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High Performance GP-Based Approach for fMRI Big Data Classification

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Published:09 July 2017Publication History

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. 80% 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.

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          • Published in

            cover image ACM Other conferences
            PEARC '17: Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact
            July 2017
            451 pages
            ISBN:9781450352727
            DOI:10.1145/3093338
            • General Chair:
            • David Hart

            Copyright © 2017 Owner/Author

            Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 9 July 2017

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            • Refereed limited

            Acceptance Rates

            PEARC '17 Paper Acceptance Rate54of79submissions,68%Overall Acceptance Rate133of202submissions,66%

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