HIGHLIGHTS
- who: -Classification and collaborators from the (UNIVERSITY) have published the research: Robust Data Driven Analysis for Electricity Theft Attack-Resilient Power Grid, in the Journal: (JOURNAL)
- what: The authors propose three distinctive mechanisms including hyper-parameters tuning regularization and skip connections to improve the performance of standard ANN to handle more complex tasks using smart meter (SM) data. The work described by_[4]-[7] concerns supervised ML algorithms to characterize the class label of normal and anomalous power consumption patterns. The authors examine binary classification issues for ETD in smart grids. To achieve this challenging . . .
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