Theses and dissertations

This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students. 

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Impact of Sleep Restriction on Muscle Recovery Following Eccentric Exercise

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This study was designed with the goal of measuring the effects of sleep deprivation on muscle function. Participants in this study consisted of 19 individuals, 11 of which were in the restricted group (age 251) and 8 were in the control group (age 231). Measurements of muscle function included isometric…

This study was designed with the goal of measuring the effects of sleep deprivation on muscle function. Participants in this study consisted of 19 individuals, 11 of which were in the restricted group (age 251) and 8 were in the control group (age 231). Measurements of muscle function included isometric strength, isokinetic velocity, and muscle soreness. Isometric strength and isokinetic velocity were taken for knee extension using a dynamometer. Muscle soreness was measured via a 100mm likert visual analogue scale for the step-up and step-down movements with the effected leg. Measurements were taken at baseline, and 48 hours after the damaging bout of eccentric exercise following either 8 hours of sleep per night or 3 hours of sleep per night. Results show that there were no statistical differences between groups for either measurements of isometric strength, isokinetic velocity, or muscle soreness. Due to possible confounding factors, future research needs to be conducted in order to get a better understanding of the effects of sleep deprivation on muscle function.
ContributorsSalmeron-Been, Aaron James (Author) / Dickinson, Jared (Thesis director) / Youngstedt, Shawn (Committee member) / School of Nutrition and Health Promotion (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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Characterizing the Performance of Machine Learning Algorithms: A Study and Novel Techniques

Description

Classification in machine learning is quite crucial to solve many problems that the world is presented with today. Therefore, it is key to understand one’s problem and develop an efficient model to achieve a solution. One technique to achieve greater model selection and thus further ease in problem solving is…

Classification in machine learning is quite crucial to solve many problems that the world is presented with today. Therefore, it is key to understand one’s problem and develop an efficient model to achieve a solution. One technique to achieve greater model selection and thus further ease in problem solving is estimation of the Bayes Error Rate. This paper provides the development and analysis of two methods used to estimate the Bayes Error Rate on a given set of data to evaluate performance. The first method takes a “global” approach, looking at the data as a whole, and the second is more “local”—partitioning the data at the outset and then building up to a Bayes Error Estimation of the whole. It is found that one of the methods provides an accurate estimation of the true Bayes Error Rate when the dataset is at high dimension, while the other method provides accurate estimation at large sample size. This second conclusion, in particular, can have significant ramifications on “big data” problems, as one would be able to clarify the distribution with an accurate estimation of the Bayes Error Rate by using this method.
ContributorsLattus, Robert (Author) / Dasarathy, Gautam (Thesis director) / Berisha, Visar (Committee member) / Turaga, Pavan (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2021-12