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    Exploring Ocean Animal Trajectory Pattern via Deep Learning

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    Type
    Thesis
    Authors
    Wang, Su cc
    Advisors
    Zhang, Xiangliang cc
    Committee members
    Gao, Xin cc
    Moshkov, Mikhail cc
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2016-05-23
    Embargo End Date
    2017-05-23
    Permanent link to this record
    http://hdl.handle.net/10754/610580
    
    Metadata
    Show full item record
    Access Restrictions
    At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2017-05-23.
    Abstract
    We trained a combined deep convolutional neural network to predict seals’ age (3 categories) and gender (2 categories). The entire dataset contains 110 seals with around 489 thousand location records. Most records are continuous and measured in a certain step. We created five convolutional layers for feature representation and established two fully connected structure as age’s and gender’s classifier, respectively. Each classifier consists of three fully connected layers. Treating seals’ latitude and longitude as input, entire deep learning network, which includes 780,000 neurons and 2,097,000 parameters, can reach to 70.72% accuracy rate for predicting seals’ age and simultaneously achieve 79.95% for gender estimation.
    Citation
    Wang, S. (2016). Exploring Ocean Animal Trajectory Pattern via Deep Learning. KAUST Research Repository. https://doi.org/10.25781/KAUST-29M51
    DOI
    10.25781/KAUST-29M51
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-29M51
    Scopus Count
    Collections
    MS Theses; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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