| Formation lithology classification using scalable gradient boosted decision trees VA Dev, MR Eden Computers & chemical engineering 128, 392-404, 2019 | 220 | 2019 |
| Gradient boosted decision trees for lithology classification VA Dev, MR Eden Computer aided chemical engineering 47, 113-118, 2019 | 76 | 2019 |
| Evaluating the boosting approach to machine learning for formation lithology classification VA Dev, MR Eden Computer aided chemical engineering 44, 1465-1470, 2018 | 31 | 2018 |
| Hybrid genetic algorithm-decision tree approach for rate constant prediction using structures of reactants and solvent for Diels-Alder reaction S Datta, VA Dev, MR Eden Computers & Chemical Engineering 106, 690-698, 2017 | 25 | 2017 |
| Mathematical principles of chemical product design and strategies LY Ng, NG Chemmangattuvalappil, VA Dev, MR Eden Computer Aided Chemical Engineering 39, 3-43, 2016 | 15 | 2016 |
| Comparison of tree based ensemble machine learning methods for prediction of rate constant of Diels-Alder reaction VA Dev, S Datta, NG Chemmangattuvalappil, MR Eden Computer Aided Chemical Engineering 40, 997-1002, 2017 | 14 | 2017 |
| Developing non-linear rate constant QSPR using decision trees and multi-gene genetic programming S Datta, VA Dev, MR Eden Computers & Chemical Engineering 127, 150-157, 2019 | 12 | 2019 |
| Structure generation of candidate reactants using signature descriptors VA Dev, NG Chemmangattuvalappil, MR Eden Computer Aided Chemical Engineering 33, 151-156, 2014 | 9 | 2014 |
| Developing QSPR for predicting DNA drug binding affinity of 9-Anilinoacridine derivatives using correlation-based adaptive LASSO algorithm S Datta, VA Dev, MR Eden Computer Aided Chemical Engineering 40, 2767-2772, 2017 | 8 | 2017 |
| Designing reactants and products with properties dependent on both structures VA Dev, NG Chemmangattuvalappil, MR Eden Computer Aided Chemical Engineering 37, 1445-1450, 2015 | 6 | 2015 |
| Using correlation based adaptive LASSO algorithm to develop QSPR of antitumour agents for DNA–drug binding prediction S Datta, VA Dev, MR Eden Computers & Chemical Engineering 122, 258-264, 2019 | 5 | 2019 |
| Multi-objective computer-aided molecular design of reactants and products VA Dev, NG Chemmangattuvalappil, MR Eden Computer Aided Chemical Engineering 38, 2055-2060, 2016 | 5 | 2016 |
| Reactant Structure Generation by Signature Descriptors and Real Coded Genetic Algorithm VA Dev, NG Chemmangattuvalappil, MR Eden Computer Aided Chemical Engineering 34, 291-296, 2014 | 5 | 2014 |
| Developing non-linear rate constant QSPR using decision trees and multi-gene genetic programming S Datta, VA Dev, MR Eden Computer Aided Chemical Engineering 44, 2473-2478, 2018 | 2 | 2018 |
| Relating Reaction Rate Constant to Structures of Reactants and Solvent Using a Hybrid GA-DT Approach S Datta, VA Dev, MR Eden Computer Aided Chemical Engineering 38, 2049-2054, 2016 | 2 | 2016 |
| Molecular Synthesis of Candidate Reactant structures Using Signature Descriptors VA Dev, R Namikis, NG Chemmangattuvalappil, MR Eden Proceedings of the 6th International Conference on Process Systems …, 2013 | 1 | 2013 |
| Towards the Computer-Aided Molecular Design of Reactants and Products VA Dev Auburn University, 2017 | | 2017 |
| Mathematical Principles of Chemical Product Design and LY Ng, NG Chemmangattuvalappil, VA Dev, MR Eden Tools For Chemical Product Design: From Consumer Products to Biomedicine 39, 1, 2016 | | 2016 |
Dr. Mario Richard EdenDean of Engineering and McMillan Professor of Chemical Engineering, Auburn Universityایمیل تأیید شده در auburn.edu