Amirhessam Tahmassebi
Amirhessam Tahmassebi
PhD in Computational Science, Department of Scientific Computing, Florida State University
Verified email at - Homepage
Cited by
Cited by
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
Evolutionary machine learning: A survey
A Telikani, A Tahmassebi, W Banzhaf, AH Gandomi
ACM Computing Surveys (CSUR) 54 (8), 1-35, 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
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
Building energy consumption forecast using multi-objective genetic programming
A Tahmassebi, AH Gandomi
Measurement 118, 164-171, 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
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
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
ideeple: Deep learning in a flash
A Tahmassebi
Disruptive Technologies in Information Sciences 10652, 177-193, 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
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
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
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
Handbook of probabilistic models
P Samui, DT Bui, S Chakraborty, R Deo
Butterworth-Heinemann, 2019
Genetic programming based on error decomposition: A big data approach
A Tahmassebi, AH Gandomi
Genetic programming theory and practice XV, 135-147, 2018
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
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
Genetic Programming Theory and Practice XVI
W Banzhaf, L Spector, L Sheneman
Springer International Publishing, 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
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
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