Impact of machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy and survival outcomes in … A Tahmassebi, GJ Wengert, TH Helbich, Z Bago-Horvath, S Alaei, ... Investigative radiology 54 (2), 110-117, 2019 | 234 | 2019 |
Evolutionary machine learning: A survey A Telikani, A Tahmassebi, W Banzhaf, AH Gandomi ACM Computing Surveys (CSUR) 54 (8), 1-35, 2021 | 157 | 2021 |
Probabilistic neural networks: a brief overview of theory, implementation, and application B Mohebali, A Tahmassebi, A Meyer-Baese, AH Gandomi Handbook of probabilistic models, 347-367, 2020 | 119 | 2020 |
Deep learning in medical imaging: fmri big data analysis via convolutional neural networks A Tahmassebi, AH Gandomi, I McCann, MHJ Schulte, AE Goudriaan, ... Proceedings of the practice and experience on advanced research computing, 1-4, 2018 | 56 | 2018 |
Building energy consumption forecast using multi-objective genetic programming A Tahmassebi, AH Gandomi Measurement 118, 164-171, 2018 | 55 | 2018 |
AI‐Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer A Meyer‐Base, L Morra, A Tahmassebi, M Lobbes, U Meyer‐Base, ... Journal of magnetic resonance imaging 54 (3), 686-702, 2021 | 36 | 2021 |
Multi-stage optimization of a deep model: A case study on ground motion modeling A Tahmassebi, AH Gandomi, S Fong, A Meyer-Baese, SY Foo PloS one 13 (9), e0203829, 2018 | 32 | 2018 |
Optimized naive‐Bayes and decision tree approaches for fMRI smoking cessation classification A Tahmassebi, AH Gandomi, MHJ Schulte, AE Goudriaan, SY Foo, ... Complexity 2018 (1), 2740817, 2018 | 29 | 2018 |
ideeple: Deep learning in a flash A Tahmassebi Disruptive Technologies in Information Sciences 10652, 177-193, 2018 | 26 | 2018 |
XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions E Karbassiyazdi, F Fattahi, N Yousefi, A Tahmassebi, AA Taromi, ... Environmental Research 215, 114286, 2022 | 24 | 2022 |
An evolutionary approach for fmri big data classification A Tahmassebi, AH Gandomi, I McCann, MHJ Schulte, L Schmaal, ... 2017 IEEE Congress on Evolutionary Computation (CEC), 1029-1036, 2017 | 23 | 2017 |
An evolutionary online framework for MOOC performance using EEG data A Tahmassebi, AH Gandomi, A Meyer-Baese 2018 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2018 | 22 | 2018 |
Using machine learning to identify karst sinkholes from LiDAR-derived topographic depressions in the Bluegrass Region of Kentucky J Zhu, AM Nolte, N Jacobs, M Ye Journal of Hydrology 588, 125049, 2020 | 20 | 2020 |
Handbook of probabilistic models P Samui, DT Bui, S Chakraborty, R Deo Butterworth-Heinemann, 2019 | 18 | 2019 |
Determining disease evolution driver nodes in dementia networks A Tahmassebi, AM Amani, K Pinker-Domenig, A Meyer-Baese Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and …, 2018 | 17 | 2018 |
Genetic programming based on error decomposition: A big data approach A Tahmassebi, AH Gandomi Genetic programming theory and practice XV, 135-147, 2018 | 17 | 2018 |
A big data inspired preprocessing scheme for bandwidth use optimization in smart cities applications using raspberry pi B Mohebali, A Tahmassebi, AH Gandomi, A Meyer-Baese Big Data: Learning, Analytics, and Applications 10989, 1098902, 2019 | 15 | 2019 |
Genetic Programming Theory and Practice XVI W Banzhaf, L Spector, L Sheneman Springer International Publishing, 2019 | 15 | 2019 |
Big data analytics in medical imaging using deep learning A Tahmassebi, A Ehtemami, B Mohebali, AH Gandomi, K Pinker, ... Big Data: Learning, Analytics, and Applications 10989, 86-101, 2019 | 14 | 2019 |
High performance gp-based approach for fmri big data classification A Tahmassebi, AH Gandomi, A Meyer-Bäse Proceedings of the Practice and Experience in Advanced Research Computing …, 2017 | 14 | 2017 |