Physical Sciences and Mathematics Commons^™

Detection And Classification Of Diabetic Retinopathy Using Deep Learning Models, Aishat Olatunji

Electronic Theses and Dissertations

Healthcare analytics leverages extensive patient data for data-driven decision-making, enhancing patient care and results. Diabetic Retinopathy (DR), a complication of diabetes, stems from damage to the retina’s blood vessels. It can affect both type 1 and type 2 diabetes patients. Ophthalmologists employ retinal images for accurate DR diagnosis and severity assessment. Early detection is crucial for preserving vision and minimizing risks. In this context, we utilized a Kaggle dataset containing patient retinal images, employing Python’s versatile tools. Our research focuses on DR detection using deep learning techniques. We used a publicly available dataset to apply our proposed neural network and …

Evaluation Of An End-To-End Radiotherapy Treatment Planning Pipeline For Prostate Cancer, Mohammad Daniel El Basha, Court Laurence, Carlos Eduardo Cardenas, Julianne Pollard-Larkin, Steven Frank, David T. Fuentes, Falk Poenisch, Zhiqian H. Yu

Dissertations & Theses (Open Access)

Radiation treatment planning is a crucial and time-intensive process in radiation therapy. This planning involves carefully designing a treatment regimen tailored to a patient’s specific condition, including the type, location, and size of the tumor with reference to surrounding healthy tissues. For prostate cancer, this tumor may be either local, locally advanced with extracapsular involvement, or extend into the pelvic lymph node chain. Automating essential parts of this process would allow for the rapid development of effective treatment plans and better plan optimization to enhance tumor control for better outcomes.

The first objective of this work, to automate the treatment …

Pedestrian Pathing Prediction Using Complex Contextual Behavioral Data In High Foot Traffic Settings, Laurel Bingham

All Graduate Theses and Dissertations, Fall 2023 to Present

Ensuring the safe integration of autonomous vehicles into real-world environments requires a comprehensive understanding of pedestrian behavior. This study addresses the challenge of predicting the movement and crossing intentions of pedestrians, a crucial aspect in the development of fully autonomous vehicles.

The research focuses on leveraging Honda's TITAN dataset, comprising 700 unique clips captured by moving vehicles in high-foot-traffic areas of Tokyo, Japan. Each clip provides detailed contextual information, including human-labeled tags for individuals and vehicles, encompassing attributes such as age, motion status, and communicative actions. Long Short-Term Memory (LSTM) networks were employed and trained on various combinations of contextual …

Extracting Dnn Architectures Via Runtime Profiling On Mobile Gpus, Dong Hyub Kim

Masters Theses

Due to significant investment, research, and development efforts over the past decade, deep neural networks (DNNs) have achieved notable advancements in classification and regression domains. As a result, DNNs are considered valuable intellectual property for artificial intelligence providers. Prior work has demonstrated highly effective model extraction attacks which steal a DNN, dismantling the provider’s business model and paving the way for unethical or malicious activities, such as misuse of personal data, safety risks in critical systems, or spreading misinformation. This thesis explores the feasibility of model extraction attacks on mobile devices using aggregated runtime profiles as a side-channel to leak …

Smart Applications And Resource Management In Internet Of Things, Zeinab Akhavan

Computer Science ETDs

Internet of Things (IoT) technologies are currently the principal solutions driving smart cities. These new technologies such as Cyber Physical Systems, 5G and data analytic have emerged to address various cities' infrastructure issues ranging from transportation and energy management to healthcare systems. An IoT setting primarily consists of a wide range of users and devices as a massive network interacting with different layers of the city infrastructure resulting in generating sheer volume of data to enable smart city services. The goal of smart city services is to create value for the entire ecosystem, whether this is health, education, transportation, energy, …

Utilizing Multitask Transfer Learning For Sonographic Rheumatoid Arthritis Synovitis Grading, Jordan Marie Claire Sanders

Doctoral Dissertations and Master's Theses

Classifying the four sonographic Rheumatoid Arthritis (RA) synovitis grades (Grade 0, Grade 1, Grade 2, and Grade 3) is a difficult problem due to the complexity of the relevant markers. Therefore, the current research proposes a Multitask Transfer Learning (MTL) framework for sonographic RA synovitis grading of Ultrasound (US) images in Brightness mode (B-Mode) and Power Doppler mode.

In the medical community, the lack of reliability of scoring these images has been an issue and reason for concern for doctors and other medical practitioners. The human/machine variability across the acquisition procedure of these US images creates an additional challenge that …

Generalized Differentiable Neural Architecture Search With Performance And Stability Improvements, Emily J. Herron

Doctoral Dissertations

This work introduces improvements to the stability and generalizability of Cyclic DARTS (CDARTS). CDARTS is a Differentiable Architecture Search (DARTS)-based approach to neural architecture search (NAS) that uses a cyclic feedback mechanism to train search and evaluation networks concurrently, thereby optimizing the search process by enforcing that the networks produce similar outputs. However, the dissimilarity between the loss functions used by the evaluation networks during the search and retraining phases results in a search-phase evaluation network, a sub-optimal proxy for the final evaluation network utilized during retraining. ICDARTS, a revised algorithm that reformulates the search phase loss functions to ensure …

Context-Aware Temporal Embeddings For Text And Video Data, Ahnaf Farhan

Open Access Theses & Dissertations

Recent years have seen an exponential increase in unstructured data, primarily in the form of text, images, and videos. Extracting useful features and trends from large-scale unstructured datasets -- such as news outlets, scientific papers, and videos like security cameras or body cam recordings -- is faced with substantial challenges of volume, scalability, complexity, and semantic understanding. In analyzing trends, comprehending the temporal context is vital for uncovering patterns and narratives that are not apparent from a single video frame or text document. Despite its importance, many existing data mining and machine learning approaches overlook extracting evolutionary contextual features in …

Deep Learning For Photovoltaic Characterization, Adrian Manuel De Luis Garcia

Graduate Theses and Dissertations

This thesis introduces a novel approach to Photovoltaic (PV) installation segmentation by proposing a new architecture to understand and identify PV modules from overhead imagery. Pivotal to this concept is the creation of a new Transformer-based network, S3Former, which focuses on small object characterization and modelling intra- and inter- object differentiation inside an image. Accurate mapping of PV installations is pivotal for understanding their adoption and guiding energy policy decisions. Drawing insights from current Deep Learning methodologies for image segmentation and building upon State-of-the-Art (SOTA) techniques in solar cell mapping, this work puts forth S3Former with the following enhancements: 1. …

Domain Specific Feature Representation Learning For Diverse Temporal Data, Farhan Asif Chowdhury

Computer Science ETDs

Humans can leverage domain context to recognize novel patterns and categories based on limited known examples. In contrast, computational learning methods are not adept at exploiting context and require sufficient labeled examples to achieve similar accuracy. Many temporal data domain, for example, seismic signals and oil mining sensor data, requires domain expert annotation, which is both costly and time-consuming. The dependency on training data limits the applicability of machine learning algorithms for domains with limited labeled data. This dissertation aims to address this gap by developing temporal mining algorithms that exploit domain context to learn discriminative feature representation from limited …

Machine Learning Modeling Of Polymer Coating Formulations: Benchmark Of Feature Representation Schemes, Nelson I. Evbarunegbe

Masters Theses

Polymer coatings offer a wide range of benefits across various industries, playing a crucial role in product protection and extension of shelf life. However, formulating them can be a non-trivial task given the multitude of variables and factors involved in the production process, rendering it a complex, high-dimensional problem. To tackle this problem, machine learning (ML) has emerged as a promising tool, showing considerable potential in enhancing various polymer and chemistry-based applications, particularly those dealing with high dimensional complexities.

Our research aims to develop a physics-guided ML approach to facilitate the formulations of polymer coatings. As the first step, this …

Deciphering Trends And Tactics: Data-Driven Techniques For Forecasting Information Spread And Detecting Coordinated Campaigns In Social Media, Kin Wai Ng Lugo

USF Tampa Graduate Theses and Dissertations

The main objective of this dissertation is to develop models that predict and investigate the spread of information in social media over time. In this context, we consider topics of discussions as the information that spreads. Thus, we are interested in forecasting the number of messages per day in a future interval of time. We take a data-driven approach, in which we compare our results with real datasets from a multitude of socio-political contexts and from multiple social media platforms, specifically, Twitter and YouTube.

We identified a number of challenges related to forecasting social media time series per topic. First, …

Evaluating Methods For Improving Dnn Robustness Against Adversarial Attacks, Laureano Griffin

USF Tampa Graduate Theses and Dissertations

Deep learning has become more widespread as advances in the field continue. As aresult, making sure deep learning is safe has become a priority. A seemingly normal image with intentional pixel changes can cause a well-trained model to misclassify the image with high confidence. Those kinds of images are called adversarial attacks. Adversarial training has been developed to defend against adversarial attacks. This thesis evaluates different adversarial training methods against a variety of adversarial attacks. The key metrics for evaluation are classification accuracy and training time. This thesis also experiments with an improvement on an existing adversarial training method, the …

Optimization And Application Of Graph Neural Networks, Shuo Zhang

Dissertations, Theses, and Capstone Projects

Graph Neural Networks (GNNs) are widely recognized for their potential in learning from graph-structured data and solving complex problems. However, optimal performance and applicability of GNNs have been an open-ended challenge. This dissertation presents a series of substantial advances addressing this problem. First, we investigate attention-based GNNs, revealing a critical shortcoming: their ignorance of cardinality information that impacts their discriminative power. To rectify this, we propose Cardinality Preserved Attention (CPA) models that can be applied to any attention-based GNNs, which exhibit a marked improvement in performance. Next, we introduce the Directional Node Pair (DNP) descriptor and the Robust Molecular Graph …

Out-Of-Distribution Generalization Of Deep Learning To Illuminate Dark Protein Functional Space, Tian Cai

Dissertations, Theses, and Capstone Projects

Dark protein illumination is a fundamental challenge in drug discovery where majority human proteins are understudied, i.e. with only known protein sequence but no known small molecule binder. It's a major road block to enable drug discovery paradigm shift from single-targeted which looks to identify a single target and design drug to regulate the single target to multi-targeted in a Systems Pharmacology perspective. Diseases such as Alzheimer's and Opioid-Use-Disorder plaguing millions of patients call for effective multi-targeted approach involving dark proteins. Using limited protein data to predict dark protein property requires deep learning systems with OOD generalization capacity. Out-of-Distribution (OOD) …

Learning Representations For Effective And Explainable Software Bug Detection And Fixing, Yi Li

Dissertations

Software has an integral role in modern life; hence software bugs, which undermine software quality and reliability, have substantial societal and economic implications. The advent of machine learning and deep learning in software engineering has led to major advances in bug detection and fixing approaches, yet they fall short of desired precision and recall. This shortfall arises from the absence of a 'bridge,' known as learning code representations, that can transform information from source code into a suitable representation for effective processing via machine and deep learning.

This dissertation builds such a bridge. Specifically, it presents solutions for effectively learning …

Countnet3d: A 3d Computer Vision Approach To Infer Counts Of Occluded Objects With Quantified Uncertainty, Stephen W. Nelson

Theses and Dissertations

3D scene understanding is an important problem that has experienced great progress in recent years, in large part due to the development of state-of-the-art methods for 3D object detection. However, the performance of 3D object detectors can suffer in scenarios where extreme occlusion of objects is present, or the number of object classes is large. In this paper, we study the problem of inferring 3D counts from densely packed scenes with heterogeneous objects. This problem has applications to important tasks such as inventory management or automatic crop yield estimation. We propose a novel regression-based method, CountNet3D, that uses mature 2D …

Global Cyber Attack Forecast Using Ai Techniques, Nusrat Kabir Samia

Electronic Thesis and Dissertation Repository

The advancement of internet technology and growing involvement in the cyber world have made us prone to cyber-attacks inducing severe damage to individuals and organizations, including financial loss, identity theft, and reputational damage. The rapid emergence and evolution of new networks and new opportunities for businesses and technologies are increasing threats to security vulnerabilities. Hence cyber-crime analysis is one of the wide range applications of Data Mining that can be eventually used to predict and detect crime. However, there are several constraints while analyzing cyber-attacks, which are yet to be resolved for more accurate cyber security inspection.

Although there are …

Predicting Network Failures With Ai Techniques, Chandrika Saha

Electronic Thesis and Dissertation Repository

Network failure is the unintentional interruption of internet services, resulting in widespread client frustration. It is especially true for time-sensitive services in the healthcare industry, smart grid control, and mobility control, among others. In addition, the COVID-19 pandemic has compelled many businesses to operate remotely, making uninterrupted internet access essential. Moreover, Internet Service Providers (ISPs) lose millions of dollars annually due to network failure, which has a negative impact on their businesses. Currently, redundant network equipment is used as a restoration technique to resolve this issue of network failure. This technique requires a strategy for failure identification and prediction to …

The Impacts Of Transfer Learning For Ungulate Recognition At Sevilleta National Wildlife Refuge, Michael Gurule

Geography ETDs

As camera traps have grown in popularity, their utilization has expanded to numerous fields, including wildlife research, conservation, and ecological studies. The information gathered using this equipment gives researchers a precise and comprehensive understanding about the activities of animals in their natural environments. For this type of data to be useful, camera trap images must be labeled so that the species in the images can be classified and counted. This has typically been done by teams of researchers and volunteers, and it can be said that the process is at best time-consuming. With recent developments in deep learning, the process …

Invading The Integrity Of Deep Learning (Dl) Models Using Lsb Perturbation & Pixel Manipulation, Ashraful Tauhid

Theses and Dissertations

The use of deep learning (DL) models for solving classification and recognition-related problems are expanding at an exponential rate. However, these models are computationally expensive both in terms of time and resources. This imposes an entry barrier for low-profile businesses and scientific research projects with limited resources. Therefore, many organizations prefer to use fully outsourced trained models, cloud computing services, pre-trained models are available for download and transfer learning. This ubiquitous adoption of DL has unlocked numerous opportunities but has also brought forth potential threats to its prospects. Among the security threats, backdoor attacks and adversarial attacks have emerged as …

Generalizable Deep-Learning-Based Wireless Indoor Localization, Ali Owfi

All Theses

The growing interest in indoor localization has been driven by its wide range of applications in areas such as smart homes, industrial automation, and healthcare. With the increasing reliance on wireless devices for location-based services, accurate estimation of device positions within indoor environments has become crucial. Deep learning approaches have shown promise in leveraging wireless parameters like Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) to achieve precise localization. However, despite their success in achieving high accuracy, these deep learning models suffer from limited generalizability, making them unsuitable for deployment in new or dynamic environments without retraining. To …

Generalizing Deep Learning Methods For Particle Tracing Using Transfer Learning, Shubham Gupta

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Particle tracing is a very important method for scientific visualization of vector fields, but it is computationally expensive. Deep learning can be used to speed up particle tracing, but existing deep learning models are domain-specific. In this work, we present a methodology to generalize the use of deep learning for particle tracing using transfer learning. We demonstrate the performance of our approach through a series of experimental studies that address the most common simulation design scenarios: varying time span, Reynolds number, and problem geometry. The results show that our methodology can be effectively used to generalize and accelerate the training …

Visual Complexity Of The Time-Frequency Image Pinpoints The Epileptogenic Zone: An Unsupervised Deep-Learning Tool To Analyze Interictal Intracranial Eeg, Sarvagya Gupta

Graduate Masters Theses

Epilepsy, a prevalent neurological disorder characterized by recurrent seizures, continues to pose significant challenges in diagnosis and treatment, particularly among children. Despite substantial advancements in medical technology and treatment modalities, localization of the part of brain that causes seizures (Epileptogenic Zone) remains a difficult task. Intracranial EEG (iEEG) is often used to estimate the epileptogenic zone (EZ) in children with drugresistant epilepsy (DRE) and target it during surgery. Conventionally, iEEG signals are inspected in the time domain by human experts aiming to locate epileptiform activity.

Visual scrutiny of the iEEG time-frequency (TF) images can be an alternative way to review …

Geospatial Wildfire Risk Prediction Using Deep Learning, Abner Alberto Benavides

Electronic Theses, Projects, and Dissertations

This report introduces a thorough analysis of wildfire prediction using satellite imagery by applying deep learning techniques. To find wildfire-prone geographical data, we use U-Net, a convolutional neural network known for its effectiveness in biomedical image segmentation. The input to the model is the Sentinel-2 multispectral images to supply a complete view of the terrain features.

We evaluated the wildfire risk prediction model’s performance using several metrics. The model showed high accuracy, with a weighted average F1 score of 0.91 and an AUC-ROC score of 0.972. These results suggest that the model is exceptionally good at predicting the location of …

Using Deep Learning For Encrypted Traffic Analysis Of Amazon Echo, Surendra Pathak

Theses and Dissertations

The adoption of the Amazon Echo family of devices in modern homes has become very widespread at the current time, with hundreds of millions of devices sold. Moreover, the global smart speaker market size is growing vigorously and is projected to continue to bigger. Smart speakers allow users hands-free interaction by allowing voice control, promoting human-computer interaction to greater avenues. Though smart speaker can be useful assistant, it has some serious security concerns that need to be studied. In this study, an analysis of the security and privacy concerns of smart speakers is presented along with a passive attack, namely …

Extending The Convolution In Graph Neural Networks To Solve Materials Science And Node Classification Problems, Steph-Yves Mike Louis

Theses and Dissertations

The usage of graph to represent one's data in machine learning has grown in popularity in both academia and the industry due to its inherent benefits. With its flexible nature and immediate translation to real life observed objects, graph representation had a considerable contribution in advancing the state-of-the-art performance of machine learning in materials.

In this dissertation proposal, we discuss how machines can learn from graph encoded data and provide excellent results through graph neural networks (GNN). Notably, we focus our adaptation of graph neural networks on three tasks: predicting crystal materials properties, nullifying the negative impact of inferior graph …

Predicting Material Structures And Properties Using Deep Learning And Machine Learning Algorithms, Yuqi Song

Theses and Dissertations

Discovering new materials and understanding their crystal structures and chemical properties are critical tasks in the material sciences. Although computational methodologies such as Density Functional Theory (DFT), provide a convenient means for calculating certain properties of materials or predicting crystal structures when combined with search algorithms, DFT is computationally too demanding for structure prediction and property calculation for most material families, especially for those materials with a large number of atoms. This dissertation aims to address this limitation by developing novel deep learning and machine learning algorithms for effective prediction of material crystal structures and properties. Our data-driven machine learning …

A Novel Approach To Extending Music Using Latent Diffusion, Keon Roohparvar, Franz J. Kurfess

Master's Theses

Using deep learning to synthetically generate music is a research domain that has gained more attention from the public in the past few years. A subproblem of music generation is music extension, or the task of taking existing music and extending it. This work proposes the Continuer Pipeline, a novel technique that uses deep learning to take music and extend it in 5 second increments. It does this by treating the musical generation process as an image generation problem; we utilize latent diffusion models (LDMs) to generate spectrograms, which are image representations of music. The Continuer Pipeline is able to …

Deep Learning Recommendations For The Acl2 Interactive Theorem Prover, Robert K. Thompson, Robert K. Thompson

Master's Theses

Due to the difficulty of obtaining formal proofs, there is increasing interest in partially or completely automating proof search in interactive theorem provers. Despite being a theorem prover with an active community and plentiful corpus of 170,000+ theorems, no deep learning system currently exists to help automate theorem proving in ACL2. We have developed a machine learning system that generates recommendations to automatically complete proofs. We show that our system benefits from the copy mechanism introduced in the context of program repair. We make our system directly accessible from within ACL2 and use this interface to evaluate our system in …