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Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
Analyzing Relationships With Machine Learning, Oscar Ko
Analyzing Relationships With Machine Learning, Oscar Ko
Dissertations, Theses, and Capstone Projects
Procedurally, this project aims to take a dataset, analyze it, and offer insights to the audience in an easy-to-digest format. Conceptually, this project will seek to explore questions like: “Do couples that meet through online dating or dating apps have higher or lower quality relationships?”, “Can any features in this dataset help predict how a subject would rate their relationship quality?”, and “What other insights can I derive from using machine learning for exploratory analysis?” The intended audience for this project is anyone interested in romantic relationships or machine learning.
The dataset is from a Stanford University survey, “How Couples …
Computerized Classification Of Surface Spikes In Three-Dimensional Electron Microscopic Reconstructions Of Viruses, Younes Benkarroum
Computerized Classification Of Surface Spikes In Three-Dimensional Electron Microscopic Reconstructions Of Viruses, Younes Benkarroum
Dissertations, Theses, and Capstone Projects
The purpose of this research is to develop computer techniques for improved three-dimensional (3D) reconstruction of viruses from electron microscopic images of them and for the subsequent improved classification of the surface spikes in the resulting reconstruction. The broader impact of such work is the following.
Influenza is an infectious disease caused by rapidly-changing viruses that appear seasonally in the human population. New strains of influenza viruses appear every year, with the potential to cause a serious global pandemic. Two kinds of spikes – hemagglutinin (HA) and neuraminidase (NA) – decorate the surface of the virus particles and these proteins …
Assessing The Utility Of Imaging Radar For Identifying White Sand Vegetation Structure, Jessica Rosenqvist
Assessing The Utility Of Imaging Radar For Identifying White Sand Vegetation Structure, Jessica Rosenqvist
Dissertations and Theses
White sand vegetation communities are wide spread across South America; found in Peru, Venezuela, Brazilian Amazon and Guyana. They are distributed in patches ranging from <1 km2 to greater than tens of square kilometers and their origins and locations are still not well understood. The communities are related to a variety of factors (soil type, flooding, nutrient content and fire); hence a precise definition for the ecosystem is still not fully defined. Nevertheless, the result of these variations creates a unique environment for endemic plant and animal species to thrive. Furthermore, analysis of these areas has been very scattered and identification of local white sand areas (<1 km2) have not been accomplished. In addition, identification of these locations has currently only used optical satellite imagery (Landsat, MODIS). Hence, in this project, we have attempted to use synthetic aperture radar to create a classification system to locate the white sand vegetation systems. The goal is to be able to apply this method to identify white sand vegetation distribution across South America. The region of focus for this thesis has been in Aracá, a large white sand area located in Brazil in the State of Amazonas. Due to the lack of ground reference data, a classified map by Capurucho et al. (2013), generated using Landsat data, was used as a comparison and reference. JAXA’s ALOS-1 PALSAR (L-band), ESA’s Sentinel-1A (C-band) and NASA’s SRTM sensors were used for land classification. As microwave signals penetrate clouds and haze, the advantage of using sensors with this wavelength allows for an unobstructed coverage of the landscape all year round. Different combinations of polarizations and wavelengths were used during the analysis to try and separate the white sand vegetation from water and terra firme forest. The resulting classification images showed a 30% agreement with the classification map by Capurucho et al. It is important to note, that this number is in fact an agreement percentage as the map used was a classification image and coarse in resolution (due to the lack of reference data). Therefore, this value does not imply a bad classification. Future work will include time-series data, precise ground reference points and data from other sensors such as ALOS-2 PALSAR, to improve the classification accuracy.