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    Ichthyoplankton Classification Tool using Generative Adversarial Networks and Transfer Learning

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    Nura_Thesis___Final_Results_and_modifications-6 (1).pdf
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    Type
    Thesis
    Authors
    Aljaafari, Nura cc
    Advisors
    Kalnis, Panos cc
    Committee members
    Duarte, Carlos M. cc
    Gao, Xin cc
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2018-04-15
    Embargo End Date
    2019-04-15
    Permanent link to this record
    http://hdl.handle.net/10754/627578
    
    Metadata
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    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 2019-04-15.
    Abstract
    The study and the analysis of marine ecosystems is a significant part of the marine science research. These systems are valuable resources for fisheries, improving water quality and can even be used in drugs production. The investigation of ichthyoplankton inhabiting these ecosystems is also an important research field. Ichthyoplankton are fish in their early stages of life. In this stage, the fish have relatively similar shape and are small in size. The currently used way of identifying them is not optimal. Marine scientists typically study such organisms by sending a team that collects samples from the sea which is then taken to the lab for further investigation. These samples need to be studied by an expert and usually end needing a DNA sequencing. This method is time-consuming and requires a high level of experience. The recent advances in AI have helped to solve and automate several difficult tasks which motivated us to develop a classification tool for ichthyoplankton. We show that using machine learning techniques, such as generative adversarial networks combined with transfer learning solves such a problem with high accuracy. We show that using traditional machine learning algorithms fails to solve it. We also give a general framework for creating a classification tool when the dataset used for training is a limited dataset. We aim to build a user-friendly tool that can be used by any user for the classification task and we aim to give a guide to the researchers so that they can follow in creating a classification tool.
    Citation
    Aljaafari, N. (2018). Ichthyoplankton Classification Tool using Generative Adversarial Networks and Transfer Learning. KAUST Research Repository. https://doi.org/10.25781/KAUST-K902H
    DOI
    10.25781/KAUST-K902H
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-K902H
    Scopus Count
    Collections
    MS Theses; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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