Matches in SemOpenAlex for { <https://semopenalex.org/work/W3169017890> ?p ?o ?g. }
Showing items 1 to 83 of
83
with 100 items per page.
- W3169017890 abstract "Previous studies have shown the effectiveness of deep learning algorithms in improving the detection of credit card fraud, which has become a major issue for financial institutions. This paper discusses how two deep learning methods, Convolutional Neural Network (CNN) and auto-encoder, perform in the fraud detection task using two different datasets. This research utilizes a brand-new dataset with all raw input variables and a Universite Libre de Bruxelles (ULB) transaction dataset, which has been preprocessed with PCA technology. Since imbalanced datasets can affect the training quality, we further preprocess the datasets using random under-sampling and the Synthetic Minority Oversampling Techniques (SMOTE) to balance the datasets. Through the experimental results, we find that the networks perform well for the ULB dataset with 93% accuracy in prediction, but perform poorly for the independent input dataset, and the performance can be improved when increasing the complexity of the network. We also notice that the under-sampling method helps improve prediction accuracy better than the oversampling method. The results indicate that more complicated networks are required to detect fraud when the criterion for fraud is stricter, while balancing the dataset before training will improve the results." @default.
- W3169017890 created "2021-06-22" @default.
- W3169017890 creator A5028972131 @default.
- W3169017890 creator A5058252399 @default.
- W3169017890 date "2020-11-01" @default.
- W3169017890 modified "2023-10-16" @default.
- W3169017890 title "Credit Card Fraud Detection via Deep Learning Method Using Data Balance Tools" @default.
- W3169017890 cites W2148143831 @default.
- W3169017890 cites W2621388707 @default.
- W3169017890 cites W2772947247 @default.
- W3169017890 cites W2786146442 @default.
- W3169017890 cites W2805595195 @default.
- W3169017890 cites W2908981107 @default.
- W3169017890 cites W2916295401 @default.
- W3169017890 doi "https://doi.org/10.1109/iccsmt51754.2020.00033" @default.
- W3169017890 hasPublicationYear "2020" @default.
- W3169017890 type Work @default.
- W3169017890 sameAs 3169017890 @default.
- W3169017890 citedByCount "2" @default.
- W3169017890 countsByYear W31690178902022 @default.
- W3169017890 countsByYear W31690178902023 @default.
- W3169017890 crossrefType "proceedings-article" @default.
- W3169017890 hasAuthorship W3169017890A5028972131 @default.
- W3169017890 hasAuthorship W3169017890A5058252399 @default.
- W3169017890 hasConcept C106131492 @default.
- W3169017890 hasConcept C108583219 @default.
- W3169017890 hasConcept C119857082 @default.
- W3169017890 hasConcept C124101348 @default.
- W3169017890 hasConcept C132964779 @default.
- W3169017890 hasConcept C136764020 @default.
- W3169017890 hasConcept C140779682 @default.
- W3169017890 hasConcept C145097563 @default.
- W3169017890 hasConcept C154945302 @default.
- W3169017890 hasConcept C169258074 @default.
- W3169017890 hasConcept C197323446 @default.
- W3169017890 hasConcept C199360897 @default.
- W3169017890 hasConcept C2776257435 @default.
- W3169017890 hasConcept C2780747020 @default.
- W3169017890 hasConcept C2983355114 @default.
- W3169017890 hasConcept C31258907 @default.
- W3169017890 hasConcept C31972630 @default.
- W3169017890 hasConcept C41008148 @default.
- W3169017890 hasConcept C75949130 @default.
- W3169017890 hasConcept C77088390 @default.
- W3169017890 hasConcept C81363708 @default.
- W3169017890 hasConceptScore W3169017890C106131492 @default.
- W3169017890 hasConceptScore W3169017890C108583219 @default.
- W3169017890 hasConceptScore W3169017890C119857082 @default.
- W3169017890 hasConceptScore W3169017890C124101348 @default.
- W3169017890 hasConceptScore W3169017890C132964779 @default.
- W3169017890 hasConceptScore W3169017890C136764020 @default.
- W3169017890 hasConceptScore W3169017890C140779682 @default.
- W3169017890 hasConceptScore W3169017890C145097563 @default.
- W3169017890 hasConceptScore W3169017890C154945302 @default.
- W3169017890 hasConceptScore W3169017890C169258074 @default.
- W3169017890 hasConceptScore W3169017890C197323446 @default.
- W3169017890 hasConceptScore W3169017890C199360897 @default.
- W3169017890 hasConceptScore W3169017890C2776257435 @default.
- W3169017890 hasConceptScore W3169017890C2780747020 @default.
- W3169017890 hasConceptScore W3169017890C2983355114 @default.
- W3169017890 hasConceptScore W3169017890C31258907 @default.
- W3169017890 hasConceptScore W3169017890C31972630 @default.
- W3169017890 hasConceptScore W3169017890C41008148 @default.
- W3169017890 hasConceptScore W3169017890C75949130 @default.
- W3169017890 hasConceptScore W3169017890C77088390 @default.
- W3169017890 hasConceptScore W3169017890C81363708 @default.
- W3169017890 hasLocation W31690178901 @default.
- W3169017890 hasOpenAccess W3169017890 @default.
- W3169017890 hasPrimaryLocation W31690178901 @default.
- W3169017890 hasRelatedWork W2588077444 @default.
- W3169017890 hasRelatedWork W2886330306 @default.
- W3169017890 hasRelatedWork W2886399336 @default.
- W3169017890 hasRelatedWork W2966860114 @default.
- W3169017890 hasRelatedWork W4223943233 @default.
- W3169017890 hasRelatedWork W4225136133 @default.
- W3169017890 hasRelatedWork W4225161397 @default.
- W3169017890 hasRelatedWork W4250304930 @default.
- W3169017890 hasRelatedWork W4283520324 @default.
- W3169017890 hasRelatedWork W435157458 @default.
- W3169017890 isParatext "false" @default.
- W3169017890 isRetracted "false" @default.
- W3169017890 magId "3169017890" @default.
- W3169017890 workType "article" @default.