Matches in SemOpenAlex for { <https://semopenalex.org/work/W2912448950> ?p ?o ?g. }
Showing items 1 to 91 of
91
with 100 items per page.
- W2912448950 abstract "Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for demographic sequences. In this paper, we presented the application of a hybrid model that integrates Long-Short Term Memory-Recurrent Neural Network (LSTM-RNN), time series analysis and clustering techniques where time series analysis and clustering methods provide augmentation of sequences data for the training of RNN. Comprehensive characteristics of nations from UN database are used as input to the hybrid model to predict the nations future population. The results prove that, RNN combined with time series and clustering methods has outperformed mere RNN approach without time series and clustering analysis. In addition, the hybrid Time Series and Clustering-RNN with relevant inputs lead to 20% higher predictive accuracy, measured by Root-Mean-Squared Error, compared to results produced by RNN alone." @default.
- W2912448950 created "2019-02-21" @default.
- W2912448950 creator A5002091761 @default.
- W2912448950 creator A5002195074 @default.
- W2912448950 creator A5051861995 @default.
- W2912448950 date "2018-11-01" @default.
- W2912448950 modified "2023-09-24" @default.
- W2912448950 title "Small-Scale Demographic Sequences Projection Based on Time Series Clustering and LSTM-RNN" @default.
- W2912448950 cites W116902681 @default.
- W2912448950 cites W141599973 @default.
- W2912448950 cites W1508403367 @default.
- W2912448950 cites W1543083376 @default.
- W2912448950 cites W1952331239 @default.
- W2912448950 cites W1970754582 @default.
- W2912448950 cites W2044692651 @default.
- W2912448950 cites W2064675550 @default.
- W2912448950 cites W2088321962 @default.
- W2912448950 cites W2089468765 @default.
- W2912448950 cites W2097747115 @default.
- W2912448950 cites W2098063401 @default.
- W2912448950 cites W2100718094 @default.
- W2912448950 cites W2104095103 @default.
- W2912448950 cites W2134089414 @default.
- W2912448950 cites W2153787847 @default.
- W2912448950 cites W2164274563 @default.
- W2912448950 cites W2180282282 @default.
- W2912448950 cites W2461096182 @default.
- W2912448950 cites W2795217212 @default.
- W2912448950 cites W2802219389 @default.
- W2912448950 cites W2919115771 @default.
- W2912448950 cites W3122980764 @default.
- W2912448950 cites W3123968317 @default.
- W2912448950 cites W3123982643 @default.
- W2912448950 cites W425806827 @default.
- W2912448950 cites W645714690 @default.
- W2912448950 cites W2114001875 @default.
- W2912448950 doi "https://doi.org/10.1109/icdmw.2018.00120" @default.
- W2912448950 hasPublicationYear "2018" @default.
- W2912448950 type Work @default.
- W2912448950 sameAs 2912448950 @default.
- W2912448950 citedByCount "1" @default.
- W2912448950 countsByYear W29124489502021 @default.
- W2912448950 crossrefType "proceedings-article" @default.
- W2912448950 hasAuthorship W2912448950A5002091761 @default.
- W2912448950 hasAuthorship W2912448950A5002195074 @default.
- W2912448950 hasAuthorship W2912448950A5051861995 @default.
- W2912448950 hasConcept C11413529 @default.
- W2912448950 hasConcept C119857082 @default.
- W2912448950 hasConcept C124101348 @default.
- W2912448950 hasConcept C143724316 @default.
- W2912448950 hasConcept C147168706 @default.
- W2912448950 hasConcept C151406439 @default.
- W2912448950 hasConcept C151730666 @default.
- W2912448950 hasConcept C153180895 @default.
- W2912448950 hasConcept C154945302 @default.
- W2912448950 hasConcept C41008148 @default.
- W2912448950 hasConcept C50644808 @default.
- W2912448950 hasConcept C57493831 @default.
- W2912448950 hasConcept C73555534 @default.
- W2912448950 hasConcept C86803240 @default.
- W2912448950 hasConceptScore W2912448950C11413529 @default.
- W2912448950 hasConceptScore W2912448950C119857082 @default.
- W2912448950 hasConceptScore W2912448950C124101348 @default.
- W2912448950 hasConceptScore W2912448950C143724316 @default.
- W2912448950 hasConceptScore W2912448950C147168706 @default.
- W2912448950 hasConceptScore W2912448950C151406439 @default.
- W2912448950 hasConceptScore W2912448950C151730666 @default.
- W2912448950 hasConceptScore W2912448950C153180895 @default.
- W2912448950 hasConceptScore W2912448950C154945302 @default.
- W2912448950 hasConceptScore W2912448950C41008148 @default.
- W2912448950 hasConceptScore W2912448950C50644808 @default.
- W2912448950 hasConceptScore W2912448950C57493831 @default.
- W2912448950 hasConceptScore W2912448950C73555534 @default.
- W2912448950 hasConceptScore W2912448950C86803240 @default.
- W2912448950 hasLocation W29124489501 @default.
- W2912448950 hasOpenAccess W2912448950 @default.
- W2912448950 hasPrimaryLocation W29124489501 @default.
- W2912448950 hasRelatedWork W1899785454 @default.
- W2912448950 hasRelatedWork W2019278659 @default.
- W2912448950 hasRelatedWork W205702776 @default.
- W2912448950 hasRelatedWork W2089166450 @default.
- W2912448950 hasRelatedWork W2146291192 @default.
- W2912448950 hasRelatedWork W2161078209 @default.
- W2912448950 hasRelatedWork W2575761508 @default.
- W2912448950 hasRelatedWork W2997055501 @default.
- W2912448950 hasRelatedWork W2997101704 @default.
- W2912448950 hasRelatedWork W50633494 @default.
- W2912448950 isParatext "false" @default.
- W2912448950 isRetracted "false" @default.
- W2912448950 magId "2912448950" @default.
- W2912448950 workType "article" @default.