Physical Sciences and Mathematics Commons^™

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 …

A Data Driven Approach To Forecast Demand, Hannah Kosinovsky, Sita Daggubati, Kumar Ramasundaram, Brent Allen

SMU Data Science Review

Abstract. In this paper, we present a model and methodology for accurately predicting the following quarter’s sales volume of individual products given the previous five years of sales data. Forecasting product demand for a single supplier is complicated by seasonal demand variation, business cycle impacts, and customer churn. We developed a novel prediction using machine learning methodology, based upon a Dense neural network (DNN) model that implicitly considers cyclical demand variation and explicitly considers customer churn while minimizing the least absolute error between predicted demand and actual sales. Using parts sales data for a supplier to the oil and gas …

Ordinal Hyperplane Loss, Bob Vanderheyden

Doctor of Data Science and Analytics Dissertations

This research presents the development of a new framework for analyzing ordered class data, commonly called “ordinal class” data. The focus of the work is the development of classifiers (predictive models) that predict classes from available data. Ratings scales, medical classification scales, socio-economic scales, meaningful groupings of continuous data, facial emotional intensity and facial age estimation are examples of ordinal data for which data scientists may be asked to develop predictive classifiers. It is possible to treat ordinal classification like any other classification problem that has more than two classes. Specifying a model with this strategy does not fully utilize …

Toward Early Detection Of Pancreatic Cancer: An Evidence-Based Approach, Omid Sharagi

Master's Projects

This study observes how an evidential reasoning approach can be used as a diagnostic tool for early detection of pancreatic cancer. The evidential reasoning model combines the output of a linear Support Vector Classifier (SVC) with factors such as smoking history, health history, biopsy location, NGS technology used, and more to predict the likelihood of the disease. The SVC was trained using genomic data of pancreatic cancer patients derived from the National Cancer Institute (NIH) Genomic Data Commons (GDC). To test the evidential reasoning model, a variety of synthetic data was compiled to test the impact of combinations of different …

Dronescape:Distributed Rapid On-Site Network Self-Deploying Cellular Advanced Phone Environment, Daryl Johnson, Bill Stackpole

Presentations and other scholarship

When disasters happen, the speed with which first responders and emergency personnel can contact and be contacted by the people affected by the disaster during the first minutes or hours is critical. Early communications can make the difference between life and death. During a disaster communications infrastructure of the affected area is likely to be compromised. This project proposes an inexpensive, rapidly deployable cloud of autonomous drones, each coupled with a micro-cellular base station that deploys from a transportable deployment module. The goal is to temporarily restore communications for both first responders to communicate amongst themselves as well as for …

A New Method To Solve Same-Different Problems With Few-Shot Learning, Yuanyuan Han

Electronic Thesis and Dissertation Repository

Visual learning of highly abstract concepts is often simple for humans but very challenging for machines. Same-different (SD) problems are a visual reasoning task with highly abstract concepts. Previous work has shown that SD problems are difficult to solve with standard deep learning algorithms, especially in the few-shot case, despite the ability of such algorithms to learn abstract features. In this thesis, we propose a new method to solve SD problems with few training samples, in which same-different visual concepts can be recognized by examining similarities between Regions of Interest by using a same-different twins network. Our method achieves state-of-the-art …

Sensor Emulation With Physiolocal Data In Immersive Virtual Reality Driving Simulator, Jungsu Pak, Oliver Mathias, Ariane Guirguis, Uri Maoz

Student Scholar Symposium Abstracts and Posters

Can we enhance the safety and comfort of AVs by training AVs with physiological data of human drivers? We will train and compare AV algorithm with/without physiological data.

The Trolley Problem In Virtual Reality, Jungsu Pak, Ariane Guirguis, Nicholas Mirchandani, Scott Cummings, Uri Maoz

Student Scholar Symposium Abstracts and Posters

Would people react to the Trolley problem differently based on the medium? Immersive Virtual Reality Driving Simulator was used to examine participants respond to the trolley problem in a realistic and controlled simulated environment.

A Longitudinal Study Of Mammograms Utilizing The Automated Wavelet Transform Modulus Maxima Method, Brian C. Toner

Electronic Theses and Dissertations

Breast cancer is a disease which predominatly affects women. About 1 in 8 women are diagnosed with breast cancer during their lifetime. Early detection is key to increasing the survival rate of breast cancer patients since the longer the tumor goes undetected, the more deadly it can become. The modern approach for diagnosing breast cancer relies on a combination of self-breast exams and mammography to detect the formation of tumors. However, this approach only accounts for tumors which are either detectable by touch or are large enough to be observed during a screening mammogram. For some individuals, by the time …

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 …

Improving Video Game Recommendations Using A Hybrid, Neural Network And Keyword Ranking Approach, Nicholas Crawford

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

Recommendations systems are software solutions for finding high-quality and relevant content for a given user type ranging from online shoppers, to music listeners, to video game players. Traditional recommendation systems use user review data to make recommendations, but we still want recommendations to perform well for new users with no review data. Currently, one of the problems that exists in recommendations is poor recommendation accuracy when only a small amount of data exists for a user, called the cold start problem. In this research we investigate solutions for the cold start problem in video game recommendations and we propose a …

Incorporating Word Order Explicitly In Glove Word Embedding, Brandon Cox

Computer Science and Computer Engineering Undergraduate Honors Theses

Word embedding is the process of representing words from a corpus of text as real number vectors. These vectors are often derived from frequency statistics from the source corpus. In the GloVe model as proposed by Pennington et al., these vectors are generated using a word-word cooccurrence matrix. However, the GloVe model fails to explicitly take into account the order in which words appear within the contexts of other words. In this paper, multiple methods of incorporating word order in GloVe word embeddings are proposed. The most successful method involves directly concatenating several word vector matrices for each position in …

The Generation Of Operational Policy For Cyber-Physical Systems In Smart Homes, Jared Wayne Hall

MSU Graduate Theses

The term “Cyber-Physical Systems” (CPS) refers to those systems which seamlessly integrate sensing, computation, control, and networking into physical objects and infrastructure [1]. In these systems, computers and networks of physical entities interact with each other to bring new capabilities to traditional physical systems. Since its introduction, the field of Cyber-Physical Systems (CPS) has evolved with new and interesting advancements concerning its capability, adaptability, scalability, and usability [1]. One such advancement is the unification of the Internet of Things (IoT), a concept that enables real-world everyday objects to connect to the internet and interact with each other, with CPS [1]. …

Image Classification Using Fuzzy Fca, Niruktha Roy Gotoor

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Formal concept analysis (FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. It has been used in various domains such as data mining, machine learning, semantic web, Sciences, for the purpose of data analysis and Ontology over the last few decades. Various extensions of FCA are being researched to expand it's scope over more departments. In this thesis,we review the theory of Formal Concept Analysis (FCA) and its extension Fuzzy FCA. Many studies to use FCA in data mining and text learning have been pursued. We extend these studies to include …

Improving Medication Information Presentation Through Interactive Visualization In Mobile Apps: Human Factors Design, Don Roosan, Yan Li, Anandi Law, Huy Truong, Mazharul Karim, Jay Chok, Moom Roosan

Pharmacy Faculty Articles and Research

Background: Despite the detailed patient package inserts (PPIs) with prescription drugs that communicate crucial information about safety, there is a critical gap between patient understanding and the knowledge presented. As a result, patients may suffer from adverse events. We propose using human factors design methodologies such as hierarchical task analysis (HTA) and interactive visualization to bridge this gap. We hypothesize that an innovative mobile app employing human factors design with an interactive visualization can deliver PPI information aligned with patients’ information processing heuristics. Such an app may help patients gain an improved overall knowledge of medications.

Objective: The …

Tools For Tutoring Theoretical Computer Science Topics, Mark Mccartin-Lim

Doctoral Dissertations

This thesis introduces COMPLEXITY TUTOR, a tutoring system to assist in learning abstract proof-based topics, which has been specifically targeted towards the population of computer science students studying theoretical computer science. Existing literature has shown tremendous educational benefits produced by active learning techniques, student-centered pedagogy, gamification and intelligent tutoring systems. However, previously, there had been almost no research on adapting these ideas to the domain of theoretical computer science. As a population, computer science students receive immediate feedback from compilers and debuggers, but receive no similar level of guidance for theoretical coursework. One hypothesis of this thesis is that immediate …

Managing Overheads In Asynchronous Many-Task Runtime Systems, Bibek Wagle

LSU Doctoral Dissertations

Asynchronous Many-Task (AMT) runtime systems are based on the idea of dividing an algorithm into small units of work, known as tasks. The runtime system is then responsible for scheduling and executing these tasks in an efficient manner by taking into account the resources provided to it and the associated data dependencies between the tasks. One of the primary challenges faced by AMTs is managing such fine-grained parallelism and the overheads associated with creating, scheduling and executing tasks. This work develops methodologies for assessing and managing overheads associated with fine-grained task execution in HPX, our exemplar Asynchronous Many-Task runtime system. …

Establishing Computational Approaches Towards Identifying Malarial Allosteric Modulators: A Case Study Of Plasmodium Falciparum Hsp70s, Arnold Amusengeri, Lindy Astl, Kevin Lobb, Gennady M. Verkhivker, Özlem Tastan Bishop

Mathematics, Physics, and Computer Science Faculty Articles and Research

Combating malaria is almost a never-ending battle, as Plasmodium parasites develop resistance to the drugs used against them, as observed recently in artemisinin-based combination therapies. The main concern now is if the resistant parasite strains spread from Southeast Asia to Africa, the continent hosting most malaria cases. To prevent catastrophic results, we need to find non-conventional approaches. Allosteric drug targeting sites and modulators might be a new hope for malarial treatments. Heat shock proteins (HSPs) are potential malarial drug targets and have complex allosteric control mechanisms. Yet, studies on designing allosteric modulators against them are limited. Here, we identified allosteric …

A New Algorithm For Primer Design, Debanjan Guha Roy

Electronic Thesis and Dissertation Repository

The Polymerase Chain Reaction (PCR) technology is widely used to create DNA copies. It has impacted many diverse fields including genetics, forensics, molecular paleontology, medical applications and environmental microbiology.

The main object in PCR is a primer, a short single strand of DNA, about 18-25 bases long, that serves as the starting point of DNA synthesis. Primers are essential for DNA replication because the enzymes that catalyze this process, DNA polymerases, can only add new nucleotides to an existing strand of DNA. The PCR starts at the 3’-end of the primer and copies the opposite strand.Designing good primers is essential …

Read And Publish: What Can Libraries Expect?, Josh Horowitz

Charleston Library Conference

The author provides a publisher's perspective on the challenges and opportunities faced by a mid-sized society in navigating the current transition to open access licensing models.

Software-Defined Infrastructure For Iot-Based Energy Systems, Stephen Lee

Doctoral Dissertations

Internet of Things (IoT) devices are becoming an essential part of our everyday lives. These physical devices are connected to the internet and can measure or control the environment around us. Further, IoT devices are increasingly being used to monitor buildings, farms, health, and transportation. As these connected devices become more pervasive, these devices will generate vast amounts of data that can be used to gain insights and build intelligence into the system. At the same time, large-scale deployment of these devices will raise new challenges in efficiently managing and controlling them. In this thesis, I argue that the IoT …

Function And Dissipation In Finite State Automata - From Computing To Intelligence And Back, Natesh Ganesh

Doctoral Dissertations

Society has benefited from the technological revolution and the tremendous growth in computing powered by Moore's law. However, we are fast approaching the ultimate physical limits in terms of both device sizes and the associated energy dissipation. It is important to characterize these limits in a physically grounded and implementation-agnostic manner, in order to capture the fundamental energy dissipation costs associated with performing computing operations with classical information in nano-scale quantum systems. It is also necessary to identify and understand the effect of quantum in-distinguishability, noise, and device variability on these dissipation limits. Identifying these parameters is crucial to designing …

Vrsensory: Designing Inclusive Virtual Games With Neurodiverse Children, Ben Wasserman, Derek Prate, Bryce Purnell, Alex Muse, Kaitlyn Abdo, Kendra Day, Louanne Boyd

Engineering Faculty Articles and Research

We explore virtual environments and accompanying interaction styles to enable inclusive play. In designing games for three neurodiverse children, we explore how designing for sensory diversity can be understood through a formal game design framework. Our process reveals that by using sensory processing needs as requirements we can make sensory and social accessible play spaces. We contribute empirical findings for accommodating sensory differences for neurodiverse children in a way that supports inclusive play. Specifically, we detail the sensory driven design choices that not only support the enjoyability of the leisure activities, but that also support the social inclusion of sensory-diverse …

Nerf This: Copyright Highly Creative Video Game Streams As Sports Broadcasts, Madeleine A. Ball

William & Mary Law Review

Since the 1980s, video games have grown exponentially as an entertainment medium. Once relegated to the niche subcultures of nerds, video games are now decidedly mainstream, drawing over 200 million American consumers yearly. As a result, the industry has stepped up its game. No longer simply a diversion to be enjoyed individually, Americans are increasingly watching others play video games like they might watch television. This practice, where enthusiastic gamers broadcast their video game session online to crowds of viewers, is called “live streaming.”

While streaming has become lucrative and popular, American copyright law currently nerfs this nascent industry. Streams …

The Internet Of Bodies, Andrea M. Matwyshyn

William & Mary Law Review

This Article introduces the ongoing progression of the Internet of Things (IoT) into the Internet of Bodies (IoB)—a network of human bodies whose integrity and functionality rely at least in part on the Internet and related technologies, such as artificial intelligence. IoB devices will evidence the same categories of legacy security flaws that have plagued IoT devices. However, unlike most IoT, IoB technologies will directly, physically harm human bodies—a set of harms courts, legislators, and regulators will deem worthy of legal redress. As such, IoB will herald the arrival of (some forms of) corporate software liability and a new legal …

On The Yellow Brick Road, A Path To Enterprise Architecture Maturity, Avsharn Bachoo

The African Journal of Information Systems

This study concentrated on the relationship between the Enterprise Architecture (EA) maturity of an organization and the business value associated with it in the South African financial services environment. It was developed within the critical realism philosophy, which states that mechanisms generate events by accentuating the underlying EA mechanisms that lead to business value, as well as provide insights into the opportunities and challenges organizations experienced as they progressed to higher levels of maturity. Constructed using the resource-based view of the firm as the underlying theoretical framework, this research examined EA as an intangible resource and maturity as a source …

Reachnn: Reachability Analysis Of Neural-Network Controlled Systems, Chao Huang, Jiameng Fan, Wenchao Li, Xin Chen, Qi Zhu

Computer Science Faculty Publications

Applying neural networks as controllers in dynamical systems has shown great promises. However, it is critical yet challenging to verify the safety of such control systems with neural-network controllers in the loop. Previous methods for verifying neural network controlled systems are limited to a few specific activation functions. In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i.e., as long as they ensure that the neural networks are Lipschitz continuous. Specifically, we consider abstracting feedforward neural networks with Bernstein polynomials for …

Analysis Of Flickr, Snapchat, And Twitter Use For The Modeling Of Visitor Activity In Florida State Parks, Hartwig H. Hochmair, Levente Juhasz

Levente Juhasz

Spatio-temporal information attached to social media posts allows analysts to study human activity and travel behavior. This study analyzes contribution patterns to the Flickr, Snapchat, and Twitter platforms in over 100 state parks in Central and Northern Florida. The first part of the study correlates monthly visitor count data with the number of Flickr images, snaps, or tweets, contributed within the park areas. It provides insight into the suitability of these different social media platforms to be used as a proxy for the prediction of visitor numbers in state parks. The second part of the study analyzes the spatial distribution …

Image Labeler: Label Earth Science Images For Machine Learning, Ashish Acharya, Iksha Gurung, Brian Freitag, Manil Maskey, Rahul Ramachandran

Von Braun Symposium Student Posters

No abstract provided.

Identifying Irrigated Agriculture Land Using Remote Sensing And Machine Learning, Ryann Firestine, Cameron Handyside, Tamseel Syed, Leiqiu Hu

Von Braun Symposium Student Posters

No abstract provided.