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    Thesis (4)
    SubjectThesis -- University of Missouri--Kansas City -- Computer science (3)Big data (2)Machine learning (2)Bioinformatics (1)Claustrum (1)... View MoreDate Issued2018 (2)2016 (2)Author/ContributorLee, Yugyung, 1960- (4)Albishri, Ahmed Awad H. (1)Bandi, Rakesh Reddy (1)Nagabhushan, Megha (1)Shen, Feichen (1)Advisor
    Lee, Yugyung, 1960- (4)
    Thesis Department
    Computer Science (UMKC) (4)
    Telecommunications and Computer Networking (UMKC) (1)

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    SigsSpace-Text: Parallel and Distributed Signature Learning in Text Analytics 

    Bandi, Rakesh Reddy (University of Missouri--Kansas City, 2016)
    extension, the proposed SigSpace-Text approach brings vital, practical information to signature learning approaches on several text classification tasks. The SigSpace-Text model supports incremental, distributed, and parallel learning using big data...

    Deep Assertion discovery using word embeddings 

    Nagabhushan, Megha (University of Missouri -- Kansas City, 2018)
    In recent years, there has been explosive growth in the amount of biomedical data (e.g., publications, notes from EHRs, clinical trial results), with the majority being unstructured data. As the volume of data is ...

    A Graph Analytics Framework for Knowledge Discovery 

    Shen, Feichen (2016)
    In the current data movement, numerous efforts have been made to convert and normalize a large number of traditionally structured and unstructured data to semi-structured data (e.g., RDF, OWL). With the increasing number ...

    Deep Learning for Semi-Automated Brain Claustrum Segmentation on Magnetic Resonance (MR) Images 

    Albishri, Ahmed Awad H. (University of Missouri--Kansas City, 2018)
    In recent years, Deep Learning (DL) has shown promising results with regard to conducting AI tasks such as computer vision and speech recognition. Specifically, DL demonstrated the state-of-the-art in computer vision ...

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