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Optimization of Deep Neural Network Based on Sine Genetic Algorithm for Electric Theft Detection

https://doi.org/10.2139/ssrn.4755077
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s13369-021-06313-zopenalex-extracted
Accurate Detection of Electricity Theft Using Classification Algorithms and Internet of Things in Smart Grid
inferredcites →https://doi.org/10.1016/j.egyr.2020.11.138openalex-extracted
Identification of stealing electricity based on big data analysis
inferredcites →https://doi.org/10.1016/j.egyr.2021.07.008openalex-extracted
A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection
inferredcites →https://doi.org/10.1016/j.egyr.2021.08.038openalex-extracted
A hybrid approach based on deep learning and support vector machine for the detection of electricity theft in power grids
inferredcites →https://doi.org/10.1016/j.egyr.2023.09.100openalex-extracted
Step towards secure and reliable smart grids in Industry 5.0: A federated learning assisted hybrid deep learning model for electricity theft detection using smart meters
inferredcites →https://doi.org/10.1016/j.epsr.2020.106904openalex-extracted
Ensemble machine learning models for the detection of energy theft
inferredcites →https://doi.org/10.1016/j.jclepro.2019.119831openalex-extracted
Grid-connected renewable energy sources: Review of the recent integration requirements and control methods
inferredcites →https://doi.org/10.1016/j.jesit.2018.01.001openalex-extracted
Smart grids security challenges: Classification by sources of threats
inferredcites →https://doi.org/10.1016/j.jpdc.2021.03.002openalex-extracted
An adaptive synthesis to handle imbalanced big data with deep siamese network for electricity theft detection in smart grids
inferredcites →https://doi.org/10.1016/j.neucom.2019.10.118openalex-extracted
A comprehensive survey on support vector machine classification: Applications, challenges and trends
inferredcites →https://doi.org/10.1016/j.rser.2017.10.040openalex-extracted
Review of various modeling techniques for the detection of electricity theft in smart grid environment
inferredcites →https://doi.org/10.1049/iet-cps.2018.5050openalex-extracted
Energy theft detection for AMI using principal component analysis based reconstructed data
inferredcites →https://doi.org/10.1109/access.2019.2912200openalex-extracted
Review of Deep Learning Algorithms and Architectures
inferredcites →https://doi.org/10.1109/access.2022.3166146openalex-extracted
Electricity Theft Detection in Smart Grids Based on Deep Neural Network
inferredcites →https://doi.org/10.1109/tifs.2020.2965276openalex-extracted
Hidden Electricity Theft by Exploiting Multiple-Pricing Scheme in Smart Grids
inferredcites →https://doi.org/10.1109/tii.2017.2785963openalex-extracted
Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids
inferredcites →https://doi.org/10.1109/tii.2018.2873814openalex-extracted
A Novel Combined Data-Driven Approach for Electricity Theft Detection
inferredcites →https://doi.org/10.1109/tsg.2020.3047864openalex-extracted
Robust Electricity Theft Detection Against Data Poisoning Attacks in Smart Grids
inferredcites →https://doi.org/10.1155/2019/4136874openalex-extracted
Electricity Theft Detection in Power Grids with Deep Learning and Random Forests
inferredcites →https://doi.org/10.11591/eei.v10i4.2875openalex-extracted
Electricity-theft detection in smart grids based on deep learning
inferredcites →https://doi.org/10.11648/j.eas.20200502.12openalex-extracted
A Technique for Electrical Energy Theft Detection and Location in Low Voltage Power Distribution Systems
inferredcites →https://doi.org/10.1186/s40537-021-00444-8openalex-extracted
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
inferredcites →https://doi.org/10.3390/en13215599openalex-extracted
A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids
inferredcites →https://doi.org/10.48550/arxiv.1912.08957openalex-extracted
Optimization for deep learning: theory and algorithms
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