Digital Commons Network^™

Machine Learning And Solvation Theory For Drug Discovery, Lieyang Chen

Dissertations, Theses, and Capstone Projects

Drug discovery is a notoriously expensive and time-consuming process; hence, developing computational methods to facilitate the discovery process and lower the associated costs is a long-sought goal of computational chemists. Protein-ligand binding, which provides the physical and chemical basis for the mechanism of action of most drugs, occurs in an aqueous environment, and binding affinity is determined not only by atomic interactions between the protein and ligand but also by changes in their interactions with surrounding water molecules that occur upon binding. Thus, a quantitative understanding of the roles water molecules play in the protein-ligand binding process is an essential …

Applying Deep Learning On Financial Sentiment Analysis, Cuiyuan Wang

Dissertations, Theses, and Capstone Projects

Portfolio Investment has always been appealing to investors and researchers. In the past, people tend to use historical trading information of the securities to predict the return or manage the portfolio. Nowadays, the literature has been proved that the market sentiment could predict asset prices. Specifically, it has been shown that the stock market movement is related to financial news and social media events. Thus, it becomes necessary to extract the sentiment of the financial news. We explicitly introduce the application of dictionary methods, traditional machine learning models and deep learning models on text classification. The experiment results show that …

Machine Learning Classification Of Traumatic Brain Injury Patients Versus Healthy Controls Using Arterial Spin Labeled Perfusion Mri, Vanessa I. Grass

Dissertations, Theses, and Capstone Projects

Traumatic brain injury (TBI) is one of the most common causes of death and disability worldwide, yet accurate in vivo detection of TBI neuropathology remains challenging due to complexities in the structural and functional changes observed post-injury as well as limitations in conventional neuroimaging modalities. Although advanced neuroimaging techniques such as arterial spin labeling (ASL) can noninvasively assess cerebral blood flow (CBF) changes observed post-injury, this technique is underutilized in TBI research partly due to the low signal-to-noise-ratio (SNR) inherent in ASL imaging. The aim of the current study is to examine the use of machine learning, specifically a Support …

An Analysis Of Machine Learning Techniques For Economic Recession Prediction, Sheridan Kamal

Dissertations, Theses, and Capstone Projects

In this project I used the supervised machine learning methods logistic regression, decision tree classifier, k nearest neighbor classifier, and support vector classifier, to determine the best method to predict economic recessions. To do this, I used the function train_test_split to create training and testing sets and the function TimeSeriesSplit to create walk-forward cross validation sets to use when tuning the model parameters. Each machine learning method was trained on both scaled and unscaled data and was performed using default parameters and using the tuned parameters so that there were four models of each method. It was determined that the …

Genomic And Ecological Dimensions Of Malagasy Reptile And Amphibian Biodiversity, Arianna L. Kuhn

Dissertations, Theses, and Capstone Projects

A long history of isolation coupled with complex topographic and ecological landscapes makes Madagascar ideal for exploring the historical factors that have shaped patterns of population diversity and endemism. Many species-level studies have suggested Late Quaternary climate change may have influenced population dynamics in the tropics, but Madagascar’s ecologically unique biomes or individual species properties may have driven idiosyncratic responses to these shifts. Using community-scale population genetic data I implement a hierarchical approximate Bayesian computation (hABC) approach to evaluate the degree of synchronous population expansion during glacial cycles across herpetofaunal assemblages both within and across discrete biomes and taxonomic groups. …

Strategic Default And Moral Hazard In Real Estate: Insights From Machine Learning Applications, Arka Prava Bandyopadhyay

Dissertations, Theses, and Capstone Projects

Strategic default has been the achilles heel in academic finance for decades. By definition, whether a default has occurred due to strategic motive is unobservable. Moreover, a household has only so many avenues of conducting a strategic default. I use the context of commercial mortgages as property value as well property cashflow co-determine the default decision of these borrowers. I tease out the different strategic aspects of default from the ones emanating from liquidity constraints. The recent advances in Deep Neural Network (DNN), the advent of big data and the computational power associated with it has enabled me to disentangle …

Learn Biologically Meaningful Representation With Transfer Learning, Di He

Dissertations, Theses, and Capstone Projects

Machine learning has made significant contributions to bioinformatics and computational biol­ogy. In particular, supervised learning approaches have been widely used in solving problems such as bio­marker identification, drug response prediction, and so on. However, because of the limited availability of comprehensively labeled and clean data, constructing predictive models in super­ vised settings is not always desirable or possible, especially when using data­hunger, red­hot learning paradigms such as deep learning methods. Hence, there are urgent needs to develop new approaches that could leverage more readily available unlabeled data in driving successful machine learning ap­ plications in this area.

In my dissertation, …

A New Feature Selection Method Based On Class Association Rule, Sami A. Al-Dhaheri

Dissertations, Theses, and Capstone Projects

Feature selection is a key process for supervised learning algorithms. It involves discarding irrelevant attributes from the training dataset from which the models are derived. One of the vital feature selection approaches is Filtering, which often uses mathematical models to compute the relevance for each feature in the training dataset and then sorts the features into descending order based on their computed scores. However, most Filtering methods face several challenges including, but not limited to, merely considering feature-class correlation when defining a feature’s relevance; additionally, not recommending which subset of features to retain. Leaving this decision to the end-user may …

Goes-R Supervised Machine Learning, Ronald Adomako

Dissertations and Theses

The GOES-R series is a product line of four satellite, with two currently on-orbit (GOES-16 “East” and GOES-17 “West”). GOES-17 is susceptible to a Loop-Heat-Pipe (LHP) phenomenon where during Fall and Spring seasons, there are times of day where some of the infrared bands records inaccurate readings from the Advanced Baseline Imager (ABI). This occurs from joint astronomical behavior and position of the GOES-17. This calibration issue occurs when the LHP instrument fails to radiate the heat of the sun out of ABI. Predictive Calibration (pCal) is an algorithm developed by instrument vendors for the National Oceanic Atmospheric Agency (NOAA) …