Physical Sciences and Mathematics Commons^™

Plant Disease Detection Through Convolutional Neural Networks: A Survey Of Existing Literature, Best Practices, And Implementation, Kevin Label

Master's Theses

In the United States alone, common diseases spread among plants account for billions of dollars lost in crop yield each year. This issue is exacerbated in countries with less infrastructure to defend against crop epidemics, and can lead to famine and forced migration. Farmers can seek the help of plant pathology experts to defend against diseases and detect crop irregularities early on. However, access to experts can be difficult, and even those trained in the field may miss symptoms before it is too late. To assist in early disease detection, a number of papers have been released on the potential …

Machine Learning Analysis Of Single Nucleotide Polymorphism (Snp) Data To Predict Bone Mineral Density In African American Women, Erick Githua Wakayu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Osteoporosis is a debilitating disease in which an individual’s bones weaken, making bones fragile and more susceptible to fracture. While commonly found amongst postmenopausal Caucasian and Asian women based on previous studies, those of African descent (African American/Black) have largely been ignored when it comes to osteoporotic studies, especially when it comes to Genome Wide Association Studies (GWAS). From GWA studies, we gain access to single nucleotide poly-morphisms (SNPs) that may contribute to certain illnesses, such as osteoporosis. With low Bone Mineral Density (BMD) being one of the primary markers of potential osteoporosis, it is prudent that proper research is …

Linear Algebra For Computer Science, M. Thulasidas

Research Collection School Of Computing and Information Systems

This textbook introduces the essential concepts and practice of Linear Algebra to the undergraduate student of computer science. The focus of this book is on the elegance and beauty of the numerical techniques and algorithms originating from Linear Algebra. As a practical handbook for computer and data scientists, LA4CS restricts itself mostly to real fields and tractable discourses, rather than deep and theoretical mathematics.

Non-Local Approximation Properties, Kira Pierce

Fall Showcase for Research and Creative Inquiry

This project concerns the approximation properties of a given set where X is a scattered sequence and Ï•(x) = 1/x* ln(1 + x^2 ). Similar approximation sets are commonly used in interpolation problems and are especially helpful due to their Fourier representation. For our work, we will work to prove the following theorem.

Excursions In Summation, Brock Erwin

Fall Showcase for Research and Creative Inquiry

Using polynomials from series representation of functions to approximate other functions on the closed interval from [-1,1].

Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang, Mark Stamp

Faculty Research, Scholarly, and Creative Activity

Low grade endometrial stromal sarcoma (LGESS) accounts for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and stain normalization algorithms. We then apply a variety of classic machine learning and advanced deep learning models to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an …

Another Brick In The Wall: An Exploratory Analysis Of Digital Forensics Programs In The United States, Syria Mccullough, Stella Abudu, Ebere Onwubuariri, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

We present a comprehensive review of digital forensics programs offered by universities across the United States (U.S.). While numerous studies on digital forensics standards and curriculum exist, few, if any, have examined digital forensics courses offered across the nation. Since digital forensics courses vary from university to university, online course catalogs for academic institutions were evaluated to curate a dataset. Universities were selected based on online searches, similar to those that would be made by prospective students. Ninety-seven (n = 97) degree programs in the U.S. were evaluated. Overall, results showed that advanced technical courses are missing from curricula. We …

A Fast Method For Computing Volume Potentials In The Galerkin Boundary Element Method In 3d Geometries, Sasan Mohyaddin

Mathematics Theses and Dissertations

We discuss how the Fast Multipole Method (FMM) applied to a boundary concentrated mesh can be used to evaluate volume potentials that arise in the boundary element method. If $h$ is the meshwidth near the boundary, then the algorithm can compute the potential in nearly $\Ord(h^{-2})$ operations while maintaining an $\Ord(h^p)$ convergence of the error. The effectiveness of the algorithms are demonstrated by solving boundary integral equations of the Poisson equation.

Linear Algebra For Computer Science, M. Thulasidas

Research Collection School Of Computing and Information Systems

This book has its origin in my experience teaching Linear Algebra to Computer Science students at Singapore Management University. Traditionally, Linear Algebra is taught as a pure mathematics course, almost as an afterthought, not fully integrated with any other applied curriculum. It certainly was taught that way to me. The course I was teaching, however, had a definite pedagogical objective of bringing out the applicability and the usefulness of Linear Algebra in Computer Science, which is nothing but applied mathematics. In today’s age of machine learning and artificial intelligence, Linear Algebra is the branch of mathematics that holds the most …

Dan Farkas, Dan Farkas

Oral History

Dan Farkas has taught on the Pleasantville campus of Pace University since 1977.

Mobile Application To Travel The World Using Virtual Reality And Machine Learning, Valentina Quiroga, Francisco Olivares, José Najera

ICT

This research intends to make travel and culture an accessible possibility for all. With a phone and a VRHeadset, people will have the opportunity to see some of the most amazing scenes in the world and learn about the history and culture of famous landmarks without leaving the comfort of their own homes.

The Shape Of A Photon, Christopher C. O’Neill

ICT

The purpose of this research is to use quantum operators, known as ‘Dimensional Gate Operator’ (DGO) as a means of investigating the properties of quantum wave functions; in this case the shape of the wave function of light.

A Comparison Of Prospective Space-Time Scan Statistics And Spatiotemporal Event Sequence Based Clustering For Covid-19 Surveillance, Fuyu Xu, Kate Beard

Teaching, Learning & Research Documents

The outbreak of the COVID-19 disease was first reported in Wuhan, China, in December 2019. Cases in the United States began appearing in late January. On March 11, the World Health Organization (WHO) declared a pandemic. By mid-March COVID-19 cases were spreading across the US with several hotspots appearing by April. Health officials point to the importance of surveillance of COVID-19 to better inform decision makers at various levels and efficiently manage distribution of human and technical resources to areas of need. The prospective space-time scan statistic has been used to help identify emerging COVID-19 disease clusters, but results from …

The “Knapsack Problem” Workbook: An Exploration Of Topics In Computer Science, Steven Cosares

Open Educational Resources

This workbook provides discussions, programming assignments, projects, and class exercises revolving around the “Knapsack Problem” (KP), which is widely a recognized model that is taught within a typical Computer Science curriculum. Throughout these discussions, we use KP to introduce or review topics found in courses covering topics in Discrete Mathematics, Mathematical Programming, Data Structures, Algorithms, Computational Complexity, etc. Because of the broad range of subjects discussed, this workbook and the accompanying spreadsheet files might be used as part of some CS capstone experience. Otherwise, we recommend that individual sections be used, as needed, for exercises relevant to a course in …

Fine-Grained Detection Of Hate Speech Using Bertoxic, Yakoob Khan

Dartmouth College Undergraduate Theses

This thesis describes our approach towards the fine-grained detection of hate speech using deep learning. We leverage the transformer encoder architecture to propose BERToxic, a system that fine-tunes a pre-trained BERT model to locate toxic text spans in a given text and utilizes additional post-processing steps to refine the prediction boundaries. The post-processing steps involve (1) labeling character offsets between consecutive toxic tokens as toxic and (2) assigning a toxic label to words that have at least one token labeled as toxic. Through experiments, we show that these two post-processing steps improve the performance of our model by 4.16% on …

A Survey Of Computer Graphics Facial Animation Methods: Comparing Traditional Approaches To Machine Learning Methods, Joseph A. Johnson

Master's Theses

Human communications rely on facial expression to denote mood, sentiment, and intent. Realistic facial animation of computer graphic models of human faces can be difficult to achieve as a result of the many details that must be approximated in generating believable facial expressions. Many theoretical approaches have been researched and implemented to create more and more accurate animations that can effectively portray human emotions. Even though many of these approaches are able to generate realistic looking expressions, they typically require a lot of artistic intervention to achieve a believable result. To reduce the intervention needed to create realistic facial animation, …

Machine Learning In Stock Price Prediction Using Long Short-Term Memory Networks And Gradient Boosted Decision Trees, Carl Samuel Cederborg

Honors Projects

Quantitative analysis has been a staple of the financial world and investing for many years. Recently, machine learning has been applied to this field with varying levels of success. In this paper, two different methods of machine learning (ML) are applied to predicting stock prices. The first utilizes deep learning and Long Short-Term Memory networks (LSTMs), and the second uses ensemble learning in the form of gradient tree boosting. Using closing price as the training data and Root Mean Squared Error (RMSE) as the error metric, experimental results suggest the gradient boosting approach is more viable.

Honors Symposium: ML is …

Game-Based Learning In Science: Can Video Games Simplify Organic Chemistry?, Rachel Israel

Senior Honors Theses

Organic chemistry has been taught in the same way for decades, and students still have difficulty understanding and comprehending the subject material. Perhaps it is time to change the methods by which this subject is taught. Video games have been successfully used in education to create learning environments that increase student motivation and engagement as well as challenge students and promote collaboration. It is difficult for students to maintain a growth mindset in organic chemistry within the classroom. However across different genres, video games create a unique environment where an individual is encouraged to try again when they fail. This …

Modernizing Legacy Business Practices And Maintaining Backwards Compatibility When Replacing Legacy Software, Thomas Hillebrandt

Honors Theses

As technology advances and hardware as well as user expectations becomes more advanced, software systems must change alongside or go obsolete. When software is no longer developed, decisions must be made regarding its future. Through various methods, legacy software may continue to see usage far past its obsolescence, however legacy software will sooner or later face replacement by new applications, built for state-of-the-art machines, to comply with modern requirements. When writing new software to replace older programs, the added challenge for developers is to help the client also modernize their workflow. When a program has been in long time use …

Mpi4py Implementation Of Greedy Algorithm For The Shortest Path Problem, Arianna Martin, Jeremy Evert, Charles Sleeper

Student Research

No abstract provided.

Proximity-Based Video Communication With Cocktailparty, Addison Fabry, William Heffernan, Andrew Shroyer

Senior Theses

CocktailParty is a video communication application designed to uniquely and efficiently solve the problems faced by traditional online video communication. The proposal for this start-up project was accepted in August 2020. CocktailParty enables users to join a video chat room overlayed on top of a virtual house, in which each user can move their own video feed around the house. Each room in each house layout hosts a different video call, allowing users to easily host large virtual gatherings that facilitate multiple conversations taking place simultaneously. This functionality makes important steps towards effective simulation of the real-life gathering experience.

Mass Incarceration In Nebraska: Data And Historical Analysis Of Inmates From 1980-2020, Anna Krause

Honors Theses

This study examines Nebraska Department of Corrections inmate data from 1980-2020, looking specifically at inmate demographics and offense trends. State-of-the-art data analysis is conducted to collect, modify, and visualize the data sources. Inmates are organized by each decade they were incarcerated within. The current active prison population is also examined in their own research group. The demographic and offense trends are compared with previous local and national research. Historical context is given for evolving trends in offenses. Solutions for Nebraska prison overcrowding are presented from various interest groups. This study aims to enlighten all interested Nebraskans on who inhabits their …

Computational Thinking In Mathematics And Computer Science: What Programming Does To Your Head, Al Cuoco, E. Paul Goldenberg

Journal of Humanistic Mathematics

How you think about a phenomenon certainly influences how you create a program to model it. The main point of this essay is that the influence goes both ways: creating programs influences how you think. The programs we are talking about are not just the ones we write for a computer. Programs can be implemented on a computer or with physical devices or in your mind. The implementation can bring your ideas to life. Often, though, the implementation and the ideas develop in tandem, each acting as a mirror on the other. We describe an example of how programming and …

The 12th Annual Graduate Research Symposium 2021 Poster Tu Dublin: How To Recruit And Retain Women In Computer Science, Alina Berry, Susan Mckeever, Brenda Murphy, Sarah Jane Delany

Other resources

While in recent decades a number of efforts have been coordinated to address the issue of gender imbalance in STEM (science, technology, engineering and mathematics) disciplines, the problem still persists. Many authors speak of the ‘leaky’ pipeline metaphor that describes the loss of women in STEM areas before reaching senior roles. Research shows that women who leave are unlikely to return. The issue is particularly severe in the area of computer science, where women represent less than 20% of the labour force across the EU.

This poster introduces a summary of findings from the literature on how to effectively recruit …

Sample Mislabeling Detection And Correction In Bioinformatics Experimental Data, Soon Jye Kho

Browse all Theses and Dissertations

Sample mislabeling or incorrect annotation has been a long-standing problem in biomedical research and contributes to irreproducible results and invalid conclusions. These problems are especially prevalent in multi-omics studies in which a large set of biological samples are characterized by multiple types of omics platforms at different times or different labs. While multi-omics studies have demonstrated tremendous value in understanding disease biology and improving patient outcomes, the complexity of these studies may increase opportunities for human error. Fortunately, the interrelated nature of the data collected in multi-omics studies can be exploited to facilitate the identification and, in some cases, correction …

Edge Processing Of Image For Uas Sense And Avoidance, Christopher J. Rave

Browse all Theses and Dissertations

Today there is a large market for Unmanned Aerial Systems. Although most current systems are remotely piloted by operators on the ground, increasingly, many of these systems will use some sort of automatic flight controller to help mitigate new challenges, due to their deployment at growing scale. These challenges include, but are not limited to, shortage of FAA-certified UAS pilots, transmission bandwidth and delay constraints and cyber security threats associated with wireless networking, profitability of operations constrained by energy capacity and efficiency and air dynamics planning, and etc. In order to address these rising challenges, this thesis is a part …

Recommending Collaborations Using Link Prediction, Nikhil Chennupati

Browse all Theses and Dissertations

Link prediction in the domain of scientific collaborative networks refers to exploring and determining whether a connection between two entities in an academic network may emerge in the future. This study aims to analyze the relevance of academic collaborations and identify the factors that drive co-author relationships in a heterogeneous bibliographic network. Using topological, semantic, and graph representation learning techniques, we measure the authors' similarities w.r.t their structural and publication data to identify the reasons that promote co-authorships. Experimental results show that the proposed approach successfully infer the co-author links by identifying authors with similar research interests. Such a system …

A Rebellion Framework With Learning For Goal-Driven Autonomy, Zahiduddin Mohammad

Browse all Theses and Dissertations

Modeling an autonomous agent that decides for itself what actions to take to achieve its goals is a central objective of artificial intelligence. There are various approaches used to build autonomous agents including neural networks, state machines, utility functions, learning agents, and cognitive architectures. In this thesis, we focus on cognitive architectures. Our approach uses specific knowledge of the world, the goals they pursue, and the actions being performed. Most agents do what they are told (i.e., achieve the goals given to them by a human), but a genuinely autonomous agent does more. It can formulate its own goal or …

Computational Simulation And Analysis Of Neuroplasticity, Madison E. Yancey

Browse all Theses and Dissertations

Homeostatic synaptic plasticity is the process by which neurons alter their activity in response to changes in network activity. Neuroscientists attempting to understand homeostatic synaptic plasticity have developed three different mathematical methods to analyze collections of event recordings from neurons acting as a proxy for neuronal activity. These collections of events are from control data and treatment data, referring to the treatment of neuron cultures with pharmacological agents that augment or inhibit network activity. If the distribution of control events can be functionally mapped to the distribution of treatment events, a better understanding of the biological processes underlying homeostatic synaptic …

Deep Learning For Compressive Sar Imaging With Train-Test Discrepancy, Morgan R. Mccamey

Browse all Theses and Dissertations

We consider the problem of compressive synthetic aperture radar (SAR) imaging with the goal of reconstructing SAR imagery in the presence of under sampled phase history. While this problem is typically considered in compressive sensing (CS) literature, we consider a variety of deep learning approaches where a deep neural network (DNN) is trained to form SAR imagery from limited data. At the cost of computationally intensive offline training, on-line test-time DNN-SAR has demonstrated orders of magnitude faster reconstruction than standard CS algorithms. A limitation of the DNN approach is that any change to the operating conditions necessitates a costly retraining …