High Performance GP-Based Approach for fMRI Big Data Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Tahmassebi:2017:PEARC,

• 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 Data",
• 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

Citations