Improving Seafood Production Through Data Science Methods

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dc.contributor.advisorKhaiter, Peter A.
dc.contributor.authorTeimouri Lotfabadi, Bahareh
dc.date.accessioned2024-11-07T10:59:57Z
dc.date.available2024-11-07T10:59:57Z
dc.date.copyright2021-08-27
dc.date.issued2024-11-07
dc.date.updated2024-11-07T10:59:55Z
dc.degree.disciplineInformation Systems and Technology
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractGlobal production of seafood has quadrupled over the past 50 years. Seafood production is characterized by one of the highest waste rates in the food industry reaching up to 50% of the original raw material. Therefore, seafood companies are interested in reducing their waste rates, thus increasing production yields. In this thesis, we apply a Data Science (DS) methodology and suggest an extended DS framework to address theoretical and practical issues in the seafood industry. The framework encapsulates data processing, statistical, machine learning, visualization and optimization capabilities. The research employs unique real-world data collected in a seafood production facility over a 2-year period. The study will contribute to the economic well-being of the individual seafood producers as they could perform their business planning and forecasting in a more informed and predictive way as well as to the overall sustainability of the seafood industry due to the waste rate reduction.
dc.identifier.urihttps://hdl.handle.net/10315/42392
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectInformation technology
dc.subjectArtificial intelligence
dc.subjectEnvironmental economics
dc.subject.keywordsSeafood production
dc.subject.keywordsWaste rate
dc.subject.keywordsProduction yield
dc.subject.keywordsData science
dc.subject.keywordsMachine learning
dc.subject.keywordsFramework
dc.titleImproving Seafood Production Through Data Science Methods
dc.typeElectronic Thesis or Dissertation

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