Matches in SemOpenAlex for { <https://semopenalex.org/work/W3036312947> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W3036312947 abstract "Machine learning techniques are widely used to take intelligent decisions in industrial and educational domains. In the educational domain, when a research scholar submits a dissertation, then it has to be indexed and classified. The number of dissertations that are submitted in an educational institute is usually high and if done manually, it becomes difficult to index and classify correctly. This study applies machine learning techniques to automate the indexing and categorization of dissertations. We have focused on dissertations from the Engineering, Medical, Social Science, and General Science fields. We used the Bag of Words (BoW) method to extract features and K-means, Density-based spatial clustering of applications with noise (DBSCAN) and Expectation-Maximisation (EM) to train our model. Our experimental results reveal that the proposed K- means technique for indexing and categorization leads to higher accuracy and significant reduction in negative predictions as compared to DBSCAN and Expectation-Maximisation (EM)." @default.
- W3036312947 created "2020-06-25" @default.
- W3036312947 creator A5044987162 @default.
- W3036312947 creator A5067003371 @default.
- W3036312947 date "2020-02-01" @default.
- W3036312947 modified "2023-09-23" @default.
- W3036312947 title "Categorization of Dissertation using Machine Learning Techniques" @default.
- W3036312947 cites W1984702507 @default.
- W3036312947 cites W1994107727 @default.
- W3036312947 cites W2579161546 @default.
- W3036312947 cites W2762926871 @default.
- W3036312947 cites W2768680874 @default.
- W3036312947 cites W2783108935 @default.
- W3036312947 cites W2785415545 @default.
- W3036312947 cites W2807508722 @default.
- W3036312947 cites W2910555889 @default.
- W3036312947 cites W2911746830 @default.
- W3036312947 cites W2930842027 @default.
- W3036312947 cites W2950577166 @default.
- W3036312947 cites W2972164670 @default.
- W3036312947 cites W3105625590 @default.
- W3036312947 doi "https://doi.org/10.1109/iconc345789.2020.9117485" @default.
- W3036312947 hasPublicationYear "2020" @default.
- W3036312947 type Work @default.
- W3036312947 sameAs 3036312947 @default.
- W3036312947 citedByCount "0" @default.
- W3036312947 crossrefType "proceedings-article" @default.
- W3036312947 hasAuthorship W3036312947A5044987162 @default.
- W3036312947 hasAuthorship W3036312947A5067003371 @default.
- W3036312947 hasConcept C104047586 @default.
- W3036312947 hasConcept C115961682 @default.
- W3036312947 hasConcept C119857082 @default.
- W3036312947 hasConcept C124101348 @default.
- W3036312947 hasConcept C134306372 @default.
- W3036312947 hasConcept C153180895 @default.
- W3036312947 hasConcept C154945302 @default.
- W3036312947 hasConcept C17212007 @default.
- W3036312947 hasConcept C23123220 @default.
- W3036312947 hasConcept C33923547 @default.
- W3036312947 hasConcept C36503486 @default.
- W3036312947 hasConcept C41008148 @default.
- W3036312947 hasConcept C46576248 @default.
- W3036312947 hasConcept C73555534 @default.
- W3036312947 hasConcept C75165309 @default.
- W3036312947 hasConcept C94124525 @default.
- W3036312947 hasConcept C99498987 @default.
- W3036312947 hasConceptScore W3036312947C104047586 @default.
- W3036312947 hasConceptScore W3036312947C115961682 @default.
- W3036312947 hasConceptScore W3036312947C119857082 @default.
- W3036312947 hasConceptScore W3036312947C124101348 @default.
- W3036312947 hasConceptScore W3036312947C134306372 @default.
- W3036312947 hasConceptScore W3036312947C153180895 @default.
- W3036312947 hasConceptScore W3036312947C154945302 @default.
- W3036312947 hasConceptScore W3036312947C17212007 @default.
- W3036312947 hasConceptScore W3036312947C23123220 @default.
- W3036312947 hasConceptScore W3036312947C33923547 @default.
- W3036312947 hasConceptScore W3036312947C36503486 @default.
- W3036312947 hasConceptScore W3036312947C41008148 @default.
- W3036312947 hasConceptScore W3036312947C46576248 @default.
- W3036312947 hasConceptScore W3036312947C73555534 @default.
- W3036312947 hasConceptScore W3036312947C75165309 @default.
- W3036312947 hasConceptScore W3036312947C94124525 @default.
- W3036312947 hasConceptScore W3036312947C99498987 @default.
- W3036312947 hasLocation W30363129471 @default.
- W3036312947 hasOpenAccess W3036312947 @default.
- W3036312947 hasPrimaryLocation W30363129471 @default.
- W3036312947 hasRelatedWork W1873761914 @default.
- W3036312947 hasRelatedWork W2040963032 @default.
- W3036312947 hasRelatedWork W2094492371 @default.
- W3036312947 hasRelatedWork W2187492663 @default.
- W3036312947 hasRelatedWork W2368219397 @default.
- W3036312947 hasRelatedWork W2503866109 @default.
- W3036312947 hasRelatedWork W2959625647 @default.
- W3036312947 hasRelatedWork W3004596345 @default.
- W3036312947 hasRelatedWork W4249836580 @default.
- W3036312947 hasRelatedWork W4290987788 @default.
- W3036312947 isParatext "false" @default.
- W3036312947 isRetracted "false" @default.
- W3036312947 magId "3036312947" @default.
- W3036312947 workType "article" @default.