Physical Sciences and Mathematics Commons^™

Machine Learning Based Three-Limb Core-Type Transformer Core Aspect Ratios Identification, Ananta Bijoy Bhadra

Electronic Theses and Dissertations

Power transformers are considered one of the key elements of electric grids. Transient studies include transformer transient analysis which is required for the continuous power supply. However, to perform the transient analysis, the details of the internal structure of the transformer are required which are unobtainable and considered as confidential information. Therefore, the application of topological-based transformer models is limited although the models can accurately represent the transformers. To address this concern, a novel approach utilizing Machine Learning (ML) to identify the core aspect ratios of the three-limb core-type transformer is introduced. The proposed approach, using only the voltage and …

Reinforcement Learning: Applying Low Discrepancy Action Selection To Deep Deterministic Policy Gradient, Aleksandr Svishchev

Electronic Theses and Dissertations

Reinforcement learning (RL) is a subfield of machine learning concerned with agents learning to behave optimally by interacting with an environment. One of the most important topics in RL is how the agent should explore, that is, how to choose actions in order to rate their impact on long-term reward. For example, a simple baseline strategy might be uniformly random action selection. This thesis investigates the heuristic idea that agents will learn faster if they explore by factoring the environment’s state into their decision and intentionally choose actions which are as different as possible from what they have previously observed. …

Leveraging Targeted Regions Of Interest By Analyzing Code Comprehension With Ai-Enabled Eye-Tracking, Md Shakil Hossain

Electronic Theses and Dissertations

Code comprehension studies techniques for extracting information that give insights on how code is understood. For educators teaching programming courses, this is an important but often difficult task, especially given the challenges of large class sizes, limited time, and grading resources. By analyzing where a student looks during a code comprehension task, instructors can gain insights into what information the student deems important and assess whether they are looking in the right areas of the code. The proportion of time spent viewing a part of the code is also a useful indicator of the student's decision-making process. The goal of …

Reinforcement Learning: Low Discrepancy Action Selection For Continuous States And Actions, Jedidiah Lindborg

Electronic Theses and Dissertations

In reinforcement learning the process of selecting an action during the exploration or exploitation stage is difficult to optimize. The purpose of this thesis is to create an action selection process for an agent by employing a low discrepancy action selection (LDAS) method. This should allow the agent to quickly determine the utility of its actions by prioritizing actions that are dissimilar to ones that it has already picked. In this way the learning process should be faster for the agent and result in more optimal policies.

Eeg Signals Classification Using Lstm-Based Models And Majority Logic, James A. Orgeron

Electronic Theses and Dissertations

The study of elecroencephalograms (EEGs) has gained enormous interest in the last decade with the increase of computational power and availability of EEG signals collected from various human activities or produced during medical tests. The applicability of analyzing EEG signals ranges from helping impaired people communicate or move (using appropriate medical equipment) to understanding people's feelings and detecting diseases.

We proposed new methodology and models for analyzing and classifying EEG signals collected from individuals observing visual stimuli. Our models rely on powerful Long-Short Term Memory (LSTM) Neural Network models, which are currently the state of the art models for performing …

License Plate Image Quality Enhancement Utilizing Super Resolution Generative Adversarial Networks, Mark Moelter

Electronic Theses and Dissertations

This thesis focuses primarily on enhancing the image quality of blurred license plates through the use of Super-Resolution Generative Adversarial Networks (SRGANs) [1]. We propose a synthetic dataset with SRGAN model to promote blurred image quality enhancement, and allow for model evaluation on a multitude of image input and output size combinations. SRGAN is mainly used for low-resolution image enhancement, but by heavily blurring the input images, the model is tested on its ability to blindly deblur and upsample images to the desired super-resolution (SR) size. The model enhances the image quality to nearly that of the reference images. The …

Developing And Validating A Machine Learning-Based Student Attentiveness Tracking System, Andrew L. Sanders

Electronic Theses and Dissertations

Academic instructors and institutions desire the ability to accurately and autonomously measure the attentiveness of students in the classroom. Generally, college departments use unreliable direct communication from students (i.e. emails, phone calls), distracting and Hawthorne effect-inducing observational sit-ins, and end-of-semester surveys to collect feedback regarding their courses. Each of these methods of collecting feedback is useful but does not provide automatic feedback regarding the pace and direction of lectures. Young et al. discuss that attention levels during passive classroom lectures generally drop after about ten to thirty minutes and can be restored to normal levels with regular breaks, novel activities, …

Lstm-Based Model For Human Brain Decisions Using Eeg Signals Analysis, Lorela Bano

Electronic Theses and Dissertations

As machine learning models become more sophisticated, and biometric data becomes more readily available through new non-invasive technologies, it becomes increasingly possible to gain access to interesting biometric data that could revolutionize Human Computer Interaction. In this research, we propose a framework to assess and quantify human preference (like or dislike) on presenting various external visual stimuli. Our framework relies on an Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) based model and on electroencephalogram (EEG) signals analysis to predict Like or Dislike preference of human subjects when presented with various marketing images.

Regression Tree Construction For Reinforcement Learning Problems With A General Action Space, Anthony S. Bush Jr

Electronic Theses and Dissertations

Part of the implementation of Reinforcement Learning is constructing a regression of values against states and actions and using that regression model to optimize over actions for a given state. One such common regression technique is that of a decision tree; or in the case of continuous input, a regression tree. In such a case, we fix the states and optimize over actions; however, standard regression trees do not easily optimize over a subset of the input variables\cite{Card1993}. The technique we propose in this thesis is a hybrid of regression trees and kernel regression. First, a regression tree splits over …

Determining Political Inclination In Tweets Using Transfer Learning, Mehtab Iqbal

Electronic Theses and Dissertations

Last few years have seen tremendous development in neural language modeling for transfer learning and downstream applications. In this research, I used Howard and Ruder’s Universal Language Model Fine Tuning (ULMFiT) pipeline to develop a classifier that can determine whether a tweet is politically left leaning or right leaning by likening the content to tweets posted by @TheDemocrats or @GOP accounts on Twitter. We achieved 87.7% accuracy in predicting political ideological inclination.

Computer Vision-Based Traffic Sign Detection And Extraction: A Hybrid Approach Using Gis And Machine Learning, Zihao Wu

Electronic Theses and Dissertations

Traffic sign detection and positioning have drawn considerable attention because of the recent development of autonomous driving and intelligent transportation systems. In order to detect and pinpoint traffic signs accurately, this research proposes two methods. In the first method, geo-tagged Google Street View images and road networks were utilized to locate traffic signs. In the second method, both traffic signs categories and locations were identified and extracted from the location-based GoPro video. TensorFlow is the machine learning framework used to implement these two methods. To that end, 363 stop signs were detected and mapped accurately using the first method (Google …

Data Patterns Discovery Using Unsupervised Learning, Rachel A. Lewis

Electronic Theses and Dissertations

Self-care activities classification poses significant challenges in identifying children’s unique functional abilities and needs within the exceptional children healthcare system. The accuracy of diagnosing a child's self-care problem, such as toileting or dressing, is highly influenced by an occupational therapists’ experience and time constraints. Thus, there is a need for objective means to detect and predict in advance the self-care problems of children with physical and motor disabilities. We use clustering to discover interesting information from self-care problems, perform automatic classification of binary data, and discover outliers. The advantages are twofold: the advancement of knowledge on identifying self-care problems in …

Multiclass Classification Of Risk Factors For Cervical Cancer Using Artificial Neural Networks, Abdullah Al Mamun

Electronic Theses and Dissertations

World Health Organization statistics show that cervical cancer is the fourth most frequent cancer in women with an estimated 530,000 new cases in 2012. Cervical cancer diagnosis typically involves liquid-based cytology (LBC) followed by a pathologist review. The accuracy of decision is therefore highly influenced by the expert’s skills and experience, resulting in relatively high false positive and/or false negative rates. Moreover, given the fact that the data being analyzed is highly dimensional, same reviewer’s decision is inherently affected by inconsistencies in interpreting the data. In this study, we use an Artificial Neural Network based model that aims to considerably …

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 …

Multiclass Classification Using Support Vector Machines, Duleep Prasanna W. Rathgamage Don

Electronic Theses and Dissertations

In this thesis, we discuss different SVM methods for multiclass classification and introduce the Divide and Conquer Support Vector Machine (DCSVM) algorithm which relies on data sparsity in high dimensional space and performs a smart partitioning of the whole training data set into disjoint subsets that are easily separable. A single prediction performed between two partitions eliminates one or more classes in a single partition, leaving only a reduced number of candidate classes for subsequent steps. The algorithm continues recursively, reducing the number of classes at each step until a final binary decision is made between the last two classes …