Matching Items (17)
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- All Subjects: Machine Learning
- All Subjects: Food
- Creators: Berisha, Visar
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
How are perceptions of morality and disgust regarding meat consumption related to each other? Which factor is more salient in determining one's willingness to eat the meat of a specific animal? How do these answers vary across religious groups? This study investigates the ways that concepts like morality and disgust are related to food preferences and hopes to shed light on the mechanisms that enforce culturally sanctioned food taboos. The study compares 4 groups of people in the U.S.: Christians (n = 39), Hindus (n = 29), Jews (n = 23), and non-religious people (n = 63). A total of 154 participants were given surveys in which they rated their feelings about eating various animals. Data from Christian and non-religious groups exhibited similar patterns such as a high likelihood of eating a given animal when starving, while results from Jews and Hindus were consistent with their religion's respective food taboos. Despite these differences, morality and disgust are strongly correlated with one another in almost all instances. Moreover, morality and disgust are almost equally important considerations when determining willingness to eat when starving.
ContributorsParekh, Shaili Rajul (Author) / Hruschka, Daniel (Thesis director) / Jacobs, Mark (Committee member) / Barrett, The Honors College (Contributor) / School of Nutrition and Health Promotion (Contributor) / School of Human Evolution and Social Change (Contributor) / Hugh Downs School of Human Communication (Contributor)
Created2014-12
Description
The purpose of this thesis is to create an informational book on gluten-free living. It is our hope that by the end of the book readers will have a better understanding that living with a gluten intolerance or auto-immune disorder does not control one's life. Someone just needs to put in a bit more planning and time in order to travel or eat out. The book goes into detail on every condition on the gluten-sensitivity spectrum. It also goes in-depth on medicines, recipes, and travel.
ContributorsSnodgrass, Allison (Co-author) / Snodgrass, Amanda (Co-author) / Johnston, Carol (Thesis director) / Jacobs, Mark (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor)
Created2015-05
Description
The goal of this creative project is to document my grandmother’s traditional Gujarati recipes with the hopes of preserving her life and passion for cooking. This process included library research to investigate the history of Indian and Gujarati cuisine, spending time in the kitchen documenting the recipes in their entirety, practicing them on my own, writing the cookbook and including passages that weave in the history, my grandmother’s stories, and techniques and tools. After completing this process, the significant findings related to my grandmother’s life and her journey from birth to now. Her marriage to my grandfather at a young age, her journey and those who influenced her ability to cook, and her impact on my family were all effects that I had understood and known during my experiences with my grandmother. In this journey, I learned more about her thoughts and experiences that I never knew before. Our relationship has deepened ten-fold and while she may not be with me forever, I now have a tangible part of her that I can keep with me for the rest of my life.
ContributorsPatel, Ekta (Author) / Graff, Sarah (Thesis director) / Jacobs, Mark (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Divergence functions are both highly useful and fundamental to many areas in information theory and machine learning, but require either parametric approaches or prior knowledge of labels on the full data set. This paper presents a method to estimate the divergence between two data sets in the absence of fully labeled data. This semi-labeled case is common in many domains where labeling data by hand is expensive or time-consuming, or wherever large data sets are present. The theory derived in this paper is demonstrated on a simulated example, and then applied to a feature selection and classification problem from pathological speech analysis.
ContributorsGilton, Davis Leland (Author) / Berisha, Visar (Thesis director) / Cochran, Douglas (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This work details the bootstrap estimation of a nonparametric information divergence measure, the Dp divergence measure, using a power law model. To address the challenge posed by computing accurate divergence estimates given finite size data, the bootstrap approach is used in conjunction with a power law curve to calculate an asymptotic value of the divergence estimator. Monte Carlo estimates of Dp are found for increasing values of sample size, and a power law fit is used to relate the divergence estimates as a function of sample size. The fit is also used to generate a confidence interval for the estimate to characterize the quality of the estimate. We compare the performance of this method with the other estimation methods. The calculated divergence is applied to the binary classification problem. Using the inherent relation between divergence measures and classification error rate, an analysis of the Bayes error rate of several data sets is conducted using the asymptotic divergence estimate.
ContributorsKadambi, Pradyumna Sanjay (Author) / Berisha, Visar (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
It is a fact of modern food processing that the majority of products contain one or multiple food additives. Yet, while these additives see great abundance of use, the average consumer has relatively little knowledge about them and, more often than not, a negative opinion of their inclusion. This piece explores the discrepancy between these two realities by delving into the origins, histories of use, health effects, and misconceptions that surround a number of modern food additives, exploring along the way the social changes and regulatory history that brought about the legal landscape of food safety in the United States. Ten author-developed recipes are included at the end to encourage not only a conceptual, but also a practical familiarity with these same food additives.
ContributorsChismar, Adam (Author) / Boyce-Jacino, Katherine (Thesis director) / Jacobs, Mark (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor)
Created2021-12
Description
This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.
ContributorsGin, Taylor (Author) / McCarthy, Alexandra (Co-author) / Berisha, Visar (Thesis director) / Baumann, Alicia (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2022-05
Description
This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.
ContributorsMcCarthy, Alexandra (Author) / Gin, Taylor (Co-author) / Berisha, Visar (Thesis director) / Baumann, Alicia (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2022-05
Description
This dissertation explores applications of machine learning methods in service of the design of screening tests, which are ubiquitous in applications from social work, to criminology, to healthcare. In the first part, a novel Bayesian decision theory framework is presented for designing tree-based adaptive tests. On an application to youth delinquency in Honduras, the method produces a 15-item instrument that is almost as accurate as a full-length 150+ item test. The framework includes specific considerations for the context in which the test will be administered, and provides uncertainty quantification around the trade-offs of shortening lengthy tests. In the second part, classification complexity is explored via theoretical and empirical results from statistical learning theory, information theory, and empirical data complexity measures. A simulation study that explicitly controls two key aspects of classification complexity is performed to relate the theoretical and empirical approaches. Throughout, a unified language and notation that formalizes classification complexity is developed; this same notation is used in subsequent chapters to discuss classification complexity in the context of a speech-based screening test. In the final part, the relative merits of task and feature engineering when designing a speech-based cognitive screening test are explored. Through an extensive classification analysis on a clinical speech dataset from patients with normal cognition and Alzheimer’s disease, the speech elicitation task is shown to have a large impact on test accuracy; carefully performed task and feature engineering are required for best results. A new framework for objectively quantifying speech elicitation tasks is introduced, and two methods are proposed for automatically extracting insights into the aspects of the speech elicitation task that are driving classification performance. The dissertation closes with recommendations for how to evaluate the obtained insights and use them to guide future design of speech-based screening tests.
ContributorsKrantsevich, Chelsea (Author) / Hahn, P. Richard (Thesis advisor) / Berisha, Visar (Committee member) / Lopes, Hedibert (Committee member) / Renaut, Rosemary (Committee member) / Zheng, Yi (Committee member) / Arizona State University (Publisher)
Created2023