Matches in SemOpenAlex for { <https://semopenalex.org/work/W4293087622> ?p ?o ?g. }
Showing items 1 to 68 of
68
with 100 items per page.
- W4293087622 abstract "Multivariate time series data is widely used in medical, meteorology, finance analysis and this data consist of miss rate due to the sensor, power failure, etc. For the given multivariate correlated time series data, it is necessary to fill inappropriate missing values to predict or make decisions based on data. Conventional imputation methods do not undergo predicting nearly accurate missing values but makes difference. Advanced techniques for machine learning and deep learning provide better results than standard mathematical methods. In this paper, we propose a combined method Multi-Scaled Deep Networks using alignment of missing values by Variational Auto-Encoders (VAEs) and flattening with non-linear regression by introducing kernel matrices and activating neurons uses that Scaled Exponential Linear Unit (SELU) to bypass dead neurons in the second phase of VAEs which includes decoding. The proposed method multi-kernel scaled deep time series imputation (MSDTSI) introduces high performance compared to the conventional and earlier machine learning algorithms by imputing missing values to predict the mortality rate of the patients through this Physionet Challenge data set. Our proposed model outperforms other models in terms of performance metric Area Under Receiver Operating Characteristic (AUROC) score as 74.8 and Mean Absolute Error(MAE) as 0.44." @default.
- W4293087622 created "2022-08-26" @default.
- W4293087622 creator A5029069930 @default.
- W4293087622 creator A5032170911 @default.
- W4293087622 creator A5054880944 @default.
- W4293087622 date "2022-03-25" @default.
- W4293087622 modified "2023-09-27" @default.
- W4293087622 title "Multi Kernel Scaled Deep Time Series Imputation" @default.
- W4293087622 cites W1480376833 @default.
- W4293087622 cites W1919216911 @default.
- W4293087622 cites W2292760311 @default.
- W4293087622 cites W2588589522 @default.
- W4293087622 cites W2889470180 @default.
- W4293087622 cites W2945703388 @default.
- W4293087622 cites W2964010366 @default.
- W4293087622 cites W3004471907 @default.
- W4293087622 cites W3133618741 @default.
- W4293087622 cites W3202638211 @default.
- W4293087622 doi "https://doi.org/10.1109/icaccs54159.2022.9784998" @default.
- W4293087622 hasPublicationYear "2022" @default.
- W4293087622 type Work @default.
- W4293087622 citedByCount "0" @default.
- W4293087622 crossrefType "proceedings-article" @default.
- W4293087622 hasAuthorship W4293087622A5029069930 @default.
- W4293087622 hasAuthorship W4293087622A5032170911 @default.
- W4293087622 hasAuthorship W4293087622A5054880944 @default.
- W4293087622 hasConcept C108583219 @default.
- W4293087622 hasConcept C11413529 @default.
- W4293087622 hasConcept C114614502 @default.
- W4293087622 hasConcept C119857082 @default.
- W4293087622 hasConcept C124101348 @default.
- W4293087622 hasConcept C151406439 @default.
- W4293087622 hasConcept C154945302 @default.
- W4293087622 hasConcept C161584116 @default.
- W4293087622 hasConcept C33923547 @default.
- W4293087622 hasConcept C41008148 @default.
- W4293087622 hasConcept C58041806 @default.
- W4293087622 hasConcept C74193536 @default.
- W4293087622 hasConcept C9357733 @default.
- W4293087622 hasConceptScore W4293087622C108583219 @default.
- W4293087622 hasConceptScore W4293087622C11413529 @default.
- W4293087622 hasConceptScore W4293087622C114614502 @default.
- W4293087622 hasConceptScore W4293087622C119857082 @default.
- W4293087622 hasConceptScore W4293087622C124101348 @default.
- W4293087622 hasConceptScore W4293087622C151406439 @default.
- W4293087622 hasConceptScore W4293087622C154945302 @default.
- W4293087622 hasConceptScore W4293087622C161584116 @default.
- W4293087622 hasConceptScore W4293087622C33923547 @default.
- W4293087622 hasConceptScore W4293087622C41008148 @default.
- W4293087622 hasConceptScore W4293087622C58041806 @default.
- W4293087622 hasConceptScore W4293087622C74193536 @default.
- W4293087622 hasConceptScore W4293087622C9357733 @default.
- W4293087622 hasLocation W42930876221 @default.
- W4293087622 hasOpenAccess W4293087622 @default.
- W4293087622 hasPrimaryLocation W42930876221 @default.
- W4293087622 hasRelatedWork W1964058989 @default.
- W4293087622 hasRelatedWork W2096740036 @default.
- W4293087622 hasRelatedWork W2159586267 @default.
- W4293087622 hasRelatedWork W2551217493 @default.
- W4293087622 hasRelatedWork W2786769798 @default.
- W4293087622 hasRelatedWork W2890686416 @default.
- W4293087622 hasRelatedWork W3121341047 @default.
- W4293087622 hasRelatedWork W4213225422 @default.
- W4293087622 hasRelatedWork W4309045103 @default.
- W4293087622 hasRelatedWork W1926376776 @default.
- W4293087622 isParatext "false" @default.
- W4293087622 isRetracted "false" @default.
- W4293087622 workType "article" @default.