Matches in SemOpenAlex for { <https://semopenalex.org/work/W3132877231> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W3132877231 abstract "Machine Learning and Deep Learning can play an essential role in determining the spread of diseases. The proposed system aims at predicting the spread of Tuberculosis by understanding the impact of various climatic and pollution parameters on the disease. The proposed solution takes into consideration the information related to Tuberculosis in different districts of India; and the climatic and pollution parameters for those regions. This information is then used to understand the sustainability conditions of Tuberculosis and correlation of different environmental factors with a number of cases of Tuberculosis. This can then help in the prediction of the spread of disease. The system will also provide visualizations depicting the spread pattern of Tuberculosis, of the different regions affected in the past and the regions which may get affected in the near future." @default.
- W3132877231 created "2021-03-01" @default.
- W3132877231 creator A5001279331 @default.
- W3132877231 creator A5018478307 @default.
- W3132877231 creator A5026824036 @default.
- W3132877231 creator A5042990479 @default.
- W3132877231 creator A5047478740 @default.
- W3132877231 creator A5053946307 @default.
- W3132877231 date "2020-12-01" @default.
- W3132877231 modified "2023-09-23" @default.
- W3132877231 title "AI-based prediction for early detection of Tuberculosis in India based on environmental factors" @default.
- W3132877231 cites W1984148466 @default.
- W3132877231 cites W2056532544 @default.
- W3132877231 cites W2058128345 @default.
- W3132877231 cites W2146036952 @default.
- W3132877231 cites W2166938897 @default.
- W3132877231 cites W2344843072 @default.
- W3132877231 cites W2547462170 @default.
- W3132877231 cites W2883585595 @default.
- W3132877231 cites W2902865140 @default.
- W3132877231 cites W2971447636 @default.
- W3132877231 cites W3033090984 @default.
- W3132877231 doi "https://doi.org/10.1109/icmla51294.2020.00053" @default.
- W3132877231 hasPublicationYear "2020" @default.
- W3132877231 type Work @default.
- W3132877231 sameAs 3132877231 @default.
- W3132877231 citedByCount "0" @default.
- W3132877231 crossrefType "proceedings-article" @default.
- W3132877231 hasAuthorship W3132877231A5001279331 @default.
- W3132877231 hasAuthorship W3132877231A5018478307 @default.
- W3132877231 hasAuthorship W3132877231A5026824036 @default.
- W3132877231 hasAuthorship W3132877231A5042990479 @default.
- W3132877231 hasAuthorship W3132877231A5047478740 @default.
- W3132877231 hasAuthorship W3132877231A5053946307 @default.
- W3132877231 hasConcept C117220453 @default.
- W3132877231 hasConcept C119857082 @default.
- W3132877231 hasConcept C142724271 @default.
- W3132877231 hasConcept C154945302 @default.
- W3132877231 hasConcept C18903297 @default.
- W3132877231 hasConcept C205649164 @default.
- W3132877231 hasConcept C2524010 @default.
- W3132877231 hasConcept C2779134260 @default.
- W3132877231 hasConcept C2781069245 @default.
- W3132877231 hasConcept C2909468537 @default.
- W3132877231 hasConcept C33923547 @default.
- W3132877231 hasConcept C41008148 @default.
- W3132877231 hasConcept C521259446 @default.
- W3132877231 hasConcept C526734887 @default.
- W3132877231 hasConcept C66204764 @default.
- W3132877231 hasConcept C71924100 @default.
- W3132877231 hasConcept C86803240 @default.
- W3132877231 hasConceptScore W3132877231C117220453 @default.
- W3132877231 hasConceptScore W3132877231C119857082 @default.
- W3132877231 hasConceptScore W3132877231C142724271 @default.
- W3132877231 hasConceptScore W3132877231C154945302 @default.
- W3132877231 hasConceptScore W3132877231C18903297 @default.
- W3132877231 hasConceptScore W3132877231C205649164 @default.
- W3132877231 hasConceptScore W3132877231C2524010 @default.
- W3132877231 hasConceptScore W3132877231C2779134260 @default.
- W3132877231 hasConceptScore W3132877231C2781069245 @default.
- W3132877231 hasConceptScore W3132877231C2909468537 @default.
- W3132877231 hasConceptScore W3132877231C33923547 @default.
- W3132877231 hasConceptScore W3132877231C41008148 @default.
- W3132877231 hasConceptScore W3132877231C521259446 @default.
- W3132877231 hasConceptScore W3132877231C526734887 @default.
- W3132877231 hasConceptScore W3132877231C66204764 @default.
- W3132877231 hasConceptScore W3132877231C71924100 @default.
- W3132877231 hasConceptScore W3132877231C86803240 @default.
- W3132877231 hasLocation W31328772311 @default.
- W3132877231 hasOpenAccess W3132877231 @default.
- W3132877231 hasPrimaryLocation W31328772311 @default.
- W3132877231 hasRelatedWork W2961085424 @default.
- W3132877231 hasRelatedWork W3046775127 @default.
- W3132877231 hasRelatedWork W3107602296 @default.
- W3132877231 hasRelatedWork W3170094116 @default.
- W3132877231 hasRelatedWork W3209574120 @default.
- W3132877231 hasRelatedWork W4205958290 @default.
- W3132877231 hasRelatedWork W4286629047 @default.
- W3132877231 hasRelatedWork W4306321456 @default.
- W3132877231 hasRelatedWork W4306674287 @default.
- W3132877231 hasRelatedWork W4224009465 @default.
- W3132877231 isParatext "false" @default.
- W3132877231 isRetracted "false" @default.
- W3132877231 magId "3132877231" @default.
- W3132877231 workType "article" @default.