Digital Commons Network^™

Exploring The Use Of Machine Learning For Daily Fire Growth Prediction In Alaska, James White

Atmospheric Sciences

Wildfire is a natural but often hazardous part of the Alaskan ecosystems. Physically based wildfire models range from simple relationships used for rapid, in-situ fire behavior analysis to complex weather models used for prediction over several days and weeks. Physical models in Alaska, however, often struggle to integrate weather forecast information to make predictions beyond just a few days. The random forest model explored here is able to leverage an array of variables to identify days of enhanced and reduced satellite fire detections. Peaks and lulls in activity are accurately identified, though exact magnitudes are often incorrect, especially when wildfire …

Machine Learning Methods For Depression Detection Using Smri And Rs-Fmri Images, Marzieh Sadat Mousavian

LSU Doctoral Dissertations

Major Depression Disorder (MDD) is a common disease throughout the world that negatively influences people’s lives. Early diagnosis of MDD is beneficial, so detecting practical biomarkers would aid clinicians in the diagnosis of MDD. Having an automated method to find biomarkers for MDD is helpful even though it is difficult. The main aim of this research is to generate a method for detecting discriminative features for MDD diagnosis based on Magnetic Resonance Imaging (MRI) data.

In this research, representational similarity analysis provides a framework to compare distributed patterns and obtain the similarity/dissimilarity of brain regions. Regions are obtained by either …

Using Machine Learning Methods To Predict The Movement Trajectories Of The Louisiana Black Bear, Daniel Clark, David Shaw, Armando Vela, Shane Weinstock, John Santerre, Joseph D. Clark

SMU Data Science Review

In 1992, the Louisiana black bear (Ursus americanus luteolus) was placed on the U.S. Endangered Species List. This was due to bear populations in Louisiana being small and isolated enough where their populations couldn’t intersect with other populations to grow. Interchange of individuals between subpopulations of bears in Louisiana is critical to maintain genetic diversity and avoid inbreeding effects. Utilizing GPS (Global Positioning System) data gathered from 31 radio-collared bears from 2010 through 2012, this research will investigate how bears traverse the landscape, which has implications for gene exchange. This paper will leverage machine learning tools to improve upon existing …

Machine Learning In The Health Industry: Predicting Congestive Heart Failure And Impactors, Alexandra Norman, James Harding, Daria Zhukova

SMU Data Science Review

Cardiovascular diseases, Congestive Heart Failure in particular, are a leading cause of deaths worldwide. Congestive Heart Failure has high mortality and morbidity rates. The key to decreasing the morbidity and mortality rates associated with Congestive Heart Failure is determining a method to detect high-risk individuals prior to the development of this often-fatal disease. Providing high-risk individuals with advanced knowledge of risk factors that could potentially lead to Congestive Heart Failure, enhances the likelihood of preventing the disease through implementation of lifestyle changes for healthy living. When dealing with healthcare and patient data, there are restrictions that led to difficulties accessing …

Breast Cancer Detection From Histopathology Images Using Machine Learning Techniques: A Bibliometric Analysis, Shubhangi A. Joshi, Anupkumar M. Bongale Dr., Arunkumar M. Bongale Dr.

Library Philosophy and Practice (e-journal)

Computer aided diagnosis has become upcoming area of research over past few years. With the advent of machine learning and especially deep learning techniques, the scenario of work flow management in healthcare sector is changing drastically. Artificial intelligence has shown potential in the field of breast cancer care. With datasets for machine learning frameworks getting eventually richer with time, we can definitely get newer insights in the field of breast cancer care. This will help in narrowing down the treatment range for patients and increasing patient survivability. The purpose of this study was to perform bibliometric analysis of the literature …

Fine-Grained Sentiment Analysis For Customer Review, Bing Han, Meng Han, Jing (Selena) He

Master of Science in Computer Science Theses

Natural Language Processing (NLP) is one of the most attractive technologies in many applications in real-life. Sentiment analysis, which has devoted to know others' think or feel about an experience or an item and hence take an action, is one of the most developed area in both academia and industry. Among sentiment analysis, fine-grained aspect sentiment analysis attempts to analyze emotional attitude categorized into different aspects or features of an(a) experience/service/product. Although aspect level sentiment analysis could provide more useful information, the proposed models' performance were relative poor compared with document-level or sentence-level sentiment analysis due to the lack of …

Real-Time Material State Assessment Of Composites Using Artificial Intelligence And Its Challenges, Muthu Ram Prabhu Elenchezhian

Mechanical and Aerospace Engineering Dissertations - Archive

Over several decades of careful experimental investigation and exhaustive development of discrete damage analysis methods including integrated computational mechanics methods, our community knows a great deal about how discrete defects such as matrix cracks and defect growth (e.g. delamination) can be predicted in structural composites. For many practical situations controlled by laminated multiaxial composite structures, the loss of performance and “sudden death” end of life is controlled by defect coupling which becomes a precursor to fracture plane development. These interaction sequences are highly dependent on local details of manufacture, design configurations, and loading for a given application material and influenced …

Data-Driven Recommendation Of Academic Options Based On Personality Traits, Aashish Ghimire

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The choice of academic major and, subsequently, an academic institution has a massive effect on a person’s career. It not only determines their career path but their earning potential, professional happiness, etc. [1] About 40% of people who are admitted to a college do not graduate within six years. Yet, very limited resources are available for students to help make those decisions, and each guidance counselor is responsible for roughly 400 to 900 students across the United States. A tool to help these decisions would benefit students, parents, and guidance counselors.

Various research studies have shown that personality traits affect …

Human Factors Analysis And Monitoring To Enhance Human-Robot Collaboration, Akilesh Rajavenkatanarayanan

Computer Science and Engineering Dissertations - Archive

Human-Machine Interaction (HMI) can be defined as a way for us to communicate with machines through user interfaces. User interfaces have evolved from complicated punch cards and levers in the first analog computers to a more natural way of interaction using speech or gestures in today's digital assistants. Technological advancements in computing devices have paved the way for smart, powerful computers to be part of our everyday lives. There is also an increasing trend of using smart computing devices and robots in manufacturing lines, medical procedures, rehabilitation, and personal care. The umbrella of HMI typically covers several areas like Human-Robot …

Kopos: A Framework To Study And Detect Physical And Cognitive Fatigue Concurrently, Varun Ajay Kanal

Computer Science and Engineering Dissertations - Archive

Fatigue is one of the most prevalent phenomena in human beings, and yet its detection is highly subjective and poorly understood. The phenomenon of fatigue has a huge impact on performance, the ability to execute tasks safely and correctly, and the ability to retain or secure a job. Fatigue can be classified into two types: physical and cognitive fatigue. Physical fatigue may occur due to excessive physical exertion, while cognitive fatigue may occur due to excessive mental exertion. Historically, these two types of fatigue have been studied independently. However, in the real world, although these often occur at the same …

Using Machine Learning To Predict Super-Utilizers Of Healthcare Services, Kevin Paul Buchan Jr.

Legacy Theses & Dissertations (2009 - 2024)

In this dissertation, I aim to forecast high utilizers of emergency care and inpatient Medicare services (i.e., healthcare visits). Through a literature review, I demonstrate that accurate and reliable prediction of these future high utilizers will not only reduce healthcare costs but will also improve the overall quality of healthcare for patients. By identifying this population at risk before manifestation, I propose that there is still time to reverse undesirable healthcare trajectories (i.e., individuals whose clinical risk increases an excessive healthcare and treatment burden) through timely attention and proper care coordination. My dissertation culminates in the delivery of state-of-the-art predictive …

Machine Learning-Based Recognition On Crowdsourced Food Images, Aditya Kulkarni

Honors Scholar Theses

With nearly a third of the world’s population suffering from food-induced chronic diseases such as obesity, the role of food in community health is required now more than ever. While current research underscores food proximity and density, there is a dearth in regard to its nutrition and quality. However, recent research in geospatial data collection and analysis as well as intelligent deep learning will help us study this further.

Employing the efficiency and interconnection of computer vision and geospatial technology, we want to study whether healthy food in the community is attainable. Specifically, with the help of deep learning in …

Finding The Needle In A Haystack: On The Automatic Identification Of Accessibility User Reviews, Eman Abdullah Alomar, Wajdi Aljedaani, Murtaza Tamjeed, Mohamed Wiem Mkaouer, Yasime Elglaly

Articles

In recent years, mobile accessibility has become an important trend with the goal of allowing all users the possibility of using any app without many limitations. User reviews include insights that are useful for app evolution. However, with the increase in the amount of received reviews, manually analyzing them is tedious and time-consuming, especially when searching for accessibility reviews. The goal of this paper is to support the automated identification of accessibility in user reviews, to help technology professionals in prioritizing their handling, and thus, creating more inclusive apps. Particularly, we design a model that takes as input accessibility user …

Data-Driven Decision Making And Control Of Rational Agents, Patrik Kolaric

Electrical Engineering Dissertations - Archive

This dissertation studies the problem of data-driven optimal decision making. The 4main contributions of this work are listed here. First, we develop a model-based and data-driven techniques for learning the cost of an Ex-pert agent. This ties fields of Inverse Optimal Control and Inverse Reinforcement Learning and represents a first data-driven algorithm of this kind in the control community. Next, we have developed optimally adaptive dynamic control allocation mechanism that optimally re-configures redundant actuators in a model-free fashion, that is, based on collected data. This work pushed the multiple frontiers of control allocation research, since state-of-the-art control allocation was Next, …

Deep Convolutional Spiking Neural Networks For Image Classification, Ruthvik Vaila

Boise State University Theses and Dissertations

Spiking neural networks are biologically plausible counterparts of artificial neural networks. Artificial neural networks are usually trained with stochastic gradient descent (SGD) and spiking neural networks are trained with bioinspired spike timing dependent plasticity (STDP). Spiking networks could potentially help in reducing power usage owing to their binary activations. In this work, we use unsupervised STDP in the feature extraction layers of a neural network with instantaneous neurons to extract meaningful features. The extracted binary feature vectors are then classified using classification layers containing neurons with binary activations. Gradient descent (backpropagation) is used only on the output layer to perform …

Imaging Based Prediction Of Pathology In Adult Diffuse Glioma With Applications To Therapy And Prognosis, Evan Gates

Dissertations and Theses (Open Access)

The overall aggressiveness of a glioma is measured by histologic and molecular analysis of tissue samples. However, the well-known spatial heterogeneity in gliomas limits the ability for clinicians to use that information to make spatially specific treatment decisions. Magnetic resonance imaging (MRI) visualizes and assesses the tumor. But, the exact degree to which MRI correlates with the actual underlying tissue characteristics is not known.

In this work, we derive quantitative relationships between imaging and underlying pathology. These relations increase the value of MRI by allowing it to be a better surrogate for underlying pathology and they allow evaluation of the …

Electronic Structure And Dynamics Of Uranyl-Peroxide Species, Ethan T. Hare

Honors Thesis

Uranyl-peroxide nanocapsules are a unique family of self-assembled actinide species. Uranyl ions rapidly self-assemble in basic peroxidic media through a myriad of reactions to coalesce into a single nanocapsule that includes both peroxide and hydroxide bridging groups between the uranyl moieties. A wide variety of capsules can be formed, and it has been proposed that square and pentagonal building blocks assemble prior to nanocapsule formation. We have studied the speciation of the pentagonal 2) uranyl-peroxide nanocapsule building blocks using density functional theory calculations. We predicted the most favorable speciation pathways for the self-assembly of the building blocks prior to cluster …

Defect Detection In Atomic Resolution Transmission Electron Microscopy Images Using Machine Learning, Philip Cho, Aihua W. Wood, Krishnamurthy Mahalingam, Kurt Eyink

Faculty Publications

Point defects play a fundamental role in the discovery of new materials due to their strong influence on material properties and behavior. At present, imaging techniques based on transmission electron microscopy (TEM) are widely employed for characterizing point defects in materials. However, current methods for defect detection predominantly involve visual inspection of TEM images, which is laborious and poses difficulties in materials where defect related contrast is weak or ambiguous. Recent efforts to develop machine learning methods for the detection of point defects in TEM images have focused on supervised methods that require labeled training data that is generated via …

Gene Selection And Classification In High-Throughput Biological Data With Integrated Machine Learning Algorithms And Bioinformatics Approaches, Abhijeet R Patil

Open Access Theses & Dissertations

With the rise of high throughput technologies in biomedical research, large volumes of expression profiling, methylation profiling, and RNA-sequencing data are being generated. These high-dimensional data have large number of features with small number of samples, a characteristic called the "curse of dimensionality." The selection of optimal features, which largely affects the performance of classification algorithms in machine learning models, has led to challenging problems in bioinformatics analyses of such high-dimensional datasets. In this work, I focus on the design of two-stage frameworks of feature selection and classification and their applications in multiple sets of colorectal cancer data. The first …

Machine Learning Approaches For Lung Cancer Diagnosis., Ahmed Mahmoud Ahmed Shaffie

Electronic Theses and Dissertations

The enormity of changes and development in the field of medical imaging technology is hard to fathom, as it does not just represent the technique and process of constructing visual representations of the body from inside for medical analysis and to reveal the internal structure of different organs under the skin, but also it provides a noninvasive way for diagnosis of various disease and suggest an efficient ways to treat them. While data surrounding all of our lives are stored and collected to be ready for analysis by data scientists, medical images are considered a rich source that could provide …

An Introduction To Federated Learning And Its Analysis, Manjari Ganapathy

UNLV Theses, Dissertations, Professional Papers, and Capstones

With the onset of the digital era, data privacy is one of the most predominant issues. Decentralized learning is becoming popular as the data can remain within local entities by maintaining privacy. Federated Learning is a decentralized machine learning approach, where multiple clients collaboratively learn a model, without sharing raw data. There are many practical challenges in solving Federated Learning, which include communication set up, data heterogeneity and computational capacity of clients. In this thesis, I explore recent methods of Federated Learning with various settings, such as data distributions and data variability, used in several applications. In addition, I, specifically, …

Achieving Differential Privacy And Fairness In Machine Learning, Depeng Xu

Graduate Theses and Dissertations

Machine learning algorithms are used to make decisions in various applications, such as recruiting, lending and policing. These algorithms rely on large amounts of sensitive individual information to work properly. Hence, there are sociological concerns about machine learning algorithms on matters like privacy and fairness. Currently, many studies only focus on protecting individual privacy or ensuring fairness of algorithms separately without taking consideration of their connection. However, there are new challenges arising in privacy preserving and fairness-aware machine learning. On one hand, there is fairness within the private model, i.e., how to meet both privacy and fairness requirements simultaneously in …

Applicant Justice Perceptions Of Machine Learning Algorithms In Personnel Selection, Megan Warrenbrand

Masters Theses and Doctoral Dissertations

Machine-learning artificial intelligence algorithms provide organizations with the opportunity to quickly and efficiently process information about potential employees while reducing costs associated with selection and turnover. However, any bias or error present in the programming of such algorithms as a result of information drawn from historically biased data is evident in the resulting output (Illingworth, 2015). Recently, applicants have expressed growing fairness and equity concerns about the risks associated with the use of algorithms in selection processes. The present quasi-experiment analyzed applicant reactions to selection processes to understand whether machine learning algorithms or human hiring decision-makers influence perceptions of fairness …

Enhancing Health And Energy Efficiency Through Data-Driven Urban Initiatives: A Smart City Approach, Jin Cho

Masters Theses and Doctoral Dissertations

With the recent growing recognition, the "smart city" project aims to advance the quality of modern cities through technology and data science. In this dissertation, two fundamental smart city applications are explored: Smart Health and Smart Energy. The goal of the presented studies is to transform the future of healthcare and energy through data-driven solutions. For Smart Health, statistical analysis and machine learning algorithms are employed to improve patient management and their eventual outcomes. This is done by implementing a predictive analytics framework to identify various risk factors associated with respective medical conditions. The aim of the Smart Energy application …

Extensive Huffman-Tree-Based Neural Network For The Imbalanced Dataset And Its Application In Accent Recognition, Jeremy Merrill

Masters Theses and Doctoral Dissertations

To classify the data-set featured with a large number of heavily imbalanced classes, this thesis proposed an Extensive Huffman-Tree Neural Network (EHTNN), which fabricates multiple component neural network-enabled classifiers (e.g., CNN or SVM) using an extensive Huffman tree. Any given node in EHTNN can have arbitrary number of children. Compared with the Binary Huffman-Tree Neural Network (BHTNN), EHTNN may have smaller tree height, involve fewer component neural networks, and demonstrate more flexibility on handling data imbalance. Using a 16-class exponentially imbalanced audio data-set as the benchmark, the proposed EHTNN was strictly assessed based on the comparisons with alternative methods such …

Machine Learning For Wireless Signal Learning, Logan Smith

Theses and Dissertations

Wireless networks are vulnerable to adversarial devices by spoofing the digital identity of valid wireless devices, allowing unauthorized devices access to the network. Instead of validating devices based on their digital identity, it is possible to use their unique "physical fingerprint" caused by changes in the signal due to deviations in wireless hardware. In this thesis, the physical fingerprint was validated by performing classification with complex-valued neural networks (NN), achieving a high level of accuracy in the process. Additionally, zero-shot learning (ZSL) was implemented to learn discriminant features to separate legitimate from unauthorized devices using outlier detection and then further …

Multispectral In-Field Sensors Observations To Estimate Corn Leaf Nitrogen Concentration And Grain Yield Using Machine Learning, Razieh Barzin

Theses and Dissertations

Nitrogen (N) is the most critical fertilizer applied nutrient for supporting plant growth. It is a critical part of photosynthesis as a component of chlorophyl, hence it is a key indicator of plant health. In recent years, rapid development of multispectral sensing technology and machine learning (ML) methods make it possible to estimate leaf chemical components such as N for predicting yield spatially and temporally. The objectives of this study were to compare the relationships between canopy reflectance and corn (Zea mays L.) leaf N concentration acquired by two multispectral sensors: red-edge multispectral camera mounted on the Unmanned Aerial Vehicle …

Quantitative Analysis Of Research On Artificial Intelligence In Retinopathy Of Prematurity, Ranjana Agrawal, Manasi Anup Agrawal, Sucheta Kulkarni, Ketan Kotecha, Rahee Walambe

Library Philosophy and Practice (e-journal)

Retinopathy of Prematurity (ROP) is a disease of the eye and a potential source of blindness in low birth weight preterm infants. It is preventable if diagnosed and treated on time. Artificial Intelligence (AI) has played an important role in developing automated screening systems to assist medical experts. There are many traditional literature review articles available that focus on the scientific content of ROP-AI. The researchers also require a bibliometric analysis to become acquainted with the competing groups and new trends in this field. This paper gives a brief overview of ROP and AI systems for ROP screening with a …

Mlatticeabc: Generic Lattice Constant Prediction Of Crystal Materials Using Machine Learning, Yuxin Li, Wenhui Yang, Rongzhi Dong, Jianjun Hu

Faculty Publications

Lattice constants such as unit cell edge lengths and plane angles are important parameters of the periodic structures of crystal materials. Predicting crystal lattice constants has wide applications in crystal structure prediction and materials property prediction. Previous work has used machine learning models such as neural networks and support vector machines combined with composition features for lattice constant prediction and has achieved a maximum performance for cubic structures with an average coefficient of determination (R2) of 0.82. Other models tailored for special materials family of a fixed form such as ABX3 perovskites can achieve much higher performance due …

Beyond The Three Laws: An Argument For Regulating Data Scientists As Fiduciaries, William Goodrum, Jacqueline Goodrum

Richmond Journal of Law & Technology

In the short story Runaround, science fiction author Isaac Asimov first introduced the world to an ethical framework for artificial intelligence known as the Three Laws of Robotics. These laws state: (1) “a robot may not injure a human being or, through inaction, allow a human being to come to harm”; (2) “a robot must obey the orders given it by human beings except where such orders would conflict with the First Law”; and (3) “a robot must protect its own existence as long as such protection does not conflict with the First and Second Laws.” On the surface, the …