Digital Commons Network^™

Private And Federated Deep Learning: System, Theory, And Applications For Social Good, Han Hu

Dissertations

During the past decade, drug abuse continues to accelerate towards becoming the most severe public health problem in the United States. The ability to detect drug­abuse risk behavior at a population scale, such as among the population of Twitter users, can help to monitor the trend of drug­abuse incidents. However, traditional methods do not effectively detect drug­abuse risk behavior in tweets, mainly due to the sparsity of such tweets and the noisy nature of tweets. In the first part of this dissertation work, the task of classifying tweets as containing drug­abuse risk behavior or not, is studied. Millions of public …

Machine Learning And Computer Vision In Solar Physics, Haodi Jiang

Dissertations

In the recent decades, the difficult task of understanding and predicting violent solar eruptions and their terrestrial impacts has become a strategic national priority, as it affects the life of human beings, including communication, transportation, the power grid, national defense, space travel, and more. This dissertation explores new machine learning and computer vision techniques to tackle this difficult task. Specifically, the dissertation addresses four interrelated problems in solar physics: magnetic flux tracking, fibril tracing, Stokes inversion and vector magnetogram generation.

First, the dissertation presents a new deep learning method, named SolarUnet, to identify and track solar magnetic flux elements in …

Deep Parameter Selection For Classic Computer Vision Applications, Michael Whitney

Theses and Dissertations

A trend in computer vision today is to retire older, so-called "classic'' methods in favor of ones based on deep neural networks. This has led to tremendous improvements in many areas, but for some problems deep neural solutions may not yet exist or be of practical application. For this and other reasons, classic methods are still widely used in a variety of applications. This paper explores the possibility of using deep neural networks to improve these older methods instead of replace them. In particular, it addresses the issue of parameter selection in these algorithms by using a neural network to …

Uncertainty-Aware Deep Learning For Prediction Of Remaining Useful Life Of Mechanical Systems, Samuel J. Cornelius

Theses and Dissertations

Remaining useful life (RUL) prediction is a problem that researchers in the prognostics and health management (PHM) community have been studying for decades. Both physics-based and data-driven methods have been investigated, and in recent years, deep learning has gained significant attention. When sufficiently large and diverse datasets are available, deep neural networks can achieve state-of-the-art performance in RUL prediction for a variety of systems. However, for end users to trust the results of these models, especially as they are integrated into safety-critical systems, RUL prediction uncertainty must be captured. This work explores an approach for estimating both epistemic and heteroscedastic …

Pranayama Breathing Detection With Deep Learning, Bikash Shrestha

Theses

Yoga, a complementary health approach, according to a 2017 National Health Interview Survey by the Center for Disease Control and Prevention (CDC), is a choice of around 14.3% adults in the US. Kapalbhati pranayama, a yoga practice of alternating fast exhales and longer passive inhales, is understood to improve our health. Incorrect and irregular practices, however, can cause injuries and adverse effects. To avoid these undesired effects, it is essential to maintain a pace fit for the practitioner. In the absence of any tools to observe a pace of practice, this work develops a deep learning method that listens to …

Auto-Curation Of Large Evolving Image Datasets, Sara Mousavicheshmehkaboodi

Doctoral Dissertations

Large image collections are becoming common in many fields and offer tantalizing opportunities to transform how research, work, and education are conducted if the information and associated insights could be extracted from them. However, major obstacles to this vision exist. First, image datasets with associated metadata contain errors and need to be cleaned and organized to be easily explored and utilized. Second, such collections typically lack the necessary context or may have missing attributes that need to be recovered. Third, such datasets are domain-specific and require human expert involvement to make the right interpretation of the image content. Fourth, the …

Contrastive Learning For Unsupervised Auditory Texture Models, Christina Trexler

Computer Science and Computer Engineering Undergraduate Honors Theses

Sounds with a high level of stationarity, also known as sound textures, have perceptually relevant features which can be captured by stimulus-computable models. This makes texture-like sounds, such as those made by rain, wind, and fire, an appealing test case for understanding the underlying mechanisms of auditory recognition. Previous auditory texture models typically measured statistics from auditory filter bank representations, and the statistics they used were somewhat ad-hoc, hand-engineered through a process of trial and error. Here, we investigate whether a better auditory texture representation can be obtained via contrastive learning, taking advantage of the stationarity of auditory textures to …

From Language Comprehension Towards General Ai, Binay Dahal

UNLV Theses, Dissertations, Professional Papers, and Capstones

Language comprehension or more formally, natural language understanding is one of the major undertakings in Artificial Intelligence. In this work, we explore a few of the problems in language understanding using fixed deep learning models. Specifically, first, we look into question generation. Asking questions relates to the cognitive ability of language comprehension and context understanding. For that reason, making progress in question generation is significant. We introduce a novel task called “question generation with masked target answer” and propose various models and present the baseline result for the task. Next, we extend on the question generation task and develop a …

Joint Linear And Nonlinear Computation With Data Encryption For Efficient Privacy-Preserving Deep Learning, Qiao Zhang

Electrical & Computer Engineering Theses & Dissertations

Deep Learning (DL) has shown unrivalled performance in many applications such as image classification, speech recognition, anomalous detection, and business analytics. While end users and enterprises own enormous data, DL talents and computing power are mostly gathered in technology giants having cloud servers. Thus, data owners, i.e., the clients, are motivated to outsource their data, along with computationally-intensive tasks, to the server in order to leverage the server’s abundant computation resources and DL talents for developing cost-effective DL solutions. However, trust is required between the server and the client to finish the computation tasks (e.g., conducting inference for the newly-input …

Multi-Object Localization In Robotic Hand, Tsing Tsow

USF Tampa Graduate Theses and Dissertations

We have developed a machine learning approach to localized objects inside a robotic hand using only images from 2D cameras. Specifically, we used deep learning method (You Only Look Once, YOLO) and Iterative closest Point (ICP) to estimate the 3D coordinates of the objects in a robotic hand. This method will also output the number of objects inside the robotic hand in addition to the coordinates of the objects. We have demonstrated the performance with simulation and obtained typical accuracy within a few pixels (couple mm) and counting accuracy of about 76%. We have also applied it to real images, …

Deep Learning Models For Irregularly Sampled And Incomplete Time Series, Satya Narayan Shukla

Doctoral Dissertations

Irregularly sampled time series data arise naturally in many application domains including biology, ecology, climate science, astronomy, geology, finance, and health. Such data present fundamental challenges to many classical models from machine learning and statistics. The first challenge with modeling such data is the presence of variable time gaps between the observation time points. The second challenge is that the dimensionality of the inputs can be different for different data cases. This occurs naturally due to the fact that different data cases are likely to include different numbers of observations. The third challenge is that different irregularly sampled instances have …

Neural Network Based Approach For Detecting Location Spoofing In Vehicular Communication, Smarth Kukreja

Electronic Theses and Dissertations

Vehicular Ad hoc Network (VANET) is an evolving subset of MANET. It's deployed on the roads, where vehicles act as mobile nodes. Active security and Intelligent Transportation System (ITS) are integral applications of VANET, which require stable and uninterrupted vehicle-to-vehicle communication technology. VANET, is a type of wireless network, due to which it is quite prone to security attacks. Extremely dynamic connections, sensitive data sharing and time-sensitivity of this network make it a vulnerable to security attacks. The messages shared between the vehicles are the basic safety message (BSM), these messages are broadcasted by each vehicle in the network to …

Traffic Sign And Light Detection Using Deep Learning For Automotive Applications, Humaira Naimi

Electronic Theses and Dissertations

Traffic sign and light detection are core components of Advanced Driver Assistance Systems (ADAS) and self-driving vehicles. The automotive industry is widely employing numerous approaches for automation through computer vision techniques. Object detection algorithms based on deep learning can be divided into two main categories, two stage and single stage detection algorithms. Two stage algorithms are designed to improve detection accuracy. While single stage algorithms are constructed to be faster, this increases their suitability for real time applications. This thesis presents a lightweight traffic sign and light detector by adapting a single stage, Single Shot Multibox Detection (SSD) algorithm by …

Towards More Trustworthy Deep Learning: Accurate, Resilient, And Explainable Countermeasures Against Adversarial Examples, Fei Zuo

Theses and Dissertations

Despite the great achievements made by neural networks on tasks such as image classification, they are brittle and vulnerable to adversarial example (AE) attacks, which are crafted by adding human-imperceptible perturbations to inputs in order that a neural-network-based classifier incorrectly labels them. Along with the prevalence of deep learning techniques, the threat of AEs attracts increasingly attentions since it may lead to serious consequences in some vital applications such as disease diagnosis.

To defeat attacks based on AEs, both detection and defensive techniques attract the research community’s attention. Given an input image, the detection system outputs whether it is an …

Advancing Proper Dataset Partitioning And Classification Of Visual Search And The Vigilance Decrement Using Eeg Deep Learning Algorithms, Alexander J. Kamrud

Theses and Dissertations

Electroencephalography (EEG) classification of visual search and vigilance tasks has vast potential in its benefits. In future human-machine teaming systems, EEG could act as the tool for operator state assessment, enabling AI teammates to know when to assist the operator in these tasks, with the potential to lead to increased safety of operations, better training systems for our operators, and improved operational effectiveness. This research investigates deep learning methods which utilize EEG signals to classify the efficiency of an operator's search and to classify whether an operator is in a decrement during a vigilance type task, and investigates performing these …

Deep Learning For Weather Clustering And Forecasting, Nathaniel R. Beveridge

Theses and Dissertations

Clustering weather data is a valuable endeavor in multiple respects. The results can be used in various ways within a larger weather prediction framework or could simply serve as an analytical tool for characterizing climatic differences of a particular region of interest. This research proposes a methodology for clustering geographic locations based on the similarity in shape of their temperature time series over a long time horizon of approximately 11 months. To this end an emerging and powerful class of clustering techniques that leverages deep learning, called deep representation clustering (DRC), are utilized. Moreover, a time series specific DRC algorithm …

Gradient Free Sign Activation Zero One Loss Neural Networks For Adversarially Robust Classification, Yunzhe Xue

Dissertations

The zero-one loss function is less sensitive to outliers than convex surrogate losses such as hinge and cross-entropy. However, as a non-convex function, it has a large number of local minima, andits undifferentiable attribute makes it impossible to use backpropagation, a method widely used in training current state-of-the-art neural networks. When zero-one loss is applied to deep neural networks, the entire training process becomes challenging. On the other hand, a massive non-unique solution probably also brings different decision boundaries when optimizing zero-one loss, making it possible to fight against transferable adversarial examples, which is a common weakness in deep learning …

Towards Adversarial Robustness With 01 Lossmodels, And Novel Convolutional Neural Netsystems For Ultrasound Images, Meiyan Xie

Dissertations

This dissertation investigates adversarial robustness with 01 loss models and a novel convolutional neural net systems for vascular ultrasound images.

In the first part, the dissertation presents stochastic coordinate descent for 01 loss and its sensitivity to adversarial attacks. The study here suggests that 01 loss may be more resilient to adversarial attacks than the hinge loss and further work is required.

In the second part, this dissertation proposes sign activation network with a novel gradient-free stochastic coordinate descent algorithm and its ensembling model. The study here finds that the ensembling model gives a high minimum distortion (as measured by …

Advances In Deep Learning With Applications To Computer Vision And Astronomy, Zhihang Hu

Dissertations

Deep Learning has spanned a variety of applications in computer vision as well as computational astronomy. These two aspects obtained similar data structure, therefore, their solutions can be transferable between each other. This dissertation look into two video-related tasks in computer vision and propose a novel problem in computational astronomy.

Specifically, acquiring an in-depth understanding of videos has been a cornerstone problem in computer vision. This problem has been studied by various researchers from different perspectives, among which video prediction has attracted much attention. Video prediction aims to generate the pixels of future frames given a sequence of context frames. …

Domain Adaptive Transfer Learning For Visual Classification, Ashiq Imran

Computer Science and Engineering Dissertations

Deep Neural Networks have made a significant impact on many computer vision applications with large-scale labeled datasets. However, in many applications, it is expensive and time-consuming to gather large-scale labeled data. With the limited availability of labeled data, it is challenging to obtain great performance. Moreover, in many real-world problems, transfer learning has been applied to cope with limited labeled training data. Transfer learning is a machine learning paradigm where pre-trained models on one task can be reused for another task. This dissertation investigates transfer learning and related machine learning techniques such as domain adaptation on visual categorization applications. At …

Machine Learning Methods To Improve Fairness And Prediction Accuracy On Largesocially Relevant Datasets, Bhanu Chaturvedi Jain

Computer Science and Engineering Dissertations

Machine learning-based decision support systems bring relief to the decision-makers in many domains such as loan application acceptance, dating, hiring, granting parole, insurance coverage, and medical diagnoses. These support systems facilitate processing tremendous amounts of data to decipher the embedded patterns. However,these decisions can also absorb and amplify bias embedded in the data. An increasing number of applications of machine learning-based decision sup-port systems in a growing number of domains has directed the attention of stake-holders to the accuracy, transparency, interpretability, cost effectiveness, and fairness encompassed in the ensuing decisions. In this dissertation, we have focused on fairness and accuracy …

Breast Ultrasound Image Segmentation Based On Uncertainty Reduction And Context Information, Kuan Huang

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Breast cancer frequently occurs in women over the world. It was one of the most serious diseases and the second common cancer among women in 2019. The survival rate of stages 0 and 1 of breast cancer is closed to 100%. It is urgent to develop an approach that can detect breast cancer in the early stages. Breast ultrasound (BUS) imaging is low-cost, portable, and effective; therefore, it becomes the most crucial approach for breast cancer diagnosis. However, BUS images are of poor quality, low contrast, and uncertain. The computer-aided diagnosis (CAD) system is developed for breast cancer to prevent …

Essays On Fake Review Detection, Managerial Response, And Consumer Perceptions, Long Chen

Theses and Dissertations

This dissertation investigates how online reviews and managerial responses jointly affect consumer perceptions. I first examine and compare the outcomes of multiple fake review classifiers using various algorithms, including traditional machine learning methods and recently developed deep learning methods (essay I). Then, based on the findings of the first essay, I examine the interrelationship between fake review detection, managerial response, and hotel ratings and ratings’ growths (essay II).The first essay is a comparative study on the methodology of identifying fake reviews. Although online reviews have attracted much attention from academia and industry for over fifteen years, how to identify fake …

Applying Deep Learning To The Ice Cream Vendor Problem: An Extension Of The Newsvendor Problem, Gaffar Solihu

Electronic Theses and Dissertations

The Newsvendor problem is a classical supply chain problem used to develop strategies for inventory optimization. The goal of the newsvendor problem is to predict the optimal order quantity of a product to meet an uncertain demand in the future, given that the demand distribution itself is known. The Ice Cream Vendor Problem extends the classical newsvendor problem to an uncertain demand with unknown distribution, albeit a distribution that is known to depend on exogenous features. The goal is thus to estimate the order quantity that minimizes the total cost when demand does not follow any known statistical distribution. The …

Automation Of Radiation Treatment Planning For Cervical Cancer, Dong Joo Rhee

Dissertations & Theses (Open Access)

Cervical cancer is one of the most common cancer in low- and middle-income countries (LMICs). The mortality rate can be reduced if radiation treatment becomes widely available. However, due to the lack of radiation treatment facilities and human resources, many cervical cancer patients in Africa are not able to receive timely treatments or advanced therapies. To increase the availability of radiation treatment in low-and middle-income countries (LMICs) including African countries, many attempts have been made to reduce the cost of medical linear accelerators. However, increasing the number of treatment machines would not instantly resolve the issues, as there would be …

Enhancing Microbiome Host Disease Prediction With Variational Autoencoders, Celeste Manughian-Peter

Computational and Data Sciences (MS) Theses

Advancements in genetic sequencing methods for microbiomes in recent decades have permitted the collection of taxonomic and functional profiles of microbial communities, accelerating the discovery of the functional aspects of the microbiome and generating an increased interest among clinicians in applying these techniques with patients. This advancement has coincided with software and hardware improvements in the field of machine learning and deep learning. Combined, these advancements implicate further potential for progress in disease diagnosis and treatment in humans. The ability to classify a human microbiome profile into a disease category, and additionally identify the differentiating factors within the profile between …

Identification Of Chemical Structures And Substructures Via Deep Q-Learning And Supervised Learning Of Ftir Spectra, Joshua D. Ellis

MSU Graduate Theses

Fourier-transform infrared (FTIR) spectra of organic compounds can be used to compare and identify compounds. A mid-FTIR spectrum gives absorbance values of a compound over the 400-4000 cm^-1 range. Spectral matching is the process of comparing the spectral signature of two or more compounds and returning a value for the similarity of the compounds based on how closely their spectra match. This process is commonly used to identify an unknown compound by searching for its spectrum’s closes match in a database of known spectra. A major limitation of this process is that it can only be used to identify …

Predicting Severity Of Traumatic Brain Injury: A Residual Learning Model From Magnetic Resonance Images, Dacosta Yeboah

MSU Graduate Theses

One of the most significant frontiers for computational scientists is the engineering of human healthcare delivery based on intelligent analysis of health data. In a variety of neurological disorders such as Traumatic Brain Injury (TBI), neuro-imaging information plays a crucial role in the decision-making regarding patient care and as a potential prognostic marker for outcome. TBI is a heterogeneous neurological disorder. Due to the economic burdens of the disorder, sorting out this heterogeneity could provide more insights and better understanding of TBI recovery trajectories, thus improving overall diagnosis and treatment options. Magnetic Resonance Imaging (MRI) is a non-invasive technique that …

Improved Motor Imagery Decoding Using Deep Learning Techniques, Olawunmi Olaboopo George

Dissertations (1934 -)

Motor imagery (MI) has been one of the most used paradigms for building brain-computer interfaces (BCI), widely used in neurorehabilitation, for restoring functionality to damaged parts of a neurologically deficient person. The existing motor imagery techniques have largely employed feature extraction techniques such as the power spectral density (PSD) and the common spatial patterns (CSP) before classification, using traditional machine learning algorithms such as support vector machines (SVM) and linear discriminant analysis (LDA). These algorithms are quite limited in their ability to generate feature representations for certain types of signals, limiting the potential for improvements in the decoding process. Also, …

Development Of Deep Learning Neural Network For Ecological And Medical Images, Shaobo Liu

Dissertations

Deep learning in computer vision and image processing has attracted attentions from various fields including ecology and medical image. Ecologists are interested in finding an effective model structure to classify different species. Tradition deep learning model use a convolutional neural network, such as LeNet, AlexNet, VGG models, residual neural network, and inception models, are first used on classifying bee wing and butterfly datasets. However, insufficient data sample and unbalanced samples in each class have caused a poor accuracy. To make improvement the test accuracy, data augmentation and transfer learning are applied. Recently developed deep learning framework based on mathematical morphology …