Matching Items (172)
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- All Subjects: Machine Learning
- Creators: Barrett, The Honors College
- Creators: Walter Cronkite School of Journalism and Mass Communication
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
Description
It is important for organizations and businesses to have some kind of online presence, as there are enormous benefits, including utilizing the power marketing tools to provide services for people. However, creating a website with a strong presence is difficult, in addition to ranking your website to be on top of google. Thus, the goal of this project was to rank a website using several marketing tools to increase an organization’s search engine optimization (SEO) for the company, Artificial Grass Master.
ContributorsSanchez-Apodaca, Esperanza Angelica (Author) / Steve, Cho (Thesis director) / Cynthia, Reid (Committee member) / Tech Entrepreneurship & Mgmt (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
Description
Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic indoor or urban environments. Using recent improvements in the field of machine learning, this project proposes a new method of localization using networks with several wireless transceivers and implemented without heavy computational loads or high costs. This project aims to build a proof-of-concept prototype and demonstrate that the proposed technique is feasible and accurate.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
ContributorsChang, Roger (Co-author) / Kann, Trevor (Co-author) / Alkhateeb, Ahmed (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This project focuses on techniques contemporary American poets use in their work. Ten different poetry collections are analyzed for dominant writing styles and techniques, which I then apply to my own poems, concentrating on modeling that particular poet. I then reflect on those poems through an evaluation of my writing process, how those techniques were implemented, and how they affected the poem. In addition to these reviews and reflections, I also wrote three articles about the literary community and what I've learned from my interactions in that community. All these materials are organized into a website, which shows the connections between the different writings via links and menus. Creating this website brings all the materials together to demonstrate my growth as a poet, writer, and designer. This heavy focus on poetry and analysis has helped sharpen my critical thinking skills and has better prepared me for a career in design and journalism.
ContributorsHansen, Elizabeth Sarah (Author) / Murphy, Patricia (Thesis director) / Savard, Jeannine (Committee member) / Barrett, The Honors College (Contributor) / Department of English (Contributor) / Hugh Downs School of Human Communication (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor)
Created2015-05
Description
The Culture of Study Abroad is a pre-departure resource for prospective American study abroad students. This resource exists in the form of a multimedia website containing expert advice, helpful resources, and true stories of American students and faculty directors who have studied abroad. Through creative non-fiction storytelling, interviews, research, and photographs, readers are encouraged to take full advantage of studying abroad as a way to expand their global knowledge and understanding. This website offers advice on the topics of cultural observation, homestays, traveling while abroad, safety, and foreign language, in an attempt to better prepare students for the unique cultural experiences awaiting them abroad. Visit the website: www.thecultureofstudyabroad.wordpress.com
ContributorsPado, Madeline Grace (Author) / Scott Lynch, Jacquelyn (Thesis director) / Rausch, Kyle (Committee member) / Barrett, The Honors College (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor)
Created2015-05
Description
Late life domestic violence is essentially synonymous with domestic violence except that it specifically refers to older adults. Although there are many similarities between younger victims and older victims, there are also distinct differences. Older victims have unique risk factors and barriers, including generational factors that stem from socialization. Unfortunately, society lacks awareness of late life domestic violence. This is reflected in current state statutes as well as the limited services provided to victims of domestic violence. For example, there are currently elder abuse or dependent abuse adult statutes in every state, yet there is no statute that specifically addresses late life domestic violence. When it comes to services, many programs are geared toward younger victims and staff is typically not trained to work with older victims, so older victims often do not get the resources they need. Yet about 1 in 10 women over the age of 50 experience abuse by an intimate partner. This is a prevalent issue needing more attention. To bring awareness and educate people on this topic, a user friendly website was created that will provide information on late life domestic violence, resources for victims, and ways to share the information with others. The website provides information that will educate people on this issue, and also promotes advocacy for older victims.
ContributorsGarcia, Brittany Nicole (Author) / Bonifas, Robin (Thesis director) / Dodge, Nancie (Committee member) / Barrett, The Honors College (Contributor) / School of Social Work (Contributor)
Created2015-05
Description
Epilepsy affects numerous people around the world and is characterized by recurring seizures, prompting the ability to predict them so precautionary measures may be employed. One promising algorithm extracts spatiotemporal correlation based features from intracranial electroencephalography signals for use with support vector machines. The robustness of this methodology is tested through a sensitivity analysis. Doing so also provides insight about how to construct more effective feature vectors.
ContributorsMa, Owen (Author) / Bliss, Daniel (Thesis director) / Berisha, Visar (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2015-05
Description
For my thesis project, I created a website, titled Noise + Heat, to serve as a guide to local music in the Phoenix area. The idea is that someone who is unfamiliar with Phoenix music can visit my site and easily be able to find the latest news, new music releases, live music venues, and be able to familiarize themselves with local artists. I designed and built the site in Adobe Edge Animate, and created all content. The website can be found at this link: www.noiseplusheat.com
ContributorsDinell, Sarah Constance (Author) / Jacoby, Jim (Thesis director) / Dodge, Nancie (Committee member) / Barrett, The Honors College (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor)
Created2014-12