Date of Award

Spring 2020

Document Type

Open Access Dissertation

Department

Computer Science and Engineering

First Advisor

Homayoun Valafar

Abstract

The development of theoretical computational methods and their application has become widespread in the world today. In this dissertation, I present my work in the creation of models to detect and describe complex biological and health related problems. The first major part of my work centers around the creation and enhancement of methods to calculate protein structure and dynamics. To this end, substantial enhancement has been made to the software package REDCRAFT to better facilitate its usage in protein structure calculation. The enhancements have led to an overall increase in its ability to characterize proteins under difficult conditions such as high noise and low data density. Secondly, a database that allows for easy and comprehensive mining of protein structures has been created and deployed. We show preliminary results for its application to protein structure calculation. This database, among other applications, can be used to create input sets for computational models for prediction of protein structure. Lastly, I present my work on the creation of a theoretical model to describe discrete state protein dynamics. The results of this work can be used to describe many real-world dynamic systems. The second major part of my work centers around the application of machine learning techniques to create a system for the automated detection of smoking using accelerometer data from smartwatches. The first aspect of this work that will be presented is binary detection of smoking puffs. This model was then expanded to perform full cigarette session detection. Next, the model was reformulated to perform quantification of smoking (such as puff duration and the time between puffs). Lastly, a rotational matrix was derived to resolve ambiguities of smartwatches due to position of the watch on the wrist.

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