Physical Sciences and Mathematics Commons^™

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

An Analysis Of The Success Of Farmers Markets In Kentucky Using Logistic Regression And Support Vector Machines, Jeron Russell

Mahurin Honors College Capstone Experience/Thesis Projects

The purpose of this research is to look at the relationship that market-specific, economic, and demographic variables have with the success of farmers markets in Kentucky. It additionally seeks to build a tool for predicting farmers market success that could be used by policy makers to aid in decision-making processes concerning farmers markets. Logistic regression and Support Vector Machines (SVMs) are used on data acquired from the Kentucky Department of Agriculture and the American Community Survey in order to analyze the data in a traditional statistical approach as well as a machine learning approach. The results included an SVM model …

Sparsity And Weak Supervision In Quantum Machine Learning, Seyran Saeedi

Theses and Dissertations

Quantum computing is an interdisciplinary field at the intersection of computer science, mathematics, and physics that studies information processing tasks on a quantum computer. A quantum computer is a device whose operations are governed by the laws of quantum mechanics. As building quantum computers is nearing the era of commercialization and quantum supremacy, it is essential to think of potential applications that we might benefit from. Among many applications of quantum computation, one of the emerging fields is quantum machine learning. We focus on predictive models for binary classification and variants of Support Vector Machines that we expect to be …

Implementation Considerations For Mitigating Bias In Supervised Machine Learning, Bardia Bijani Aval

CSB and SJU Distinguished Thesis

Machine Learning (ML) is an important component of computer science and a mainstream way of making sense of large amounts of data. Although the technology is establishing new possibilities in different fields, there are also problems to consider, one of which is bias. Due to the inductive reasoning of ML algorithms in creating mathematical models, the predictions and trends found by the models will never necessarily be true – just more or less probable. Knowing this, it is unreasonable for us to expect the applied deductive reasoning of these models to ever be fully unbiased. Therefore, it is important that …

A Probabilistic Machine Learning Framework For Cloud Resource Selection On The Cloud, Syeduzzaman Khan

University of the Pacific Theses and Dissertations

The execution of the scientific applications on the Cloud comes with great flexibility, scalability, cost-effectiveness, and substantial computing power. Market-leading Cloud service providers such as Amazon Web service (AWS), Azure, Google Cloud Platform (GCP) offer various general purposes, memory-intensive, and compute-intensive Cloud instances for the execution of scientific applications. The scientific community, especially small research institutions and undergraduate universities, face many hurdles while conducting high-performance computing research in the absence of large dedicated clusters. The Cloud provides a lucrative alternative to dedicated clusters, however a wide range of Cloud computing choices makes the instance selection for the end-users. This thesis …

Detecting Myocardial Infarctions Using Machine Learning Methods, Aniruddh Mathur

Master's Projects

Myocardial Infarction (MI), commonly known as a heart attack, occurs when one of the three major blood vessels carrying blood to the heart get blocked, causing the death of myocardial (heart) cells. If not treated immediately, MI may cause cardiac arrest, which can ultimately cause death. Risk factors for MI include diabetes, family history, unhealthy diet and lifestyle. Medical treatments include various types of drugs and surgeries which can prove very expensive for patients due to high healthcare costs. Therefore, it is imperative that MI is diagnosed at the right time. Electrocardiography (ECG) is commonly used to detect MI. ECG …

Exploring Emotion Recognition For Vr-Ebt Using Deep Learning On A Multimodal Physiological Framework, Nicholas Dass

Faculty of Applied Science and Technology - Exceptional Student Work, Applied Computing Theses

Post-Traumatic Stress Disorder is a mental health condition that affects a growing number of people. A variety of PTSD treatment methods exist, however current research indicates that virtual reality exposure-based treatment has become more prominent in its use.Yet the treatment method can be costly and time consuming for clinicians and ultimately for the healthcare system. PTSD can be delivered in a more sustainable way using virtual reality. This is accomplished by using machine learning to autonomously adapt virtual reality scene changes. The use of machine learning will also support a more efficient way of inserting positive stimuli in virtual reality …

Editorial: Machine Learning In Biomolecular Simulations, Gennady M. Verkhivker, Vojtech Spiwok, Francesco Luigi Gervasio

Mathematics, Physics, and Computer Science Faculty Articles and Research

"Interest in machine learning is growing in all fields of science, industry, and business. This interest was not primarily initiated by new theoretical findings. Interestingly, the theoretical basis of the majority of machine learning techniques, such as artificial neural networks, decision trees, or kernel methods, have been known for a relatively long time. Instead, there are other effects that triggered the recent boom of machine learning."

Predicting Switch-Like Behavior In Proteins Using Logistic Regression On Sequence-Based Descriptors, Benjamin Strauss

Master's Projects

Ligands can bind at specific protein locations, inducing conformational changes such as those involving secondary structure. Identifying these possible switches from sequence, including homology, is an important ongoing area of research. We attempt to predict possible secondary structure switches from sequence in proteins using machine learning, specifically a logistic regression approach with 48 N-acetyltransferases as our learning set and 5 sirtuins as our test set. Validated residue binary assignments of 0 (no change in secondary structure) and 1 (change in secondary structure) were determined (DSSP) from 3D X-ray structures for sets of virtually identical chains crystallized under different conditions. Our …

Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae

Psychology Faculty Articles and Research

Background

As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Methods

Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.

Results

A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …

Evaluating Projections And Developing Projection Models For Daily Fantasy Basketball, Eric C. Evangelista

Master's Theses

Daily fantasy sports (DFS) has grown in popularity with millions of participants throughout the world. However, studies have shown that most profits from DFS contests are won by only a small percentage of players. This thesis addresses the challenges faced by DFS participants by evaluating sources that provide player projections for NBA DFS contests and by developing machine learning models that produce competitive player projections.

External sources are evaluated by constructing daily lineups based on the projections offered and evaluating those lineups in the context of all potential lineups, as well as those submitted by participants in competitive FanDuel DFS …

Using Computer Vision To Quantify Coral Reef Biodiversity, Niket Bhodia

Master's Projects

The preservation of the world’s oceans is crucial to human survival on this planet, yet we know too little to begin to understand anthropogenic impacts on marine life. This is especially true for coral reefs, which are the most diverse marine habitat per unit area (if not overall) as well as the most sensitive. To address this gap in knowledge, simple field devices called autonomous reef monitoring structures (ARMS) have been developed, which provide standardized samples of life from these complex ecosystems. ARMS have now become successful to the point that the amount of data collected through them has outstripped …

Stock Market Prediction Using Ensemble Of Graph Theory, Machine Learning And Deep Learning Models, Pratik Patil

Master's Projects

Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices. Even though some studies claim to get prediction accuracy higher than a random …

The New Legal Landscape For Text Mining And Machine Learning, Matthew Sag

Faculty Articles

Now that the dust has settled on the Authors Guild cases, this Article takes stock of the legal context for TDM research in the United States. This reappraisal begins in Part I with an assessment of exactly what the Authors Guild cases did and did not establish with respect to the fair use status of text mining. Those cases held unambiguously that reproducing copyrighted works as one step in the process of knowledge discovery through text data mining was transformative, and thus ultimately a fair use of those works. Part I explains why those rulings followed inexorably from copyright's most …

Ifocus: A Framework For Non-Intrusive Assessment Of Student Attention Level In Classrooms, Narayanan Veliyath

Electronic Theses and Dissertations

The process of learning is not merely determined by what the instructor teaches, but also by how the student receives that information. An attentive student will naturally be more open to obtaining knowledge than a bored or frustrated student. In recent years, tools such as skin temperature measurements and body posture calculations have been developed for the purpose of determining a student's affect, or emotional state of mind. However, measuring eye-gaze data is particularly noteworthy in that it can collect measurements non-intrusively, while also being relatively simple to set up and use. This paper details how data obtained from such …

Predicting Post-Procedural Complications Using Neural Networks On Mimic-Iii Data, Namratha Mohan

LSU Master's Theses

The primary focus of this paper is the creation of a Machine Learning based algorithm for the analysis of large health based data sets. Our input was extracted from MIMIC-III, a large Health Record database of more than 40,000 patients. The main question was to predict if a patient will have complications during certain specified procedures performed in the hospital. These events are denoted by the icd9 code 996 in the individuals' health record. The output of our predictive model is a binary variable which outputs the value 1 if the patient is diagnosed with the specific complication or 0 …

A Transfer Learning Approach For Sentiment Classification., Omar Abdelwahab

Electronic Theses and Dissertations

The idea of developing machine learning systems or Artificial Intelligence agents that would learn from different tasks and be able to accumulate that knowledge with time so that it functions successfully on a new task that it has not seen before is an idea and a research area that is still being explored. In this work, we will lay out an algorithm that allows a machine learning system or an AI agent to learn from k different domains then uses some or no data from the new task for the system to perform strongly on that new task. In order …

Cleaver: Classification Of Everyday Activities Via Ensemble Recognizers, Samantha Hsu

Master's Theses

Physical activity can have immediate and long-term benefits on health and reduce the risk for chronic diseases. Valid measures of physical activity are needed in order to improve our understanding of the exact relationship between physical activity and health. Activity monitors have become a standard for measuring physical activity; accelerometers in particular are widely used in research and consumer products because they are objective, inexpensive, and practical. Previous studies have experimented with different monitor placements and classification methods. However, the majority of these methods were developed using data collected in controlled, laboratory-based settings, which is not reliably representative of real …

Cryptovisor: A Cryptocurrency Advisor Tool, Matthew Baldree, Paul Widhalm, Brandon Hill, Matteo Ortisi

SMU Data Science Review

In this paper, we present a tool that provides trading recommendations for cryptocurrency using a stochastic gradient boost classifier trained from a model labeled by technical indicators. The cryptocurrency market is volatile due to its infancy and limited size making it difficult for investors to know when to enter, exit, or stay in the market. Therefore, a tool is needed to provide investment recommendations for investors. We developed such a tool to support one cryptocurrency, Bitcoin, based on its historical price and volume data to recommend a trading decision for today or past days. This tool is 95.50% accurate with …

A Multiple Classifier System For Predicting Best-Selling Amazon Products, Michael Kranzlein

Master of Science in Computer Science Theses

In this work, I examine a dataset of Amazon product metadata and propose a heterogeneous multiple classifier system for the task of identifying best-selling products in multiple categories. This system of classifiers consumes the product description and the featured product image as input and feeds them through binary classifiers of the following types: Convolutional Neural Network, Na¨ıve Bayes, Random Forest, Ridge Regression, and Support Vector Machine. While each individual model is largely successful in identifying best-selling products from non best-selling products and from worst-selling products, the multiple classifier system is shown to be stronger than any individual model in the …

Ai-Human Collaboration Via Eeg, Adam Noack

All College Thesis Program, 2016-2019

As AI becomes ever more competent and integrated into our lives, the issue of AI-human goal misalignment looms larger. This is partially because there is often a rift between what humans explicitly command and what they actually mean. Most contemporary AI systems cannot bridge this gap. In this study we attempted to reconcile the goals of human and machine by using EEG signals from a human to help a simulated agent complete a task.

Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels

SMU Data Science Review

In this paper, we present a comparative evaluation of deep learning approaches to network intrusion detection. A Network Intrusion Detection System (NIDS) is a critical component of every Internet connected system due to likely attacks from both external and internal sources. A NIDS is used to detect network born attacks such as Denial of Service (DoS) attacks, malware replication, and intruders that are operating within the system. Multiple deep learning approaches have been proposed for intrusion detection systems. We evaluate three models, a vanilla deep neural net (DNN), self-taught learning (STL) approach, and Recurrent Neural Network (RNN) based Long Short …

Cognitive Virtual Admissions Counselor, Kumar Raja Guvindan Raju, Cory Adams, Raghuram Srinivas

SMU Data Science Review

Abstract. In this paper, we present a cognitive virtual admissions counselor for the Master of Science in Data Science program at Southern Methodist University. The virtual admissions counselor is a system capable of providing potential students accurate information at the time that they want to know it. After the evaluation of multiple technologies, Amazon’s LEX was selected to serve as the core technology for the virtual counselor chatbot. Student surveys were leveraged to collect and generate training data to deploy the natural language capability. The cognitive virtual admissions counselor platform is currently capable of providing an end-to-end conversational dialog to …

Using Data Visualization To Inform Machine Learning Approaches, Eric N. Zielonka, Aleksandr Fritz

STEM Student Research Symposium Posters

Machine learning with big data is a complicated task to tackle. Using data visualizations, one can find trends, anomalies, and patterns to help select the appropriate approach to the problem in machine learning. Using 2D visualizations, we’ve displayed flight data on interactive maps, visualizing density and property changes in an area. We’ve also used frequency histograms to view the quantitative properties of each point to look for trends. Using scatterplots, anomalies in data collection were found. Other plots confirmed previously found trends and initial thoughts about the data. These visualizations helped inform a machine learning approach to our problem and …

Old English Character Recognition Using Neural Networks, Sattajit Sutradhar

Electronic Theses and Dissertations

Character recognition has been capturing the interest of researchers since the beginning of the twentieth century. While the Optical Character Recognition for printed material is very robust and widespread nowadays, the recognition of handwritten materials lags behind. In our digital era more and more historical, handwritten documents are digitized and made available to the general public. However, these digital copies of handwritten materials lack the automatic content recognition feature of their printed materials counterparts. We are proposing a practical, accurate, and computationally efficient method for Old English character recognition from manuscript images. Our method relies on a modern machine learning …

Accurate And Justifiable : New Algorithms For Explainable Recommendations., Behnoush Abdollahi

Electronic Theses and Dissertations

Websites and online services thrive with large amounts of online information, products, and choices, that are available but exceedingly difficult to find and discover. This has prompted two major paradigms to help sift through information: information retrieval and recommender systems. The broad family of information retrieval techniques has given rise to the modern search engines which return relevant results, following a user's explicit query. The broad family of recommender systems, on the other hand, works in a more subtle manner, and do not require an explicit query to provide relevant results. Collaborative Filtering (CF) recommender systems are based on algorithms …

Learning From Pairwise Proximity Data, Hamid Dadkhahi

Doctoral Dissertations

In many areas of machine learning, the characterization of the input data is given by a form of proximity measure between data points. Examples of such representations are pairwise differences, pairwise distances, and pairwise comparisons. In this work, we investigate different learning problems on data represented in terms of such pairwise proximities. More specifically, we consider three problems: masking (feature selection) for dimensionality reduction, extension of the dimensionality reduction for time series, and online collaborative filtering. For each of these problems, we start with a form of pairwise proximity which is relevant in the problem at hand. We evaluate the …

Categorizing Blog Spam, Brandon Bevans

Master's Theses

The internet has matured into the focal point of our era. Its ecosystem is vast, complex, and in many regards unaccounted for. One of the most prevalent aspects of the internet is spam. Similar to the rest of the internet, spam has evolved from simply meaning ‘unwanted emails’ to a blanket term that encompasses any unsolicited or illegitimate content that appears in the wide range of media that exists on the internet.

Many forms of spam permeate the internet, and spam architects continue to develop tools and methods to avoid detection. On the other side, cyber security engineers continue to …

Algorithmic Music Composition And Accompaniment Using Neural Networks, Daniel Wilton Risdon

Senior Projects Spring 2016

The goal of this project was to use neural networks as a tool for live music performance. Specifically, the intention was to adapt a preexisting neural network code library to work in Max, a visual programming language commonly used to create instruments and effects for electronic music and audio processing. This was done using ConvNetJS, a JavaScript library created by Andrej Karpathy.

Several neural network models were trained using a range of different training data, including music from various genres. The resulting neural network-based instruments were used to play brief pieces of music, which they used as input to create …

Epistemological Databases For Probabilistic Knowledge Base Construction, Michael Louis Wick

Doctoral Dissertations

Knowledge bases (KB) facilitate real world decision making by providing access to structured relational information that enables pattern discovery and semantic queries. Although there is a large amount of data available for populating a KB; the data must first be gathered and assembled. Traditionally, this integration is performed automatically by storing the output of an information extraction pipeline directly into a database as if this prediction were the ``truth.'' However, the resulting KB is often not reliable because (a) errors accumulate in the integration pipeline, and (b) they persist in the KB even after new information arrives that could rectify …