High Performance GP-Based Approach for fMRI Big Data Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Amirhessam Tahmassebi and Amir H. Gandomi and 
                 Anke Meyer-Baese",
  title =        "High Performance {GP}-Based Approach for {fMRI} Big
                 Data Classification",
  booktitle =    "Proceedings of the Practice and Experience in Advanced
                 Research Computing 2017 on Sustainability, Success and
                 Impact, PEARC17",
  year =         "2017",
  pages =        "57:1--57:4",
  address =      "New Orleans, LA, USA",
  month =        jul # " 9-13",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming,
                 Classification, High Performance Computing, fMRI Big
  articleno =    "57",
  isbn13 =       "978-1-4503-5272-7",
  acmid =        "3104145",
  DOI =          "doi:10.1145/3093338.3104145",
  abstract =     "We consider resting-state Functional Magnetic
                 Resonance Imaging (fMRI) of two classes of patients:
                 one that took the drug N-acetylcysteine (NAC) and the
                 other one a placebo before and after a smoking
                 cessation treatment. Our goal was to classify the
                 relapse in nicotine-dependent patients as treatment or
                 non-treatment based on their fMRI scans. 80percent
                 accuracy was obtained using Independent Component
                 Analysis (ICA) along with Genetic Programming (GP)
                 classifier using High Performance Computing (HPC) which
                 we consider significant enough to suggest that there is
                 a difference in the resting-state fMRI images of a
                 smoker that undergoes this smoking cessation treatment
                 compared to a smoker that receives a placebo.",

Genetic Programming entries for Amirhessam Tahmassebi A H Gandomi Anke Meyer-Baese