Matches in SemOpenAlex for { <https://semopenalex.org/work/W3010832015> ?p ?o ?g. }
Showing items 1 to 63 of
63
with 100 items per page.
- W3010832015 abstract "There are many applications available for detecting the electricity theft. However, only few studies compare the machine learning techniques in discovering electricity-stealing behavior. This study, therefore, compares the predictive accuracy of several machine learning methods including Logistic Regression (LR), The K-Nearest Neighbor Algorithm, (K-NN), Support Vector Machines (SVM), and Neural Networks (NNet) for predicting the electricity thefts in a concrete model." @default.
- W3010832015 created "2020-03-23" @default.
- W3010832015 creator A5006098403 @default.
- W3010832015 creator A5011693379 @default.
- W3010832015 creator A5056168495 @default.
- W3010832015 creator A5086484914 @default.
- W3010832015 date "2018-10-01" @default.
- W3010832015 modified "2023-10-16" @default.
- W3010832015 title "Comparison of Machine Learning Techniques for the Detection of the Electricity Theft" @default.
- W3010832015 cites W2003437905 @default.
- W3010832015 cites W2034436397 @default.
- W3010832015 cites W2122111042 @default.
- W3010832015 cites W2156909104 @default.
- W3010832015 cites W2542346878 @default.
- W3010832015 cites W4239510810 @default.
- W3010832015 cites W5690377 @default.
- W3010832015 doi "https://doi.org/10.1109/cciot45285.2018.9032675" @default.
- W3010832015 hasPublicationYear "2018" @default.
- W3010832015 type Work @default.
- W3010832015 sameAs 3010832015 @default.
- W3010832015 citedByCount "3" @default.
- W3010832015 countsByYear W30108320152021 @default.
- W3010832015 countsByYear W30108320152023 @default.
- W3010832015 crossrefType "proceedings-article" @default.
- W3010832015 hasAuthorship W3010832015A5006098403 @default.
- W3010832015 hasAuthorship W3010832015A5011693379 @default.
- W3010832015 hasAuthorship W3010832015A5056168495 @default.
- W3010832015 hasAuthorship W3010832015A5086484914 @default.
- W3010832015 hasConcept C119599485 @default.
- W3010832015 hasConcept C119857082 @default.
- W3010832015 hasConcept C12267149 @default.
- W3010832015 hasConcept C127413603 @default.
- W3010832015 hasConcept C151956035 @default.
- W3010832015 hasConcept C154945302 @default.
- W3010832015 hasConcept C206658404 @default.
- W3010832015 hasConcept C41008148 @default.
- W3010832015 hasConcept C50644808 @default.
- W3010832015 hasConceptScore W3010832015C119599485 @default.
- W3010832015 hasConceptScore W3010832015C119857082 @default.
- W3010832015 hasConceptScore W3010832015C12267149 @default.
- W3010832015 hasConceptScore W3010832015C127413603 @default.
- W3010832015 hasConceptScore W3010832015C151956035 @default.
- W3010832015 hasConceptScore W3010832015C154945302 @default.
- W3010832015 hasConceptScore W3010832015C206658404 @default.
- W3010832015 hasConceptScore W3010832015C41008148 @default.
- W3010832015 hasConceptScore W3010832015C50644808 @default.
- W3010832015 hasLocation W30108320151 @default.
- W3010832015 hasOpenAccess W3010832015 @default.
- W3010832015 hasPrimaryLocation W30108320151 @default.
- W3010832015 hasRelatedWork W1996541855 @default.
- W3010832015 hasRelatedWork W2773213923 @default.
- W3010832015 hasRelatedWork W2937631562 @default.
- W3010832015 hasRelatedWork W2979979539 @default.
- W3010832015 hasRelatedWork W3136979370 @default.
- W3010832015 hasRelatedWork W3194539120 @default.
- W3010832015 hasRelatedWork W3195168932 @default.
- W3010832015 hasRelatedWork W4205958290 @default.
- W3010832015 hasRelatedWork W4361795583 @default.
- W3010832015 hasRelatedWork W4362499384 @default.
- W3010832015 isParatext "false" @default.
- W3010832015 isRetracted "false" @default.
- W3010832015 magId "3010832015" @default.
- W3010832015 workType "article" @default.