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 …

Characterizing Dryland Ecosystems Using Remote Sensing And Dynamic Global Vegetation Modeling, Abdolhamid Dashtiahangar

Boise State University Theses and Dissertations

Drylands include all terrestrial regions where the production of crops, forage, wood and other ecosystem services are limited by water. These ecosystems cover approximately 40% of the earth terrestrial surface and accommodate more than 2 billion people (Millennium Ecosystem Assessment, 2005). Moreover, the interannual variability of the global carbon budget is strongly regulated by vegetation dynamics in drylands. Understanding the dynamics of such ecosystems is significant for assessing the potential for and impacts of natural or anthropogenic disturbances and mitigation planning, and a necessary step toward enhancing the economic and social well-being of dryland communities in a sustainable manner (Global …

Fractional Random Weighted Bootstrapping For Classification On Imbalanced Data With Ensemble Decision Tree Methods, Sean Charles Carter

USF Tampa Graduate Theses and Dissertations

Ensemble methods are commonly used for building predictive models for classification. Models that are unstable to perturbations in the training set, such as the decision tree, often see considerable reductions in error when grouped, using bootstrapped resamples of the training data to train many models. The non-parametric bootstrap, however, has limited efficacy when used on severely imbalanced data, especially when the number of observations of one or more classes is exceptionally small. We explore the fractional random weighted bootstrap, which randomly assigns fractional weights to observations, as an alternative resampling pro cedure in training machine learning ensembles, particularly decision tree …

Multimodal Emotion Recognition Using 3d Facial Landmarks, Action Units, And Physiological Data, Diego Fabiano

USF Tampa Graduate Theses and Dissertations

To fully understand the complexities of human emotion, the integration of multiple physical features from different modalities can be advantageous. Considering this, this thesis presents an approach to emotion recognition using handcrafted features that consist of 3D facial data, action units, and physiological data. Each modality independently, as well as the combination of each for recognizing human emotion were analyzed.

This analysis includes the use of principal component analysis to determine which dimensions of the feature vector are most important for emotion recognition. The proposed features are shown to be able to be used to accurately recognize emotion and that …

Adaptive Feature Engineering Modeling For Ultrasound Image Classification For Decision Support, Hatwib Mugasa

Doctoral Dissertations

Ultrasonography is considered a relatively safe option for the diagnosis of benign and malignant cancer lesions due to the low-energy sound waves used. However, the visual interpretation of the ultrasound images is time-consuming and usually has high false alerts due to speckle noise. Improved methods of collection image-based data have been proposed to reduce noise in the images; however, this has proved not to solve the problem due to the complex nature of images and the exponential growth of biomedical datasets. Secondly, the target class in real-world biomedical datasets, that is the focus of interest of a biopsy, is usually …

The Application Of Synthetic Signals For Ecg Beat Classification, Elliot Morgan Brown

Theses and Dissertations

A brief overview of electrocardiogram (ECG) properties and the characteristics of various cardiac conditions is given. Two different models are used to generate synthetic ECG signals. Domain knowledge is used to create synthetic examples of 16 different heart beat types with these models. Other techniques for synthesizing ECG signals are explored. Various machine learning models with different combinations of real and synthetic data are used to classify individual heart beats. The performance of the different methods and models are compared, and synthetic data is shown to be useful in beat classification.

Classification With Measurement Error In Covariates Or Response, With Application To Prostate Cancer Imaging Study, Kexin Luo

Electronic Thesis and Dissertation Repository

The research is motivated by the prostate cancer imaging study conducted at the University of Western Ontario to classify cancer status using multiple in-vivo images. The prostate cancer histological image and the in-vivo images are subject to misalignment in the co-registration procedure, which can be viewed as measurement error in covariates or response. We investigate methods to correct this problem.

The first proposed method corrects the predicted class probability when the data has misclassified labels. The correction equation is derived from the relationship between the true response and the error-prone response. The probability for the observed class label is adjusted …

Classification Of Isometry Algebras Of Solutions Of Einstein's Field Equations, Eugene Hwang

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Since Schwarzschild found the first solution of the Einstein’s equations, more than 800 solutions were found. Solutions of Einstein’s equations are classified according to their Lie algebras of isometries and their isotropy subalgebras. Solutions were taken from the USU electronic library of solutions of Einstein’s field equations and the classification used Maple code developed at USU. This classification adds to the data contained in the library of solutions and provides additional tools for addressing the equivalence problem for solutions to the Einstein field equations. In this thesis, homogeneous spacetimes, hypersurface-homogeneous spacetimes, Robinson-Trautman solutions, and some famous black hole solutions have …

A Machine Learning Approach To Predicting Community Engagement On Social Media During Disasters, Adel Alshehri

USF Tampa Graduate Theses and Dissertations

The use of social media is expanding significantly and can serve a variety of purposes. Over the last few years, users of social media have played an increasing role in the dissemination of emergency and disaster information. It is becoming more common for affected populations and other stakeholders to turn to Twitter to gather information about a crisis when decisions need to be made, and action is taken. However, social media platforms, especially on Twitter, presents some drawbacks when it comes to gathering information during disasters. These drawbacks include information overload, messages are written in an informal format, the presence …

An Adaptive Weighted Average (Wav) Reprojection Algorithm For Image Denoising, Halimah Alsurayhi

Electronic Thesis and Dissertation Repository

Patch-based denoising algorithms have an effective improvement in the image denoising domain. The Non-Local Means (NLM) algorithm is the most popular patch-based spatial domain denoising algorithm. Many variants of the NLM algorithm have proposed to improve its performance. Weighted Average (WAV) reprojection algorithm is one of the most effective improvements of the NLM denoising algorithm. Contrary to the NLM algorithm, all the pixels in the patch contribute into the averaging process in the WAV reprojection algorithm, which enhances the denoising performance. The key parameters in the WAV reprojection algorithm are kept fixed regardless of the image structure. In this thesis, …

Classifying Challenging Behaviors In Autism Spectrum Disorder With Neural Document Embeddings, Abigail Atchison

Computational and Data Sciences (MS) Theses

The understanding and treatment of challenging behaviors in individuals with Autism Spectrum Disorder is paramount to enabling the success of behavioral therapy; an essential step in this process being the labeling of challenging behaviors demonstrated in therapy sessions. These manifestations differ across individuals and within individuals over time and thus, the appropriate classification of a challenging behavior when considering purely qualitative factors can be unclear. In this thesis we seek to add quantitative depth to this otherwise qualitative task of challenging behavior classification. We do so through the application of natural language processing techniques to behavioral descriptions extracted from the …

Human Activity Recognition Based On Multimodal Body Sensing, Anish Hemant Narkhede

Master's Projects

In the recent years, human activity recognition has been widely popularized by a lot of smartphone manufacturers and fitness tracking companies. It has allowed us to gain a deeper insight into our physical health on a daily basis. However, with the evolution of fitness tracking devices and smartphones, the amount of data that is being captured by these devices is growing exponentially. This paper aims at understanding the process of dimensionality reduction such as PCA so that the data can be used to make meaningful predictions along with novel techniques using autoencoders with different activation functions. The paper also looks …

Toward On-Demand Profile Hidden Markov Models For Genetic Barcode Identification, Jessica Sheu

Master's Projects

Genetic identification aims to solve the shortcomings of morphological identification. By using the cytochrome c oxidase subunit 1 (COI) gene as the Eukaryotic “barcode,” scientists hope to research species that may be morphologically ambiguous, elusive, or similarly difficult to visually identify. Current COI databases allow users to search only for existing database records. However, as the number of sequenced, potential COI genes increases, COI identification tools should ideally also be informative of novel, previously unreported sequences that may represent new species. If an unknown COI sequence does not represent a reported organism, an ideal identification tool would report taxonomic ranks …

Species Classification Using Dna Barcoding And Profile Hidden Markov Models, Sphoorti Poojary

Master's Projects

Traditional classification systems for living organisms like the Linnaean taxonomy involved classification based on morphological features of species. This traditional system is being replaced by molecular approaches which involve using gene sequences. The COI gene, also known as the ”DNA barcode” since it is unique in every species, can be used to uniquely identify organisms and thus, classify them. Classifying using gene sequences has many advantages, including correct identification of cryptic species(individuals which appear similar but belong to different species) and species which are extremely small in size. In this project, I worked on classifying COI sequences of unknown species …

Teaching Computers To Teach Themselves: Synthesizing Training Data Based On Human-Perceived Elements, James Little

Honors Projects

Isolation-Based Scene Generation (IBSG) is a process for creating synthetic datasets made to train machine learning detectors and classifiers. In this project, we formalize the IBSG process and describe the scenarios—object detection and object classification given audio or image input—in which it can be useful. We then look at the Stanford Street View House Number (SVHN) dataset and build several different IBSG training datasets based on existing SVHN data. We try to improve the compositing algorithm used to build the IBSG dataset so that models trained with synthetic data perform as well as models trained with the original SVHN training …

Classification Of Vegetation In Aerial Imagery Via Neural Network, Gevand Balayan

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis focuses on the task of trying to find a Neural Network that is best suited for identifying vegetation from aerial imagery. The goal is to find a way to quickly classify items in an image as highly likely to be vegetation(trees, grass, bushes and shrubs) and then interpolate that data and use it to mark sections of an image as vegetation. This has practical applications as well. The main motivation of this work came from the effort that our town takes in conserving water. By creating an AI that can easily recognize plants, we can better monitor the …

Computer-Aided Classification Of Impulse Oscillometric Measures Of Respiratory Small Airways Function In Children, Nancy Selene Avila

Open Access Theses & Dissertations

Computer-aided classification of respiratory small airways dysfunction is not an easy task. There is a need to develop more robust classifiers, specifically for children as the classification studies performed to date have the following limitations: 1) they include features derived from tests that are not suitable for children and 2) they cannot distinguish between mild and severe small airway dysfunction.

This Dissertation describes the classification algorithms with high discriminative capacity to distinguish different levels of respiratory small airways function in children (Asthma, Small Airways Impairment, Possible Small Airways Impairment, and Normal lung function). This ability came from innovative feature selection, …

Utilizing Multi-Level Classification Techniques To Predict Adverse Drug Effects And Reactions, Victoria Puhl

Undergraduate Honors Thesis Collection

Multi-class classification models are used to predict categorical response variables with more than two possible outcomes. A collection of multi-class classification techniques such as Multinomial Logistic Regression, Na\"{i}ve Bayes, and Support Vector Machine is used in predicting patients’ drug reactions and adverse drug effects based on patients’ demographic and drug administration. The newly released 2018 data on drug reactions and adverse drug effects from U.S. Food and Drug Administration are tested with the models. The applicability of model evaluation measures such as sensitivity, specificity and prediction accuracy in multi-class settings, are also discussed.

Deep Neural Network Learning-Based Classifier Design For Big-Data Analytics, Krishnan Raghavan

Doctoral Dissertations

"In this digital age, big-data sets are commonly found in the field of healthcare, manufacturing and others where sustainable analysis is necessary to create useful information. Big-data sets are often characterized by high-dimensionality and massive sample size. High dimensionality refers to the presence of unwanted dimensions in the data where challenges such as noise, spurious correlation and incidental endogeneity are observed. Massive sample size, on the other hand, introduces the problem of heterogeneity because complex and unstructured data types must analyzed. To mitigate the impact of these challenges while considering the application of classification, a two step analysis approach is …