Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384563401> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W4384563401 endingPage "100483" @default.
- W4384563401 startingPage "100483" @default.
- W4384563401 abstract "Data preprocessing is a fundamental stage in deep learning modeling and serves as the cornerstone of reliable data analytics. These deep learning models require significant amounts of training data to be effective, with small datasets often resulting in overfitting and poor performance on large datasets. One solution to this problem is parallelization in data modeling, which allows the model to fit the training data more effectively, leading to higher accuracy on large data sets and higher performance overall. In this research, we developed a novel approach that effectively deployed tools such as MPI and MPI4Py from parallel computing to handle data preprocessing and deep learning modeling processes. As a case study, the technique is applied to COVID-19 data from state of Tennessee, USA. Finally, the effectiveness of our approach is demonstrated by comparing it with existing methods without parallel computing concepts like MPI4Py. Our results demonstrate promising outcome for the deployment of parallel computing in modeling to minimize high computational cost." @default.
- W4384563401 created "2023-07-18" @default.
- W4384563401 creator A5008836782 @default.
- W4384563401 creator A5018695597 @default.
- W4384563401 creator A5035916245 @default.
- W4384563401 creator A5038127748 @default.
- W4384563401 creator A5050088214 @default.
- W4384563401 creator A5051401548 @default.
- W4384563401 creator A5058653796 @default.
- W4384563401 creator A5079366815 @default.
- W4384563401 creator A5092486970 @default.
- W4384563401 date "2023-09-01" @default.
- W4384563401 modified "2023-10-16" @default.
- W4384563401 title "Minimization of high computational cost in data preprocessing and modeling using MPI4Py" @default.
- W4384563401 cites W1909422130 @default.
- W4384563401 cites W2050883661 @default.
- W4384563401 cites W2081612620 @default.
- W4384563401 cites W2560000422 @default.
- W4384563401 cites W2787714175 @default.
- W4384563401 cites W3093354410 @default.
- W4384563401 cites W3129254793 @default.
- W4384563401 cites W3136569803 @default.
- W4384563401 cites W3164436820 @default.
- W4384563401 cites W4200269653 @default.
- W4384563401 cites W4229505208 @default.
- W4384563401 cites W4312247616 @default.
- W4384563401 doi "https://doi.org/10.1016/j.mlwa.2023.100483" @default.
- W4384563401 hasPublicationYear "2023" @default.
- W4384563401 type Work @default.
- W4384563401 citedByCount "1" @default.
- W4384563401 countsByYear W43845634012023 @default.
- W4384563401 crossrefType "journal-article" @default.
- W4384563401 hasAuthorship W4384563401A5008836782 @default.
- W4384563401 hasAuthorship W4384563401A5018695597 @default.
- W4384563401 hasAuthorship W4384563401A5035916245 @default.
- W4384563401 hasAuthorship W4384563401A5038127748 @default.
- W4384563401 hasAuthorship W4384563401A5050088214 @default.
- W4384563401 hasAuthorship W4384563401A5051401548 @default.
- W4384563401 hasAuthorship W4384563401A5058653796 @default.
- W4384563401 hasAuthorship W4384563401A5079366815 @default.
- W4384563401 hasAuthorship W4384563401A5092486970 @default.
- W4384563401 hasBestOaLocation W43845634011 @default.
- W4384563401 hasConcept C10551718 @default.
- W4384563401 hasConcept C108583219 @default.
- W4384563401 hasConcept C119857082 @default.
- W4384563401 hasConcept C124101348 @default.
- W4384563401 hasConcept C154945302 @default.
- W4384563401 hasConcept C22019652 @default.
- W4384563401 hasConcept C34736171 @default.
- W4384563401 hasConcept C41008148 @default.
- W4384563401 hasConcept C50644808 @default.
- W4384563401 hasConcept C67186912 @default.
- W4384563401 hasConcept C75684735 @default.
- W4384563401 hasConcept C77088390 @default.
- W4384563401 hasConceptScore W4384563401C10551718 @default.
- W4384563401 hasConceptScore W4384563401C108583219 @default.
- W4384563401 hasConceptScore W4384563401C119857082 @default.
- W4384563401 hasConceptScore W4384563401C124101348 @default.
- W4384563401 hasConceptScore W4384563401C154945302 @default.
- W4384563401 hasConceptScore W4384563401C22019652 @default.
- W4384563401 hasConceptScore W4384563401C34736171 @default.
- W4384563401 hasConceptScore W4384563401C41008148 @default.
- W4384563401 hasConceptScore W4384563401C50644808 @default.
- W4384563401 hasConceptScore W4384563401C67186912 @default.
- W4384563401 hasConceptScore W4384563401C75684735 @default.
- W4384563401 hasConceptScore W4384563401C77088390 @default.
- W4384563401 hasLocation W43845634011 @default.
- W4384563401 hasOpenAccess W4384563401 @default.
- W4384563401 hasPrimaryLocation W43845634011 @default.
- W4384563401 hasRelatedWork W2021866862 @default.
- W4384563401 hasRelatedWork W3014300295 @default.
- W4384563401 hasRelatedWork W3033926838 @default.
- W4384563401 hasRelatedWork W3099765033 @default.
- W4384563401 hasRelatedWork W3198542605 @default.
- W4384563401 hasRelatedWork W4200250512 @default.
- W4384563401 hasRelatedWork W4280545349 @default.
- W4384563401 hasRelatedWork W4285479813 @default.
- W4384563401 hasRelatedWork W4313289316 @default.
- W4384563401 hasRelatedWork W4361732492 @default.
- W4384563401 hasVolume "13" @default.
- W4384563401 isParatext "false" @default.
- W4384563401 isRetracted "false" @default.
- W4384563401 workType "article" @default.