| 1 |
Recent advances in knowledge discovery for heterogeneous catalysis using machine learning
Gunay ME, Yildirim R
Catalysis Reviews-Science and Engineering, 63(1), 120, 2021 |
| 2 |
Statistical review of dry reforming of methane literature using decision tree and artificial neural network analysis
Sener AN, Gunay ME, Leba A, Yildirim R
Catalysis Today, 299, 289, 2018 |
| 3 |
Decision tree analysis of past publications on catalytic steam reforming to develop heuristics for high performance: A statistical review
Baysal M, Gunay ME, Yildirim R
International Journal of Hydrogen Energy, 42(1), 243, 2017 |
| 4 |
Constructing global models from past publications to improve design and operating conditions for direct alcohol fuel cells
Tapan NA, Gunay ME, Yildirim R
Chemical Engineering Research & Design, 105, 162, 2016 |
| 5 |
Knowledge extraction for water gas shift reaction over noble metal catalysts from publications in the literature between 2002 and 2012
Odabasi C, Gunay ME, Yildirim R
International Journal of Hydrogen Energy, 39(11), 5733, 2014 |
| 6 |
Network-Induced Supervised Learning: Network-Induced Classification (NI-C) and Network-Induced Regression (NI-R)
Reis MS
AIChE Journal, 59(5), 1570, 2013 |
| 7 |
Developing global reaction rate model for CO oxidation over Au catalysts from past data in literature using artificial neural networks
Gunay ME, Yildirim R
Applied Catalysis A: General, 468, 395, 2013 |
| 8 |
Modeling preferential CO oxidation over promoted Au/Al2O3 catalysts using decision trees and modular neural networks
Gunay ME, Yildirim R
Chemical Engineering Research & Design, 91(5), 874, 2013 |
| 9 |
Pattern matching of alarm flood sequences by a modified Smith-Waterman algorithm
Cheng Y, Izadi I, Chen TW
Chemical Engineering Research & Design, 91(6), 1085, 2013 |
| 10 |
IPL2 and 3 performance improvement method for process safety using event correlation analysis
Nishiguchi J, Takai T
Computers & Chemical Engineering, 34(12), 2007, 2010 |
