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- Creators: School of Accountancy
Description
Multiple sclerosis is a neurological disease that attacks the nerves in the central nervous system of the brain and spinal cord. Multiple sclerosis is a neurological disease that attacks the nerves in the central nervous system of the brain and spinal cord. The severity of multiple sclerosis varies based on the each person and the progression of the disease. There are roughly 2.5 million people that suffer from this disease that life is changed dramatically from being diagnosed with no main way to ease into adjusting to a new lifestyle. The increase of people that are diagnosed with multiple sclerosis, and with a majority of those people being diagnosed in their early 20’s, there is a need for an application that will help patients manage their health. Multiple sclerosis leads to a lifestyle change, which includes various treatment options as well as routine doctor appointments. The creation of the myMS Specialist application will allow patients with multiple sclerosis to live a more comfortable lifestyle while they easily track and manage their health through their mobile devices. Our application has seven components that all play an important role in adjusting to the new everyday lifestyle for a patient with multiple sclerosis. All seven components are largely intertwined with each other to help patients realize patterns in their diet, sleep, exercise and the weather that causes their symptoms to worsen. Our application not only connects to a patient’s doctor so that there is full access of information at all time to the doctor but provides beneficial research to help further the understanding of multiple sclerosis. This application will be marketed and available for purchase to not only patients but doctors. It is our goal to lessen the burden of a new lifestyle to a patient, create constant communication with one’s doctor and provide beneficial data to researchers.
ContributorsSaenz, Devon (Co-author) / Peterson, Tyler (Co-author) / Chomina-Chavez, Aram (Thesis director) / Staats, Cody (Committee member) / W. P. Carey School of Business (Contributor) / Herberger Institute for Design and the Arts (Contributor) / School of Accountancy (Contributor) / Sandra Day O'Connor College of Law (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The Wish List is a website that allows users to input URLs of products that they like into a wish list, much like Amazon's Wish List. The website also connects users to their Facebook friends who also use the application, so that users can view their friends' wish lists and "claim" products that they've purchased. This makes the Wish List like a registry as well. This report documents the functionality and the structure of the website, but the website itself is not yet released to the general public.
ContributorsChesley, Bryana Renee (Author) / Ahmad, Altaf (Thesis director) / Prince, Linda (Committee member) / Barrett, The Honors College (Contributor) / WPC Graduate Programs (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor)
Created2014-05
Description
Attributes - that delineating the properties of data, and connections - that describing the dependencies of data, are two essential components to characterize most real-world phenomena. The synergy between these two principal elements renders a unique data representation - the attributed networks. In many cases, people are inundated with vast amounts of data that can be structured into attributed networks, and their use has been attractive to researchers and practitioners in different disciplines. For example, in social media, users interact with each other and also post personalized content; in scientific collaboration, researchers cooperate and are distinct from peers by their unique research interests; in complex diseases studies, rich gene expression complements to the gene-regulatory networks. Clearly, attributed networks are ubiquitous and form a critical component of modern information infrastructure. To gain deep insights from such networks, it requires a fundamental understanding of their unique characteristics and be aware of the related computational challenges.
My dissertation research aims to develop a suite of novel learning algorithms to understand, characterize, and gain actionable insights from attributed networks, to benefit high-impact real-world applications. In the first part of this dissertation, I mainly focus on developing learning algorithms for attributed networks in a static environment at two different levels: (i) attribute level - by designing feature selection algorithms to find high-quality features that are tightly correlated with the network topology; and (ii) node level - by presenting network embedding algorithms to learn discriminative node embeddings by preserving node proximity w.r.t. network topology structure and node attribute similarity. As changes are essential components of attributed networks and the results of learning algorithms will become stale over time, in the second part of this dissertation, I propose a family of online algorithms for attributed networks in a dynamic environment to continuously update the learning results on the fly. In fact, developing application-aware learning algorithms is more desired with a clear understanding of the application domains and their unique intents. As such, in the third part of this dissertation, I am also committed to advancing real-world applications on attributed networks by incorporating the objectives of external tasks into the learning process.
My dissertation research aims to develop a suite of novel learning algorithms to understand, characterize, and gain actionable insights from attributed networks, to benefit high-impact real-world applications. In the first part of this dissertation, I mainly focus on developing learning algorithms for attributed networks in a static environment at two different levels: (i) attribute level - by designing feature selection algorithms to find high-quality features that are tightly correlated with the network topology; and (ii) node level - by presenting network embedding algorithms to learn discriminative node embeddings by preserving node proximity w.r.t. network topology structure and node attribute similarity. As changes are essential components of attributed networks and the results of learning algorithms will become stale over time, in the second part of this dissertation, I propose a family of online algorithms for attributed networks in a dynamic environment to continuously update the learning results on the fly. In fact, developing application-aware learning algorithms is more desired with a clear understanding of the application domains and their unique intents. As such, in the third part of this dissertation, I am also committed to advancing real-world applications on attributed networks by incorporating the objectives of external tasks into the learning process.
ContributorsLi, Jundong (Author) / Liu, Huan (Thesis advisor) / Faloutsos, Christos (Committee member) / He, Jingrui (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2019