Browsing 2013 Dissertations (MU) by Thesis Department "Computer science (MU)"

Now showing items 1-6 of 6

  • Active labeling in deep learning and its application to emotion prediction 

    Wang, Dan (University of Missouri--Columbia, 2013)
    Recent breakthroughs in deep learning have made possible the learning of deep layered hierarchical representations of sensory input. Stacked restricted Boltzmann machines (RBMs), also called deep belief networks (DBNs), ...

  • Geo-tagging and privacy-preservation in mobile cloud computing 

    Wang, Qia, 1985- (University of Missouri--Columbia, 2013)
    With the emerge of the cloud computing service and the explosive growth of the mobile devices and applications, mobile computing technologies and cloud computing technologies have been drawing significant attentions. Mobile ...

  • Improved computational methods of protein sequence alignment, model selection and tertiary structure prediction 

    Deng, Xin (University of Missouri--Columbia, 2013)
    Protein sequence and profile alignment has been used essentially in most bioinformatics tasks such as protein structure modeling, function prediction, and phylogenetic analysis. We designed a new algorithm MSACompro to ...

  • LIDAR data classification and compression 

    Li, Xiaoling (University of Missouri--Columbia, 2013)
    Airborne Laser Detection and Ranging (LIDAR) data has a wide range of applications in agriculture, archaeology, biology, geology, meteorology, military and transportation, etc. LIDAR data consumes hundreds of gigabytes in ...

  • On cross-domain social semantic learning 

    Deb Roy, Suman (University of Missouri--Columbia, 2013)
    Approximately 2.4 billion people are now connected to the Internet, generating massive amounts of data through laptops, mobile phones, sensors and other electronic devices or gadgets. Not surprisingly then, ninety percent ...

  • Predicting protein residue-residue contacts and disorder 

    Eickholt, Jesse (University of Missouri--Columbia, 2013)
    [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Predicting a protein's three dimensional structure from its corresponding sequence has long been an extremely important and challenging problem in the ...