Matching Items (55)
Filtering by
- All Subjects: artificial intelligence
- All Subjects: Health
- Creators: Computer Science and Engineering Program
- Creators: School of International Letters and Cultures
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
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
According to the CDC, diabetes is the 7th leading cause of death in the U.S. and rates are continuing to rise nationally and internationally. Chronically elevated blood glucose levels can lead to type 2 diabetes and other complications. Medications can be used to treat diabetes, but often have side effects. Lifestyle and diet modifications can be just as effective as medications in helping to improve glycemic control, and prevent diabetes or improve the condition in those who have it. Studies have demonstrated that consuming vinegar with carbohydrates can positively impact postprandial glycemia in diabetic and healthy individuals. Continuous vinegar intake with meals may even reduce fasting blood glucose levels. Since vinegar is a primary ingredient in mustard, the purpose of this study was to determine if mustard consumption with a carbohydrate-rich meal (bagel and fruit juice) had an effect on the postprandial blood glucose levels of subjects. The results showed that mustard improved glycemia by 17% when subjects consumed the meal with mustard as opposed to the control. A wide variety of vinegars exists. The defining ingredient in all vinegars is acetic acid, behind the improvement in glycemic response observed with vinegar ingestion. Vinegar-containing foods range from mustard, to vinaigrette dressings, to pickled foods. The benefits of vinegar ingestion with carbohydrates are dose-dependent, meaning that adding even small amounts to meals can help. Making a conscious effort to incorporate these foods into meals, in addition to an overall healthy lifestyle, could provide an additional tool for diabetics and nondiabetics alike to consume carbohydrates in a healthier manner.
ContributorsJimenez, Gabriela (Author) / Johnston, Carol (Thesis director) / Lespron, Christy (Committee member) / School of Nutrition and Health Promotion (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Because children do not have the same decision-making powers as adults in matters affecting their health, their opinions have often been underrepresented in research (Bradding & Horstman, 1999). However, there is growing interest in the way that children view health because this knowledge elicits the development of more child-centered and effective approaches to health education and intervention (Bradding & Horstman, 1999). Professionals have often utilized the write-and-draw technique in school settings to gain a better understanding of how to best implement health education programs. The "bottom-up" approach of the write-and-draw method encourages participation and has been shown to elicit thoughtful responses about how children conceptualize health (Pridmore & Bendelow, 1995). This study uses the write-and-draw method to perform a cross- cultural comparison of child perspectives of health in the United States and Guatemala, countries that represent contrasting paradigms for child health. The results of this study are consistent with previous research, especially the emergent health themes. Children from the United States and Guatemala predominantly depicted health in terms of food. Guatemalan students were more likely to refer to hygienic practices and environmental conditions, while US children mentioned vegetables, water, and exercise as being healthy. For the unhealthy category, themes of poor hygiene, chips, fat/grease, fruit, carbohydrates, and environment were mentioned more often in Guatemala, while U.S. students listed sweets and fast food more frequently. Results support claims made in other literature that children's concepts of health are shaped by life experience and social context. Potential applications of the research include exposing areas (themes) where children are less likely to understand health implications and developing educational curriculum to increase a more comprehensive understanding of health.
ContributorsRenslow, Jillian Marie (Author) / Maupin, Jonathan (Thesis director) / BurnSilver, Shauna (Committee member) / Barrett, The Honors College (Contributor) / School of Human Evolution and Social Change (Contributor) / School of International Letters and Cultures (Contributor)
Created2015-05
Description
The objective of this research is to determine an approach for automating the learning of the initial lexicon used in translating natural language sentences to their formal knowledge representations based on lambda-calculus expressions. Using a universal knowledge representation and its associated parser, this research attempts to use word alignment techniques to align natural language sentences to the linearized parses of their associated knowledge representations in order to learn the meanings of individual words. The work includes proposing and analyzing an approach that can be used to learn some of the initial lexicon.
ContributorsBaldwin, Amy Lynn (Author) / Baral, Chitta (Thesis director) / Vo, Nguyen (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
Penetration testing is regarded as the gold-standard for understanding how well an organization can withstand sophisticated cyber-attacks. However, the recent prevalence of markets specializing in zero-day exploits on the darknet make exploits widely available to potential attackers. The cost associated with these sophisticated kits generally precludes penetration testers from simply obtaining such exploits – so an alternative approach is needed to understand what exploits an attacker will most likely purchase and how to defend against them. In this paper, we introduce a data-driven security game framework to model an attacker and provide policy recommendations to the defender. In addition to providing a formal framework and algorithms to develop strategies, we present experimental results from applying our framework, for various system configurations, on real-world exploit market data actively mined from the darknet.
ContributorsRobertson, John James (Author) / Shakarian, Paulo (Thesis director) / Doupe, Adam (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Telemedicine is a multipurpose tool that allows medical professionals to use technology as a means to evaluate, diagnose, and treat patients remotely. This paper focuses on the challenges that developing telemedicine programs face, specifically discussing target population, user experience, and physician adoption. Various users of telemedicine share their experiences overcoming such challenges with the greater goal of this paper being to facilitate the growth of telemedicine programs.
ContributorsPalakodaty, Shivani Venkatasri (Author) / Liss, Julie (Thesis director) / Berisha, Visar (Committee member) / School of International Letters and Cultures (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Classical planning is a field of Artificial Intelligence concerned with allowing autonomous agents to make reasonable decisions in complex environments. This work investigates
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
the application of deep learning and planning techniques, with the aim of constructing generalized plans capable of solving multiple problem instances. We construct a Deep Neural Network that, given an abstract problem state, predicts both (i) the best action to be taken from that state and (ii) the generalized “role” of the object being manipulated. The neural network was tested on two classical planning domains: the blocks world domain and the logistic domain. Results indicate that neural networks are capable of making such
predictions with high accuracy, indicating a promising new framework for approaching generalized planning problems.
ContributorsNakhleh, Julia Blair (Author) / Srivastava, Siddharth (Thesis director) / Fainekos, Georgios (Committee member) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records is an important step in furthering clinical care. One important aspect within these records is the presence of prescription information. Existing techniques for extracting prescription information — which includes medication names, dosages, frequencies, reasons for taking, and mode of administration — from unstructured text have focused on the application of rule- and classifier-based methods. While state-of-the-art systems can be effective in extracting many types of information, they require significant effort to develop hand-crafted rules and conduct effective feature engineering. This paper presents the use of a bidirectional LSTM with CRF tagging model initialized with precomputed word embeddings for extracting prescription information from sentences without requiring significant feature engineering. The experimental results, run on the i2b2 2009 dataset, achieve an F1 macro measure of 0.8562, and scores above 0.9449 on four of the six categories, indicating significant potential for this model.
ContributorsRawal, Samarth Chetan (Author) / Baral, Chitta (Thesis director) / Anwar, Saadat (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
DescriptionFresh15 is an iOS application geared towards helping college students eat healthier. This is based on a user's preferences of price range, food restrictions, and favorite ingredients. Our application also considers the fact that students may have to order their ingredients online since they don't have access to transportation.
ContributorsBailey, Reece (Co-author) / Fallah-Adl, Sarah (Co-author) / Meuth, Ryan (Thesis director) / McDaniel, Troy (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Most reliable nutrition information can be found online, but it can be nearly impossible to differentiate from the unreliable blogs and websites that claim their information is correct. Because of this, it can be difficult for students to determine which information is true and which advice they will follow. During this time of growth and learning, it is essential that students have access to accurate information that will help them to be healthier individuals for years to come. The goal of this project was to provide students with an easily accessible and reliable resource for nutrition information that was presented in a simple and relatable way. The following videos and attached materials were created in response to ASU student needs and will be available for students on the ASU wellness website. Eating Healthy on a Budget: https://youtu.be/H-IUArD0phY Healthy Choices at Fast Food Restaurants: https://youtu.be/ZxcjBblpRtM Quick Healthy Meals: https://youtu.be/7uIDFe15-dM
ContributorsBaum, Makenna (Author) / Dixon, Kathleen (Thesis director) / Levinson, Simin (Committee member) / School of Nutrition and Health Promotion (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12