Matches in SemOpenAlex for { <https://semopenalex.org/work/W4205684906> ?p ?o ?g. }
- W4205684906 endingPage "1275" @default.
- W4205684906 startingPage "1257" @default.
- W4205684906 abstract "This paper presents a novel, sequentially executed supervised machine learning-based electric theft detection framework using a Jaya-optimized combined Kernel and Tree Boosting (KTBoost) classifier. It utilizes the intelligence of the XGBoost algorithm to estimate the missing values in the acquired dataset during the data pre-processing phase. An oversampling algorithm based on the Robust-SMOTE technique is utilized to avoid the unbalanced data class distribution issue. Afterward, with the aid of few very significant statistical, temporal, and spectral features extracted from the acquired kWh dataset, the complex underlying data patterns are comprehended to enhance the accuracy and detection rate of the classifier. For effectively classifying the consumers into “Honest” and “Fraudster,” the ensemble machine learning-based classifier KTBoost, with Jaya algorithm optimized hyperparameters, is utilized. Finally, the developed model is re-trained using a reduced set of highly important features to minimize the computational resources without compromising the performance of the developed model. The outcome of this study reveals that the proposed theft detection method achieves the highest accuracy (93.38%), precision (95%), and recall (93.18%) among all the studied methods, thus signifying its importance in the studied area of research." @default.
- W4205684906 created "2022-01-25" @default.
- W4205684906 creator A5013398556 @default.
- W4205684906 creator A5015695424 @default.
- W4205684906 creator A5018312503 @default.
- W4205684906 creator A5071896704 @default.
- W4205684906 creator A5091222441 @default.
- W4205684906 date "2022-01-11" @default.
- W4205684906 modified "2023-09-25" @default.
- W4205684906 title "Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel‐Tree boosting classifier—A novel sequentially executed supervised machine learning approach" @default.
- W4205684906 cites W1480376833 @default.
- W4205684906 cites W1567694985 @default.
- W4205684906 cites W172222775 @default.
- W4205684906 cites W1963956309 @default.
- W4205684906 cites W1964303812 @default.
- W4205684906 cites W1980835351 @default.
- W4205684906 cites W1993704769 @default.
- W4205684906 cites W2021022350 @default.
- W4205684906 cites W2030717059 @default.
- W4205684906 cites W2032161710 @default.
- W4205684906 cites W2050495423 @default.
- W4205684906 cites W2081506432 @default.
- W4205684906 cites W2082678545 @default.
- W4205684906 cites W2096863518 @default.
- W4205684906 cites W2099306544 @default.
- W4205684906 cites W2111027089 @default.
- W4205684906 cites W2121352581 @default.
- W4205684906 cites W2141810254 @default.
- W4205684906 cites W2141842212 @default.
- W4205684906 cites W2206481872 @default.
- W4205684906 cites W2212529815 @default.
- W4205684906 cites W2242558534 @default.
- W4205684906 cites W2280895239 @default.
- W4205684906 cites W2291620186 @default.
- W4205684906 cites W2328146686 @default.
- W4205684906 cites W2581082906 @default.
- W4205684906 cites W2582953896 @default.
- W4205684906 cites W2608730641 @default.
- W4205684906 cites W2725329029 @default.
- W4205684906 cites W2732928394 @default.
- W4205684906 cites W2740333758 @default.
- W4205684906 cites W2765429727 @default.
- W4205684906 cites W2773107986 @default.
- W4205684906 cites W2776990447 @default.
- W4205684906 cites W2788544268 @default.
- W4205684906 cites W2789012589 @default.
- W4205684906 cites W2793916350 @default.
- W4205684906 cites W2831439818 @default.
- W4205684906 cites W2888260667 @default.
- W4205684906 cites W2910992412 @default.
- W4205684906 cites W2963749793 @default.
- W4205684906 cites W2965226712 @default.
- W4205684906 cites W2970705010 @default.
- W4205684906 cites W3016889527 @default.
- W4205684906 cites W3034856730 @default.
- W4205684906 cites W3035901522 @default.
- W4205684906 cites W3038113735 @default.
- W4205684906 cites W3044867970 @default.
- W4205684906 cites W3081010851 @default.
- W4205684906 cites W3093212523 @default.
- W4205684906 cites W3097369108 @default.
- W4205684906 cites W3125903163 @default.
- W4205684906 cites W3133453494 @default.
- W4205684906 cites W3133879210 @default.
- W4205684906 cites W3138780148 @default.
- W4205684906 cites W3171389320 @default.
- W4205684906 cites W3176527066 @default.
- W4205684906 cites W3177895698 @default.
- W4205684906 cites W3184342818 @default.
- W4205684906 cites W3185320029 @default.
- W4205684906 cites W3187717620 @default.
- W4205684906 cites W3197188289 @default.
- W4205684906 doi "https://doi.org/10.1049/gtd2.12386" @default.
- W4205684906 hasPublicationYear "2022" @default.
- W4205684906 type Work @default.
- W4205684906 citedByCount "4" @default.
- W4205684906 countsByYear W42056849062022 @default.
- W4205684906 countsByYear W42056849062023 @default.
- W4205684906 crossrefType "journal-article" @default.
- W4205684906 hasAuthorship W4205684906A5013398556 @default.
- W4205684906 hasAuthorship W4205684906A5015695424 @default.
- W4205684906 hasAuthorship W4205684906A5018312503 @default.
- W4205684906 hasAuthorship W4205684906A5071896704 @default.
- W4205684906 hasAuthorship W4205684906A5091222441 @default.
- W4205684906 hasBestOaLocation W42056849061 @default.
- W4205684906 hasConcept C119857082 @default.
- W4205684906 hasConcept C12267149 @default.
- W4205684906 hasConcept C124101348 @default.
- W4205684906 hasConcept C136389625 @default.
- W4205684906 hasConcept C153180895 @default.
- W4205684906 hasConcept C154945302 @default.
- W4205684906 hasConcept C169258074 @default.
- W4205684906 hasConcept C197323446 @default.
- W4205684906 hasConcept C2776257435 @default.
- W4205684906 hasConcept C31258907 @default.
- W4205684906 hasConcept C41008148 @default.
- W4205684906 hasConcept C46686674 @default.
- W4205684906 hasConcept C50644808 @default.