Data Science Commons^™

Applications Of Machine Learning Algorithms In Materials Science And Bioinformatics, Mohammed Quazi

Mathematics & Statistics ETDs

The piezoelectric response has been a measure of interest in density functional theory (DFT) for micro-electromechanical systems (MEMS) since the inception of MEMS technology. Piezoelectric-based MEMS devices find wide applications in automobiles, mobile phones, healthcare devices, and silicon chips for computers, to name a few. Piezoelectric properties of doped aluminum nitride (AlN) have been under investigation in materials science for piezoelectric thin films because of its wide range of device applicability. In this research using rigorous DFT calculations, high throughput ab-initio simulations for 23 AlN alloys are generated.

This research is the first to report strong enhancements of piezoelectric properties …

Leveraging Context Patterns For Medical Entity Classification, Garrett Johnston

Computer Science Senior Theses

The ability of patients to understand health-related text is important for optimal health outcomes. A system that can automatically annotate medical entities could help patients better understand health-related text. Such a system would also accelerate manual data annotation for this low-resource domain as well as assist in down- stream medical NLP tasks such as finding textual similarity, identifying conflicting medical advice, and aspect-based sentiment analysis. In this work, we investigate a state-of-the-art entity set expansion model, BootstrapNet, for the task of medical entity classification on a new dataset of medical advice text. We also propose EP SBERT, a simple model …

Exploring The Effectiveness Of Multiple-Exemplar Training For Visual Analysis Of Ab-Design Graphs, Verena S. Bethke

Dissertations, Theses, and Capstone Projects

In behavior analysis, data are usually analyzed using visual analysis of the graphed data. There are a wide range of methods used to visually analyze data, from a basic ‘textbook’ style approach to the use of visual aids, decision-rubrics, and computer-based approaches. In the literature, there have been some comparisons of the efficacy of different approaches. Visual analysis as a behavior can be taught using a variety of methods, independent of how the skill itself is to be performed. Teaching methods include lecture, online instruction, and equivalence-based instruction. There is not much research on the teaching of visual analysis specifically, …

A Comparison Of Machine Learning Techniques For Validating Students’ Proficiency In Mathematics, Alexander Avdeev

Dissertations, Theses, and Capstone Projects

A principal goal of this project was to compare several machine learning (ML) algorithms to explore and validate math proficiency classifications based on standardized test scores. The data used in these analyses came from the 6th-grade students’ mathematics assessment records of the New York State Education Department’s Testing Program (NYSTP). Our approach was to test a number of competing machine learning (ML) algorithms for classifying students’ as proficient based on their test scores and other demographic information. Our samples were drawn from the 2016 test-taking cohort of 6th-grade students (N=156,800). Five classifiers including multinominal logistic regression (MLR), XGBoost, Tree-As, Lagrangian …

Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness, Nicholas Furth

Theses

Machine learning models have been shown to be vulnerable against various backdoor and data poisoning attacks that adversely affect model behavior. Additionally, these attacks have been shown to make unfair predictions with respect to certain protected features. In federated learning, multiple local models contribute to a single global model communicating only using local gradients, the issue of attacks become more prevalent and complex. Previously published works revolve around solving these issues both individually and jointly. However, there has been little study on the effects of attacks against model fairness. Demonstrated in this work, a flexible attack, which we call Un-Fair …

Real Time Call-Flagging System To Respond To Suicidal Ideation In Call Centers, Vishnu Menon, Joseph Carrigan, Charles Floeder, Thomas Walton, Devin Mcguire

Honors Program: Senior Projects (Public)

The 2021-2022 Signature Performance Design Studio team developed a live audio call-flagging system that enables faster responses and new response pathways to veteran crises by call service representatives and their management team. Using a custom made deep learning model, live audio streaming server, and Teams broadcasting add-on, the system empowers Signature Performance call service representatives to make quicker and more well informed decisions to provide veteran’s the best care possible.

Generating A Dataset For Comparing Linear Vs. Non-Linear Prediction Methods In Education Research, Jack Mauro, Elena Martinez, Anna Bargagliotti

Honors Thesis

Machine learning is often used to build predictive models by extracting patterns from large data sets. Such techniques are increasingly being utilized to predict outcomes in the social sciences. One such application is predicting student success. Machine learning can be applied to predicting student acceptance and success in academia. Using these tools for education-related data analysis, may enable the evaluation of programs, resources and curriculum. Currently, research is needed to examine application, admissions, and retention data in order to address equity in college computer science programs. However, most student-level data sets contain sensitive data that cannot be made public. To …

Computational Approaches To Facilitate Automated Interchange Between Music And Art, Rao Hamza Ali

Computational and Data Sciences (PhD) Dissertations

Recently, there has been a tremendous increase in generating and synthesizing music and art using various computational techniques. An area that is still under-researched, however, is how one medium can be converted into the other, while maintaining the overall aesthetics. Over the last few centuries, artists, composers, and scholars, have attempted to use substitute one form of art for the other: by proposing techniques where music notes are synonymous to colors, by inventing instruments that combine the aesthetics of music and visual art, and by incorporating the two media in live performances. A widely accepted computational approach, for the conversion, …

Intraday Algorithmic Trading Using Momentum And Long Short-Term Memory Network Strategies, Andrew R. Whitinger Ii

Undergraduate Honors Theses

Intraday stock trading is an infamously difficult and risky strategy. Momentum and reversal strategies and long short-term memory (LSTM) neural networks have been shown to be effective for selecting stocks to buy and sell over time periods of multiple days. To explore whether these strategies can be effective for intraday trading, their implementations were simulated using intraday price data for stocks in the S&P 500 index, collected at 1-second intervals between February 11, 2021 and March 9, 2021 inclusive. The study tested 160 variations of momentum and reversal strategies for profitability in long, short, and market-neutral portfolios, totaling 480 portfolios. …

New Debiasing Strategies In Collaborative Filtering Recommender Systems: Modeling User Conformity, Multiple Biases, And Causality., Mariem Boujelbene

Electronic Theses and Dissertations

Recommender Systems are widely used to personalize the user experience in a diverse set of online applications ranging from e-commerce and education to social media and online entertainment. These State of the Art AI systems can suffer from several biases that may occur at different stages of the recommendation life-cycle. For instance, using biased data to train recommendation models may lead to several issues, such as the discrepancy between online and offline evaluation, decreasing the recommendation performance, and hurting the user experience. Bias can occur during the data collection stage where the data inherits the user-item interaction biases, such as …

New Accurate, Explainable, And Unbiased Machine Learning Models For Recommendation With Implicit Feedback., Khalil Damak

Electronic Theses and Dissertations

Recommender systems have become ubiquitous Artificial Intelligence (AI) tools that play an important role in filtering online information in our daily lives. Whether we are shopping, browsing movies, or listening to music online, AI recommender systems are working behind the scene to provide us with curated and personalized content, that has been predicted to be relevant to our interest. The increasing prevalence of recommender systems has challenged researchers to develop powerful algorithms that can deliver recommendations with increasing accuracy. In addition to the predictive accuracy of recommender systems, recent research has also started paying attention to their fairness, in particular …

Beyond Accuracy In Machine Learning., Aneseh Alvanpour

Electronic Theses and Dissertations

Machine Learning (ML) algorithms are widely used in our daily lives. The need to increase the accuracy of ML models has led to building increasingly powerful and complex algorithms known as black-box models which do not provide any explanations about the reasons behind their output. On the other hand, there are white-box ML models which are inherently interpretable while having lower accuracy compared to black-box models. To have a productive and practical algorithmic decision system, precise predictions may not be sufficient. The system may need to have transparency and be able to provide explanations, especially in applications with safety-critical contexts …

Nucleate Boiling Under Different Gravity Values: Numerical Simulations & Data-Driven Techniques., Sandipan Banerjee

Electronic Theses and Dissertations

Nucleate boiling is important in nuclear applications and cooling applications under earth gravity conditions. Under reduced gravity or microgravity environment, it is significant too, especially in space exploration applications. Although multiple studies have been performed on nucleate boiling, the effect of gravity on nucleate boiling is not well understood. This dissertation primarily deals with numerical simulations of nucleate boiling using an adaptive Moment-of-Fluid (MoF) method for a single vapor bubble (water vapor or Perfluoro-n-hexane) in saturated liquid for different gravity levels. Results concerning the growth rate of the bubble, specifically the departure diameter and departure time have been provided. The …

Dataset Evaluation For Data Trading Using Expected Loss And Homomorphic Encryption, Minsung Joo

Senior Honors Papers / Undergraduate Theses

Supervised machine learning suffers from the ``garbage-in garbage-out" phenomenon where the performance of a model is limited by the quality of the data. While a myriad of data is collected every second, there is no general rigorous method of evaluating the quality of a given dataset. This hinders fair pricing of data in scenarios where a buyer may look to buy data for use with machine learning. In this work, I propose using the expected loss corresponding to a dataset as a measure of its quality, relying on Bayesian methods for uncertainty quantification. Furthermore, I present a secure multi-party computation …

Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano

Electrical and Computer Engineering ETDs

Due to the increasing use of photovoltaic systems, power grids are vulnerable to the projection of shadows from moving clouds. An intra-hour solar forecast provides power grids with the capability of automatically controlling the dispatch of energy, reducing the additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This dissertation introduces a novel sky imager consisting of a long-wave radiometric infrared camera and a visible light camera with a fisheye lens. The imager is mounted on a solar tracker to maintain the Sun in the center of the images throughout the day, reducing the scattering effect produced …

Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian

Northeast Journal of Complex Systems (NEJCS)

In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts …

Telemetry Data Mining For Unmanned Aircraft Systems, Li Yu

Theses and Dissertations

With ever more data becoming available to the US Air Force, it is vital to develop effective methods to leverage this strategic asset. Machine learning (ML) techniques present a means of meeting this challenge, as these tools have demonstrated successful use in commercial applications. For this research, three ML methods were applied to a unmanned aircraft system (UAS) telemetry dataset with the aim of extracting useful insight related to phases of flight. It was shown that ML provides an advantage in exploratory data analysis and as well as classification of phases. Neural network models demonstrated the best performance with over …

Assessing Feature Representations For Instance-Based Cross-Domain Anomaly Detection In Cloud Services Univariate Time Series Data, Rahul Agrahari, Matthew Nicholson, Clare Conran, Haythem Assem, John D. Kelleher

Articles

In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly detection in a time series, we calculate and compare confidence intervals on the average performance of different feature sets across a number of different model types and cross-domain time-series datasets. Our results indicate that the catch22 time-series feature set augmented with features based on rolling mean and variance performs best on average, and that the difference in performance between this feature set and the next best …

Machine Learning For Predicting Risk Of Early Dropout In A Recovery Program For Opioid Use Disorder, Assaf Gottlieb, Andrea Yatsco, Christine Bakos-Block, James R Langabeer, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

BACKGROUND: An increase in opioid use has led to an opioid crisis during the last decade, leading to declarations of a public health emergency. In response to this call, the Houston Emergency Opioid Engagement System (HEROES) was established and created an emergency access pathway into long-term recovery for individuals with an opioid use disorder. A major contributor to the success of the program is retention of the enrolled individuals in the program.

METHODS: We have identified an increase in dropout from the program after 90 and 120 days. Based on more than 700 program participants, we developed a machine learning …

Development Of Advanced Machine Learning Models For Analysis Of Plutonium Surrogate Optical Emission Spectra, Ashwin P. Rao, Phillip R. Jenkins, John D. Auxier Ii, Michael B. Shattan, Anil K. Patnaik

Faculty Publications

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of …

A Predictive Model To Predict Cyberattack Using Self-Normalizing Neural Networks, Oluwapelumi Eniodunmo

Theses, Dissertations and Capstones

Cyberattack is a never-ending war that has greatly threatened secured information systems. The development of automated and intelligent systems provides more computing power to hackers to steal information, destroy data or system resources, and has raised global security issues. Statistical and Data mining tools have received continuous research and improvements. These tools have been adopted to create sophisticated intrusion detection systems that help information systems mitigate and defend against cyberattacks. However, the advancement in technology and accessibility of information makes more identifiable elements that can be used to gain unauthorized access to systems and resources. Data mining and classification tools …

Finding The Best Predictors For Foot Traffic In Us Seafood Restaurants, Isabel Paige Beaulieu

Honors Theses and Capstones

COVID-19 caused state and nation-wide lockdowns, which altered human foot traffic, especially in restaurants. The seafood sector in particular suffered greatly as there was an increase in illegal fishing, it is made up of perishable goods, it is seasonal in some places, and imports and exports were slowed. Foot traffic data is useful for business owners to have to know how much to order, how many employees to schedule, etc. One issue is that the data is very expensive, hard to get, and not available until months after it is recorded. Our goal is to not only find covariates that …

A Citizen-Science Approach For Urban Flood Risk Analysis Using Data Science And Machine Learning, Candace Agonafir

Dissertations and Theses

Street flooding is problematic in urban areas, where impervious surfaces, such as concrete, brick, and asphalt prevail, impeding the infiltration of water into the ground. During rain events, water ponds and rise to levels that cause considerable economic damage and physical harm. The main goal of this dissertation is to develop novel approaches toward the comprehension of urban flood risk using data science techniques on crowd-sourced data. This is accomplished by developing a series of data-driven models to identify flood factors of significance and localized areas of flood vulnerability in New York City (NYC). First, the infrastructural (catch basin clogs, …

Exploring Cyberterrorism, Topic Models And Social Networks Of Jihadists Dark Web Forums: A Computational Social Science Approach, Vivian Fiona Guetler

Graduate Theses, Dissertations, and Problem Reports (ETD)

This three-article dissertation focuses on cyber-related topics on terrorist groups, specifically Jihadists’ use of technology, the application of natural language processing, and social networks in analyzing text data derived from terrorists' Dark Web forums. The first article explores cybercrime and cyberterrorism. As technology progresses, it facilitates new forms of behavior, including tech-related crimes known as cybercrime and cyberterrorism. In this article, I provide an analysis of the problems of cybercrime and cyberterrorism within the field of criminology by reviewing existing literature focusing on (a) the issues in defining terrorism, cybercrime, and cyberterrorism, (b) ways that cybercriminals commit a crime in …

Using Landsat-Based Phenology Metrics, Terrain Variables, And Machine Learning For Mapping And Probabilistic Prediction Of Forest Community Types In West Virginia, Faith M. Hartley

Graduate Theses, Dissertations, and Problem Reports (ETD)

This study investigates the mapping of forest community types for the entire state of West Virginia, USA using Global Land Analysis and Discovery (GLAD) Phenology Metrics analysis ready data (ARD) derived from the Landsat time series and digital terrain variables derived from a digital terrain model (DTM). Both classifications and probabilistic predictions were made using random forest (RF) machine learning (ML) and training data derived from ground plots provided by the West Virginia Natural Heritage Program (WVNHP). The primary goal of this study is to explore the use of globally consistent ARD data for operational forest type mapping over a …

Classifying Blood Glucose Levels Through Noninvasive Features, Rishi Reddy

Graduate Theses, Dissertations, and Problem Reports (ETD)

Blood glucose monitoring is a key process in the prevention and management of certain chronic diseases, such as diabetes. Currently, glucose monitoring for those interested in their blood glucose levels are confronted with options that are primarily invasive and relatively costly. A growing topic of note is the development of non-invasive monitoring methods for blood glucose. This development holds a significant promise for improvement to the quality of life of a significant portion of the population and is overall met with great enthusiasm from the scientific community as well as commercial interest. This work aims to develop a potential pipeline …

Facial Landmark Feature Fusion In Transfer Learning Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Norou Diawara, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Automatic classification of child facial expressions is challenging due to the scarcity of image samples with annotations. Transfer learning of deep convolutional neural networks (CNNs), pretrained on adult facial expressions, can be effectively finetuned for child facial expression classification using limited facial images of children. Recent work inspired by facial age estimation and age-invariant face recognition proposes a fusion of facial landmark features with deep representation learning to augment facial expression classification performance. We hypothesize that deep transfer learning of child facial expressions may also benefit from fusing facial landmark features. Our proposed model architecture integrates two input branches: a …

Multi-Modality Automatic Lung Tumor Segmentation Method Using Deep Learning And Radiomics, Siqiu Wang

Theses and Dissertations

Delineation of the tumor volume is the initial and fundamental step in the radiotherapy planning process. The current clinical practice of manual delineation is time-consuming and suffers from observer variability. This work seeks to develop an effective automatic framework to produce clinically usable lung tumor segmentations. First, to facilitate the development and validation of our methodology, an expansive database of planning CTs, diagnostic PETs, and manual tumor segmentations was curated, and an image registration and preprocessing pipeline was established. Then a deep learning neural network was constructed and optimized to utilize dual-modality PET and CT images for lung tumor segmentation. …

Incorporating Ontological Information In Biomedical Entity Linking Of Phrases In Clinical Text, Evan French

Theses and Dissertations

Biomedical Entity Linking (BEL) is the task of mapping spans of text within biomedical documents to normalized, unique identifiers within an ontology. Translational application of BEL on clinical notes has enormous potential for augmenting discretely captured data in electronic health records, but the existing paradigm for evaluating BEL systems developed in academia is not well aligned with real-world use cases. In this work, we demonstrate a proof of concept for incorporating ontological similarity into the training and evaluation of BEL systems to begin to rectify this misalignment. This thesis has two primary components: 1) a comprehensive literature review and 2) …

On Performance Optimization And Prediction Of Parallel Computing Frameworks In Big Data Systems, Haifa Alquwaiee

Dissertations

A wide spectrum of big data applications in science, engineering, and industry generate large datasets, which must be managed and processed in a timely and reliable manner for knowledge discovery. These tasks are now commonly executed in big data computing systems exemplified by Hadoop based on parallel processing and distributed storage and management. For example, many companies and research institutions have developed and deployed big data systems on top of NoSQL databases such as HBase and MongoDB, and parallel computing frameworks such as MapReduce and Spark, to ensure timely data analyses and efficient result delivery for decision making and business …