Optimal Deep Learning LSTM Model for Electric Load Forecasting Using Feature Selection and Genetic Algorithm: Comparison With Machine Learning Approaches †

Energies - Switzerland
doi 10.3390/en11071636
Full Text
Abstract

Available in full text

Date
Authors
Publisher

MDPI AG

Related search

Intrusion Detection Using Feature Selection and Machine Learning Algorithm With Misuse Detection

International Journal of Computer Science and Information Technology
2016English

Eta Correlation Coefficient Based Feature Selection Algorithm for Machine Learning: E-Score Feature Selection Algorithm

Journal of Intelligent Systems: Theory and Applications
2019English

Feature Selection for DDoS Detection Using Classification Machine Learning Techniques

IAES International Journal of Artificial Intelligence
ControlSystems EngineeringInformation SystemsElectronic EngineeringManagementElectricalArtificial Intelligence
2020English

Feature Selection Using Parallel Genetic Algorithm for the Prediction of Geometric Mean Diameter of Soil Aggregates by Machine Learning Methods

Arid Land Research and Management
Soil Science
2014English

Malayalam Error Sentence Detection Using Deep Learning With RNN-LSTM

International Journal of Recent Technology and Engineering
EngineeringManagement of TechnologyInnovation
2019English

Intrusion Detection Using Machine Learning and Deep Learning

International Journal of Recent Technology and Engineering
EngineeringManagement of TechnologyInnovation
2019English

Comparing Feature Bias and Feature Selection Strategies for Many-Attribute Machine Learning

2010English

Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction

Wirtschaftsinformatik
Information Systems
2020English

Feature-Based Mobile Phone Rating Using Sentiment Analysis and Machine Learning Approaches

2019English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy