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- All Subjects: Machine Learning
- All Subjects: Sustainability
- Creators: Computer Science and Engineering Program
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Description
RecyclePlus is an iOS mobile application that allows users to be knowledgeable in the realms of sustainability. It gives encourages users to be environmental responsible by providing them access to recycling information. In particular, it allows users to search up certain materials and learn about its recyclability and how to properly dispose of the material. Some searches will show locations of facilities near users that collect certain materials and dispose of the materials properly. This is a full stack software project that explores open source software and APIs, UI/UX design, and iOS development.
ContributorsTran, Nikki (Author) / Ganesh, Tirupalavanam (Thesis director) / Meuth, Ryan (Committee member) / Watts College of Public Service & Community Solut (Contributor) / Department of Information Systems (Contributor) / Computer Science and 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
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
The goal of this project is to gain and use knowledge of sustainability topics as a value-adding function for a business in the Tempe, AZ area and to develop the skills to approach and consult with business owners and staff about sustainable business options. Sustainability searches for a balance between society, economy and the environment where all three can thrive; therefore, the ideal project partner was a business that values the wellbeing of mankind, is locally owned and operated and promotes environmental stewardship. The Original Chop Shop Co in Tempe Arizona was appropriately selected. Throughout the duration of our partnership, I observed their daily routine, interviewed employees and managers and used the collected information to identify three areas of focus that have the largest potential to reduce The Original Chop Shop Company's impact on the environment. Information on the areas of recycling, composting, and food sourcing was researched and synthesized to make suggestions for ecofriendly changes to business practices. The scope of the project includes small changes in daily practices such as implementing a recycling and composting program and employee training sessions and minor investments such as purchasing a micro washer and silverware in order to eliminate nonrenewable plastic utensils. The scope does not include major renovations or investments in technology. The suggestions offered position The Original Chop Shop to conduct business in a way that does not compromise the health of the environment, society, or economy.
ContributorsFerry, Brianna Aislinn (Author) / Dooley, Kevin (Thesis director) / Darnall, Nicole (Committee member) / Barrett, The Honors College (Contributor) / W. P. Carey School of Business (Contributor) / School of Politics and Global Studies (Contributor)
Created2015-05
Description
Sustainability has been a growing topic since the 1970’s, but is truly taking shape today as society is beginning to understand the necessity of protecting our environment. Business organizations are following this ‘megatrend’ and are beginning to incorporate sustainable initiatives in their organizations from the inside out. The sports industry is no exception as they are extremely influential over the millions of fans that follow them, whom have a strong affiliation with their favorite team. The Arizona Diamondbacks understand this responsibility and seek to be a leader in their community by creating many sustainable initiatives within their organization and community. The current problem the organization faces, is that much of the community are not aware of their environmental commitment. This is in part due to a lack of marketing within the organization and to the Arizona valley. This project analyzes the sports industry’s commitment to sustainability and how the Arizona Diamondbacks compare to industry leaders. Included is a detailed marketing plan for the organization comprised of current initiatives and of new initiatives that the Diamondbacks could potentially carry out. The implementation of this proposal could deem extremely beneficial as it would strengthen their identity, unify their employees and engage fans, which will make them feel a deeper affiliation with the organization. The Diamondbacks have made a commitment to the environment, but it is time to deepen that commitment, set an example for people in the Valley and in turn, spark social change.
ContributorsBauman, Jillian (Co-author) / Hopson, Emma (Co-author) / Eaton, John (Thesis director) / Kutz, Elana (Committee member) / Barrett, The Honors College (Contributor) / W. P. Carey School of Business (Contributor) / Department of Management (Contributor) / Department of Marketing (Contributor) / School of Sustainability (Contributor)
Created2015-05
Description
Bots tamper with social media networks by artificially inflating the popularity of certain topics. In this paper, we define what a bot is, we detail different motivations for bots, we describe previous work in bot detection and observation, and then we perform bot detection of our own. For our bot detection, we are interested in bots on Twitter that tweet Arabic extremist-like phrases. A testing dataset is collected using the honeypot method, and five different heuristics are measured for their effectiveness in detecting bots. The model underperformed, but we have laid the ground-work for a vastly untapped focus on bot detection: extremist ideal diffusion through bots.
ContributorsKarlsrud, Mark C. (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
Derived from the idea that the utilization of sustainable practices could improve small business practice, this honors thesis offers a full business assessment and recommendations for improvements of a local, family-owned coffee shop, Gold Bar. A thorough analysis of the shop's current business practices and research on unnecessary expenses and waste guides this assessment.
ContributorsSorden, Clarissa (Co-author) / Boden, Alexandra (Co-author) / Darnall, Nicole (Thesis director) / Dooley, Kevin (Committee member) / Barrett, The Honors College (Contributor) / School of Sustainability (Contributor) / W. P. Carey School of Business (Contributor) / Department of Management (Contributor) / Department of Supply Chain Management (Contributor)
Created2015-05
Description
Twitter, the microblogging platform, has grown in prominence to the point that the topics that trend on the network are often the subject of the news and other traditional media. By predicting trends on Twitter, it could be possible to predict the next major topic of interest to the public. With this motivation, this paper develops a model for trends leveraging previous work with k-nearest-neighbors and dynamic time warping. The development of this model provides insight into the length and features of trends, and successfully generalizes to identify 74.3% of trends in the time period of interest. The model developed in this work provides understanding into why par- ticular words trend on Twitter.
ContributorsMarshall, Grant A (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
With the development of technology, there has been a dramatic increase in the number of machine learning programs. These complex programs make conclusions and can predict or perform actions based off of models from previous runs or input information. However, such programs require the storing of a very large amount of data. Queries allow users to extract only the information that helps for their investigation. The purpose of this thesis was to create a system with two important components, querying and visualization. Metadata was stored in Sedna as XML and time series data was stored in OpenTSDB as JSON. In order to connect the two databases, the time series ID was stored as a metric in the XML metadata. Queries should be simple, flexible, and return all data that fits the query parameters. The query language used was an extension of XQuery FLWOR that added time series parameters. Visualization should be easily understood and be organized in a way to easily find important information and details. Because of the possibility of a large amount of data being returned from a query, a multivariate heat map was used to visualize the time series results. The two programs that the system performed queries on was Energy Plus and Epidemic Simulation Data Management System. By creating such a system, it would be easier for people of the project's fields to find the relationship between metadata that leads to the desired results over time. Over the time of the thesis project, the overall software was completed, however the software must be optimized in order to take the enormous amount of data expected from the system.
ContributorsTse, Adam Yusof (Author) / Candan, Selcuk (Thesis director) / Chen, Xilun (Committee member) / Barrett, The Honors College (Contributor) / School of Music (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
Social media sites are platforms in which individuals discuss a wide range of topics and share a huge amount of information about themselves and their interests. So much of this information is encoded through unstructured text that users post on the these types of sites. There has been a considerable amount of work done in respect to sentiment analysis on these sites to infer users' opinions and preferences. However there is a gap where it may be difficult to infer how a user feels about particular pages or topics that they have not conveyed their sentiment for in a observable form. Collaborative filtering is a common method used to solve this problem with user data, but has only infrequently been used with sentiment information in order to make inferences about users preferences. In this paper we extend previous work on leveraging sentiment in collaborative filtering, specifically to approximate user sentiment and subsequently their vote for candidates in an online election. Sentiment is shown to be an effective tool for making these types of predictions in the absence of other more explicit user preference information. In addition to this, we present an evaluation of sentiment analysis methods and tools that are used in state of the art sentiment analysis systems in order to understand which of these methods to leverage in our experiments.
ContributorsBaird, James Daniel (Author) / Liu, Huan (Thesis director) / Wang, Suhang (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05