Matching Items (120)
Filtering by
- All Subjects: Machine Learning
- All Subjects: Marketing
- All Subjects: Founders Lab
- Creators: Computer Science and Engineering Program
- Creators: Department of Supply Chain Management
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
- Status: Published
Description
The contemporary world is motivated by data-driven decision-making. Small 501(c)3 nonprofit organizations are often limited in their reach due to their size, lack of funding, and a lack of data analysis expertise. In an effort to increase accessibility to data analysis for such organizations, a Founders Lab team designed a product to help them understand and utilize geographic information systems (GIS) software. This product – You Got GIS – strikes the balance between highly technical documentation and general overviews, benefiting 501(c)3 nonprofits in their pursuit of data-driven decision-making. Through the product’s use of case studies and methodologies, You Got GIS serves as a thought experiment platform to start answering questions regarding GIS. The product aims to continuously build partnerships in an effort to improve curriculum and user engagement.
ContributorsFletcher, Griffin (Co-author) / Heekin, Noah (Co-author) / Ferrara, John (Co-author) / Byrne, Jared (Thesis director) / Givens, Jessica (Committee member) / Satpathy, Asish (Committee member) / Historical, Philosophical & Religious Studies (Contributor) / Department of Supply Chain Management (Contributor) / Department of Economics (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
When you are sitting at the terminal waiting for your flight or taking the bus to get to work, have you ever thought about who used your seat last? More importantly, have you ever thought about the last time that seat was cleaned? Sadly, it is uncertain to see if it was properly sanitized in the last hour, yesterday, in the last week, or even last month. Especially during these tough times, everyone wants to be assured that they are always in a safe and healthy environment. Through the Founders Lab, our team is collaborating with an engineering capstone team to bring automated seat cleaning technology into the market. This product is a custom-designed seat cover that is tear-resistant and provides a sanitary surface for anyone to sit on. When someone leaves the seat, a pressure sensor is triggered, and the cover is replaced with a secondary cover that was stored in a UV radiated container. The waterproof fabric and internal filters prevent spills and food crumbs from remaining when the user changes. The reason for bringing this product into the market is due to the unsanitary conditions in many high traffic areas. This technology can be implemented in public transportation, restaurants, sports stadiums, and much more. It will instantly improve the efficiency of sanitation for many businesses and keep a promise to its users that they will never bring something they sat on back home. #Safeseating
ContributorsJawahar, Nandita (Co-author) / Yang, Tiger (Co-author) / Nimmagadda, Viraj (Co-author) / Byrne, Jared (Thesis director) / Sebold, Brent (Committee member) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / School of Community Resources and Development (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The current model of revenue generation for some free to play video games is preventing the companies controlling them from growing, but with a few changes in approach these issues could be alleviated. A new style of video games, called a MOBA (Massive Online Battle Arena) has emerged in the past few years bringing with it a new style of generating wealth. Contrary to past gaming models, where users must either purchase the game outright, view advertisements, or purchase items to gain a competitive advantage, MOBAs require no payment of any kind. These are free to play computer games that provides users with all the tools necessary to compete with anyone free of charge; no advantages can be purchased in this game. This leaves the only way for users to provide money to the company through optional purchases of purely aesthetic items, only to be purchased if the buyer wishes to see their character in a different set of attire. The genre’s best in show—called League of Legends, or LOL—has spearheaded this method of revenue-generation. Fortunately for LOL, its level of popularity has reached levels never seen in video games: the world championships had more viewers than game 7 of the NBA Finals (Dorsey). The player base alone is enough to keep the company afloat currently, but the fact that they only convert 3.75% of the players into revenue is alarming. Each player brings the company an average of $1.32, or 30% of what some other free to play games earn per user (Comparing MMO). It is this low per player income that has caused Riot Games, the developer of LOL, to state that their e-sports division is not currently profitable. To resolve this issue, LOL must take on a more aggressive marketing plan. Advertisements for the NBA Finals cost $460,000 for 30 seconds, and LOL should aim for ads in this range (Lombardo). With an average of 3 million people logged on at any time, 90% of the players being male and 85% being between the ages of 16 and 30, advertising via this game would appeal to many companies, making a deal easy to strike (LOL infographic 2012). The idea also appeals to players: 81% of players surveyed said that an advertisement on the client that allows for the option to place an order would improve or not impact their experience. Moving forward with this, the gaming client would be updated to contain both an option to order pizza and an advertisement for Mountain Dew. This type of advertising was determined based on community responses through a sequence of survey questions. These small adjustments to the game would allow LOL to generate enough income for Riot Games to expand into other areas of the e-sports industry.
ContributorsSeip, Patrick (Co-author) / Zhao, BoNing (Co-author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Barrett, The Honors College (Contributor) / Sandra Day O'Connor College of Law (Contributor) / Department of Economics (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
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
ASU's international student population has been growing exponentially in the last few years. Specifically, the fastest growing group has been international students from China. However, many of these students are arriving with inaccurate expectations of life at an American university. Furthermore, prospective students in China that have a desire to attend school in the U.S. are struggling to find a university that is affordable and respected. There is a huge opportunity for ASU to reach this market of students and increase their enrollment of international Chinese students. Our project aimed to create advertisements of ASU that target international Chinese students and their parents. The purpose of our project is to provide inspiration that ASU can utilize to create a professional marketing campaign to target this population of potential students.
ContributorsKagiyama, Kristen (Co-author) / Le, Alethea (Co-author) / Chien, Hsui Fen (Thesis director) / Chau, Angie (Committee member) / W. P. Carey School of Business (Contributor) / Department of Marketing (Contributor) / Department of Supply Chain Management (Contributor) / School of International Letters and Cultures (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Food safety is vital to the well-being of society; therefore, it is important to inspect food products to ensure minimal health risks are present. A crucial phase of food inspection is the identification of foreign particles found in the sample, such as insect body parts. The presence of certain species of insects, especially storage beetles, is a reliable indicator of possible contamination during storage and food processing. However, the current approach to identifying species is visual examination by human analysts; this method is rather subjective and time-consuming. Furthermore, confident identification requires extensive experience and training. To aid this inspection process, we have developed in collaboration with FDA analysts some image analysis-based machine intelligence to achieve species identification with up to 90% accuracy. The current project is a continuation of this development effort. Here we present an image analysis environment that allows practical deployment of the machine intelligence on computers with limited processing power and memory. Using this environment, users can prepare input sets by selecting images for analysis, and inspect these images through the integrated pan, zoom, and color analysis capabilities. After species analysis, the results panel allows the user to compare the analyzed images with referenced images of the proposed species. Further additions to this environment should include a log of previously analyzed images, and eventually extend to interaction with a central cloud repository of images through a web-based interface. Additional issues to address include standardization of image layout, extension of the feature-extraction algorithm, and utilizing image classification to build a central search engine for widespread usage.
ContributorsMartin, Daniel Luis (Author) / Ahn, Gail-Joon (Thesis director) / Doupé, Adam (Committee member) / Xu, Joshua (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In today’s society we see a strong encouragement of those who put effort into various aspects of their life. Additionally, we also see a strong push towards making oneself more attractive to reap social benefits. However, a paradox exists between effort and attractiveness. In a society that values both effort and attractiveness, why do we see negative reactions to those who put effort into their appearance, and can we make these effects go away? How can cosmetic companies alter those reactions to suit their advertising needs? Through a pretest and a main study we show how consumers react to differing amounts of perceived effort in a cosmetic product, and how we can alter the effect that effort has by priming consumers with the idea of their ‘natural self’ vs. their ‘ideal’ self.
ContributorsDaniels, Michelle Elizabeth (Author) / Samper, Adriana (Thesis director) / Montoya, Detra (Committee member) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor) / Department of Marketing (Contributor)
Created2014-05
Description
A fun, interactive, and practical motivational speaking package designed to inspire and encourage high school and college students, as well as young adults, to achieve success and discover their leadership potential. Using secrets learned from starting my own business, Board Blazers LED Underglow Skateboard Lighting, and performing as Drum Major of the 400+ member ASU Sun Devil Marching Band, I share tips and tricks that can be applied in everyday life. Topics include surviving in difficult leadership situations unique to young leaders, celebrity confidence secrets, and creating infectious enthusiasm while working on a team.
ContributorsRudolph, Gregory James (Author) / Eaton, John (Thesis director) / Desch, Timothy (Committee member) / Barrett, The Honors College (Contributor) / Department of Marketing (Contributor) / Department of Supply Chain Management (Contributor) / W. P. Carey School of Business (Contributor)
Created2014-05
Description
Due to the popularity of the movie industry, a film's opening weekend box-office performance is of great interest not only to movie studios, but to the general public, as well. In hopes of maximizing a film's opening weekend revenue, movie studios invest heavily in pre-release advertisement. The most visible advertisement is the movie trailer, which, in no more than two minutes and thirty seconds, serves as many people's first introduction to a film. The question, however, is how can we be confident that a trailer will succeed in its promotional task, and bring about the audience a studio expects? In this thesis, we use machine learning classification techniques to determine the effectiveness of a movie trailer in the promotion of its namesake. We accomplish this by creating a predictive model that automatically analyzes the audio and visual characteristics of a movie trailer to determine whether or not a film's opening will be successful by earning at least 35% of a film's production budget during its first U.S. box office weekend. Our predictive model performed reasonably well, achieving an accuracy of 68.09% in a binary classification. Accuracy increased to 78.62% when including genre in our predictive model.
ContributorsWilliams, Terrance D'Mitri (Author) / Pon-Barry, Heather (Thesis director) / Zafarani, Reza (Committee member) / Maciejewski, Ross (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05