Machine learning analysis of electric arc furnace process for the evaluation of energy efficiency parameters

References

Page 1

	Year	Citations
Random Forests Leo Breiman Machine Learning	2001	119.3K
Scikit-learn: Machine Learning in Python Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort, arXiv (Cornell University)	2012	63.3K
Greedy function approximation: A gradient boosting machine. Jerome H. Friedman The Annals of Statistics EngineeringMachine LearningData MiningPattern RecognitionGreedy Function Approximation	2001	27.3K
Multilayer feedforward networks are universal approximators Kurt Hornik, Maxwell B. Stinchcombe, Halbert White Neural Networks Evolving Neural NetworkEngineeringMachine LearningComputational Learning TheoryMultilayer Feedforward Networks	1989	20.7K
Random search for hyper-parameter optimization James Bergstra, Yoshua Bengio	2012	7.9K
XGBoost: A Scalable Tree Boosting System arXiv (Cornell University)	2016	7.1K
TensorFlow: a system for large-scale machine learning Martı́n Abadi, Paul Barham, Jianmin Chen, Operating Systems Design and Implementation Cluster ComputingEngineeringMachine LearningMachine Learning ToolComputer Architecture	2016	6.3K
Support Vector Regression Machines Harris Drucker, Christopher J. C. Burges, Linda Kaufman,	1996	4.2K
Influence of direct reduced iron on the energy balance of the electric arc furnace in steel industry Marcus Kirschen, Karim Badr, Herbert Pfeifer Energy Steel IndustryEngineeringIndustrial EngineeringEnergy BalanceEnergy Efficiency	2011	200
Atmospheric leaching of EAF dust with diluted sulphuric acid Tomáš Havlík, Martina Turzakova, Sreċko Stopić, Hydrometallurgy Environmental ChemistryAerosol TransportAtmospheric Leaching	2005	138

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