FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3207807837> ?p ?o ?g. }

• W3207807837 endingPage "141166" @default.
• W3207807837 startingPage "141154" @default.
• W3207807837 abstract "Enhanced metering infrastructure is a key component of the electrical system, offering many advantages, including load management and demand response. However, several additional energy theft channels are introduced by the automation of the metering system. With data analysis techniques, adapting the smart grid significantly reduces energy theft loss. In this article, we proposed deep learning methods for the identification of power theft. A three-stage technique has been devised, which includes selection, extraction, and classification of features. In the selection phase, the average hybrid feature importance determines the most important features and high priority. The feature extraction technique employs the ZFNET method to remove the unwanted features. For the detection of electric fraud, Convolutional Neural Network based Long Short Term Memory (CNN-LSTM) is utilized. Meta-heuristic techniques, including BlackWidow Optimization (BWO) and Blue Monkey Optimization (BMO), are used to calculate optimized values for the hyperparameters of CNN-LSTM. The tuning of hyperparameters of the classifier helps in better training on data. After extensive simulation, our proposed methods CNN-LSTM-BMO and CNN-LSTM-BWO achieved an accuracy of 91% and 93%. Our proposed methods outperform all the existing compared schemes. The performance of our models has attained high accuracy and low error rate. Furthermore, the statistical analysis also shows the superiority of the proposed methods." @default.
• W3207807837 created "2021-10-25" @default.
• W3207807837 creator A5030827985 @default.
• W3207807837 creator A5058352305 @default.
• W3207807837 date "2021-01-01" @default.
• W3207807837 modified "2023-10-17" @default.
• W3207807837 title "A Novel Method CNN-LSTM Ensembler Based on Black Widow and Blue Monkey Optimizer for Electricity Theft Detection" @default.
• W3207807837 cites W1937275748 @default.
• W3207807837 cites W2028868384 @default.
• W3207807837 cites W2212529815 @default.
• W3207807837 cites W2608730641 @default.
• W3207807837 cites W2776990447 @default.
• W3207807837 cites W2786330811 @default.
• W3207807837 cites W2790074059 @default.
• W3207807837 cites W2794620754 @default.
• W3207807837 cites W2801761896 @default.
• W3207807837 cites W2893605396 @default.
• W3207807837 cites W2904322393 @default.
• W3207807837 cites W2910992412 @default.
• W3207807837 cites W2922515190 @default.
• W3207807837 cites W2970705010 @default.
• W3207807837 cites W2978986055 @default.
• W3207807837 cites W2980570898 @default.
• W3207807837 cites W2995221930 @default.
• W3207807837 cites W3000313095 @default.
• W3207807837 cites W3005173691 @default.
• W3207807837 cites W3005498056 @default.
• W3207807837 cites W3007211815 @default.
• W3207807837 cites W3016889527 @default.
• W3207807837 cites W3033222914 @default.
• W3207807837 cites W3039814348 @default.
• W3207807837 cites W3088839307 @default.
• W3207807837 cites W3090158923 @default.
• W3207807837 cites W3094328446 @default.
• W3207807837 cites W3097369108 @default.
• W3207807837 cites W3106556155 @default.
• W3207807837 cites W3128592100 @default.
• W3207807837 cites W3129175333 @default.
• W3207807837 cites W3132166193 @default.
• W3207807837 cites W3138780148 @default.
• W3207807837 cites W3164801929 @default.
• W3207807837 cites W3168515984 @default.
• W3207807837 cites W3176484245 @default.
• W3207807837 cites W3176996088 @default.
• W3207807837 cites W3190512233 @default.
• W3207807837 cites W3193536302 @default.
• W3207807837 cites W3198120912 @default.
• W3207807837 cites W4231400843 @default.
• W3207807837 cites W4247574165 @default.
• W3207807837 doi "https://doi.org/10.1109/access.2021.3119575" @default.
• W3207807837 hasPublicationYear "2021" @default.
• W3207807837 type Work @default.
• W3207807837 sameAs 3207807837 @default.
• W3207807837 citedByCount "4" @default.
• W3207807837 countsByYear W32078078372022 @default.
• W3207807837 countsByYear W32078078372023 @default.
• W3207807837 crossrefType "journal-article" @default.
• W3207807837 hasAuthorship W3207807837A5030827985 @default.
• W3207807837 hasAuthorship W3207807837A5058352305 @default.
• W3207807837 hasBestOaLocation W32078078371 @default.
• W3207807837 hasConcept C10558101 @default.
• W3207807837 hasConcept C108583219 @default.
• W3207807837 hasConcept C119857082 @default.
• W3207807837 hasConcept C148483581 @default.
• W3207807837 hasConcept C153180895 @default.
• W3207807837 hasConcept C154945302 @default.
• W3207807837 hasConcept C18903297 @default.
• W3207807837 hasConcept C41008148 @default.
• W3207807837 hasConcept C52622490 @default.
• W3207807837 hasConcept C81363708 @default.
• W3207807837 hasConcept C8642999 @default.
• W3207807837 hasConcept C86803240 @default.
• W3207807837 hasConceptScore W3207807837C10558101 @default.
• W3207807837 hasConceptScore W3207807837C108583219 @default.
• W3207807837 hasConceptScore W3207807837C119857082 @default.
• W3207807837 hasConceptScore W3207807837C148483581 @default.
• W3207807837 hasConceptScore W3207807837C153180895 @default.
• W3207807837 hasConceptScore W3207807837C154945302 @default.
• W3207807837 hasConceptScore W3207807837C18903297 @default.
• W3207807837 hasConceptScore W3207807837C41008148 @default.
• W3207807837 hasConceptScore W3207807837C52622490 @default.
• W3207807837 hasConceptScore W3207807837C81363708 @default.
• W3207807837 hasConceptScore W3207807837C8642999 @default.
• W3207807837 hasConceptScore W3207807837C86803240 @default.
• W3207807837 hasLocation W32078078371 @default.
• W3207807837 hasLocation W32078078372 @default.
• W3207807837 hasOpenAccess W3207807837 @default.
• W3207807837 hasPrimaryLocation W32078078371 @default.
• W3207807837 hasRelatedWork W2140186469 @default.
• W3207807837 hasRelatedWork W2775233965 @default.
• W3207807837 hasRelatedWork W3183136280 @default.
• W3207807837 hasRelatedWork W4206951940 @default.
• W3207807837 hasRelatedWork W4280563792 @default.
• W3207807837 hasRelatedWork W4293868382 @default.
• W3207807837 hasRelatedWork W4311551265 @default.
• W3207807837 hasRelatedWork W4318559728 @default.
• W3207807837 hasRelatedWork W4318719684 @default.
• W3207807837 hasRelatedWork W4382602594 @default.

• next