
- W2973102739 cites W2469452823 @default.
- W2973102739 cites W2534920278 @default.
- W2973102739 cites W2547146855 @default.
- W2973102739 cites W2579550061 @default.
- W2973102739 cites W2588843192 @default.
- W2973102739 cites W2624419954 @default.
- W2973102739 cites W2775675437 @default.
- W2973102739 cites W2793420802 @default.
- W2973102739 cites W2904022596 @default.
- W2973102739 cites W2963942586 @default.
- W2973102739 doi "https://doi.org/10.1145/3349341.3349422" @default.
- W2973102739 hasPublicationYear "2019" @default.
- W2973102739 type Work @default.
- W2973102739 sameAs 2973102739 @default.
- W2973102739 citedByCount "14" @default.
- W2973102739 countsByYear W29731027392020 @default.
- W2973102739 countsByYear W29731027392021 @default.
- W2973102739 countsByYear W29731027392022 @default.
- W2973102739 countsByYear W29731027392023 @default.
- W2973102739 crossrefType "proceedings-article" @default.
- W2973102739 hasAuthorship W2973102739A5016467822 @default.
- W2973102739 hasAuthorship W2973102739A5026303573 @default.
- W2973102739 hasAuthorship W2973102739A5056988611 @default.
- W2973102739 hasAuthorship W2973102739A5086905984 @default.
- W2973102739 hasConcept C119857082 @default.
- W2973102739 hasConcept C121332964 @default.
- W2973102739 hasConcept C12267149 @default.
- W2973102739 hasConcept C153180895 @default.
- W2973102739 hasConcept C154945302 @default.
- W2973102739 hasConcept C15744967 @default.
- W2973102739 hasConcept C167966045 @default.
- W2973102739 hasConcept C168900304 @default.
- W2973102739 hasConcept C169258074 @default.
- W2973102739 hasConcept C169760540 @default.
- W2973102739 hasConcept C206310091 @default.
- W2973102739 hasConcept C2777438025 @default.
- W2973102739 hasConcept C36951298 @default.
- W2973102739 hasConcept C39890363 @default.
- W2973102739 hasConcept C41008148 @default.
- W2973102739 hasConcept C51632099 @default.
- W2973102739 hasConcept C62520636 @default.
- W2973102739 hasConceptScore W2973102739C119857082 @default.
- W2973102739 hasConceptScore W2973102739C121332964 @default.
- W2973102739 hasConceptScore W2973102739C12267149 @default.
- W2973102739 hasConceptScore W2973102739C153180895 @default.
- W2973102739 hasConceptScore W2973102739C154945302 @default.
- W2973102739 hasConceptScore W2973102739C15744967 @default.
- W2973102739 hasConceptScore W2973102739C167966045 @default.
- W2973102739 hasConceptScore W2973102739C168900304 @default.
- W2973102739 hasConceptScore W2973102739C169258074 @default.
- W2973102739 hasConceptScore W2973102739C169760540 @default.
- W2973102739 hasConceptScore W2973102739C206310091 @default.
- W2973102739 hasConceptScore W2973102739C2777438025 @default.
- W2973102739 hasConceptScore W2973102739C36951298 @default.
- W2973102739 hasConceptScore W2973102739C39890363 @default.
- W2973102739 hasConceptScore W2973102739C41008148 @default.
- W2973102739 hasConceptScore W2973102739C51632099 @default.
- W2973102739 hasConceptScore W2973102739C62520636 @default.
- W2973102739 hasLocation W29731027391 @default.
- W2973102739 hasOpenAccess W2973102739 @default.
- W2973102739 hasPrimaryLocation W29731027391 @default.
- W2973102739 hasRelatedWork W2010827016 @default.
- W2973102739 hasRelatedWork W2129455854 @default.
- W2973102739 hasRelatedWork W2151942619 @default.
- W2973102739 hasRelatedWork W2154129660 @default.
- W2973102739 hasRelatedWork W2296162375 @default.
- W2973102739 hasRelatedWork W2386075208 @default.
- W2973102739 hasRelatedWork W2967180365 @default.
- W2973102739 hasRelatedWork W3195168932 @default.
- W2973102739 hasRelatedWork W4312250044 @default.
- W2973102739 hasRelatedWork W560265342 @default.
- W2973102739 isParatext "false" @default.
- W2973102739 isRetracted "false" @default.
- W2973102739 magId "2973102739" @default.
- W2973102739 workType "article" @default.