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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 …

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 …

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 …

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 …

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 …

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 …

Detecting Ai Generated Text Using Neural Networks, Jesus Guerrero

Masters Theses

For humans, distinguishing machine generated text from human written text is men- tally taxing and slow. NLP models have been created to do this more effectively and faster. But, what if some adversarial changes have been added to the machine generated text? This thesis discusses this issue and text detectors in general.

The primary goal of this thesis is to describe the current state of text detectors in research and to discuss a key adversarial issue in modern NLP transformers. To describe the current state of text detectors a Systematic Literature Review was done on 50 relevant papers to machine-centric …

Enhancing Health Tweet Classification: An Evaluation Of Transformer-Based Models For Comprehensive Analysis, Foram Pankajbhai Patel

Computer Science and Engineering Theses

The task of health tweet classification entails identifying whether a given tweet is health-related or not. While existing research in this area has made significant progress in classifying tweets into specific sub-domains of health, such as mental health, COVID-19, or specific diseases, there is a need for a more comprehensive approach that considers a broader range of health-related topics. This thesis addresses this need by proposing a diverse and comprehensive dataset that includes various existing health-related datasets, data collected through a keyword-based approach, and manually annotated data. However, the use of health-related keywords in a figurative or non-health context poses …

Adaptive And Topological Deep Learning With Applications To Neuroscience, Edward Mitchell

Doctoral Dissertations

Deep Learning and neuroscience have developed a two way relationship with each informing the other. Neural networks, the main tools at the heart of Deep Learning, were originally inspired by connectivity in the brain and have now proven to be critical to state-of-the-art computational neuroscience methods. This dissertation explores this relationship, first, by developing an adaptive sampling method for a neural network-based partial different equation solver and then by developing a topological deep learning framework for neural spike decoding. We demonstrate that our adaptive scheme is convergent and more accurate than DGM -- as long as the residual mirrors the …

Reducing Negative Transfer Of Random Data In Source-Free Unsupervised Domain Adaptation, Anthony Wong

Electronic Thesis and Dissertation Repository

In domain adaptation, a model trained on one dataset (source domain) is applied to a different but related dataset (target domain). The most cutting-edge method is unsupervised source-free domain adaptation (SFDA), in which source data, source labels, and target labels are not available during adaptation. This thesis explores a realistic scenario where the target dataset includes some images that are unrelated to the adaptation process. This scenario can occur from errors in data collection or processing. We provide experiments and analysis to show that current state-of-the-art (SOTA) SFDA methods suffer significant performance drops under a specific domain adaptation setup when …

Graph Analysis On Social Networks, Shen Lu

USF Tampa Graduate Theses and Dissertations

With the development of transportation network, social network, and communication network, there are many applications in streaming data. For example, traffic congestion happens between the origin and destination of daily trips. Traffic analysis can help plan the trips so that traffic congestion can be avoided. Social network and communication network represent the behaviors of the entire population. People build connections based on their hobbies, daily activities, photos, videos, simple messages, and even anonymous web surfing. All of these can be turned into commercial use, such as product marketing, business network building, and technology trending. Data science is about how to …

Attention-Based Multi-Source-Free Domain Adaptation For Eeg Emotion Recognition, Amir Hesam Salimnia

Electronic Thesis and Dissertation Repository

Electroencephalography (EEG) based emotion recognition in affective brain-computer interfaces has advanced significantly in recent years. Unsupervised domain adaptation (UDA) methods have been successfully used to mitigate the need for large amounts of training data, which is required due to the inter-subject variability of EEG signals. Typical UDA solutions require access to raw source data to leverage the knowledge learned from the labelled source domains (previous subjects) across the target domain (a new subject), raising privacy concerns. To tackle this issue, we propose Attention-based Multi-Source-Free Domain Adaptation (AMFDA) for EEG emotion recognition. AMFDA attempts to transfer knowledge of source models to …

Breast Density Classification Using Deep Learning, Conrad Thomas Testagrose

UNF Graduate Theses and Dissertations

Breast density screenings are an accepted means to determine a patient's predisposed risk of breast cancer development. Although the direct correlation is not fully understood, breast cancer risk increases with higher levels of mammographic breast density. Radiologists visually assess a patient's breast density using mammogram images and assign a density score based on four breast density categories outlined by the Breast Imaging and Reporting Data Systems (BI-RADS). There have been efforts to develop automated tools that assist radiologists with increasing workloads and to help reduce the intra- and inter-rater variability between radiologists. In this thesis, I explored two deep-learning-based approaches …

Deep Learning Approaches For Automatic Colorization, Super-Resolution, And Representation Of Volumetric Data, Sudarshan Devkota

Graduate Thesis and Dissertation 2023-2024

This dissertation includes a collection of studies that aim to improve the way we represent and visualize volume data. The advancement of medical imaging has revolutionized healthcare, providing crucial anatomical insights for accurate diagnosis and treatment planning. Our first study introduces an innovative technique to enhance the utility of medical images, transitioning from monochromatic scans to vivid 3D representations. It presents a framework for reference-based automatic color transfer, establishing deep semantic correspondences between a colored reference image and grayscale medical scans. This methodology extends to volumetric rendering, eliminating the need for manual intervention in parameter tuning. Next, it delves into …

Towards A Robust And Efficient Deep Neural Network For The Lidar Point Cloud Perception, Zixiang Zhou

Graduate Thesis and Dissertation 2023-2024

In recent years, LiDAR has emerged as a crucial perception tool for robotics and autonomous vehicles. However, most LiDAR perception methods are adapted from 2D image-based deep learning methods, which are not well-suited to the unique geometric structure of LiDAR point cloud data. This domain gap poses challenges for the fast-growing LiDAR perception tasks. This dissertation aims to investigate suitable deep network structures tailored for LiDAR point cloud data, and therefore design a more efficient and robust LiDAR perception framework. Our approach to address this challenge is twofold. First, we recognize that LiDAR point cloud data is characterized by an …

Machine Learning Framework For Real-World Electronic Health Records Regarding Missingness, Interpretability, And Fairness, Jing Lucas Liu

Theses and Dissertations--Computer Science

Machine learning (ML) and deep learning (DL) techniques have shown promising results in healthcare applications using Electronic Health Records (EHRs) data. However, their adoption in real-world healthcare settings is hindered by three major challenges. Firstly, real-world EHR data typically contains numerous missing values. Secondly, traditional ML/DL models are typically considered black-boxes, whereas interpretability is required for real-world healthcare applications. Finally, differences in data distributions may lead to unfairness and performance disparities, particularly in subpopulations.

This dissertation proposes methods to address missing data, interpretability, and fairness issues. The first work proposes an ensemble prediction framework for EHR data with large missing …

Practical Ai Value Alignment Using Stories, Md Sultan Al Nahian

Theses and Dissertations--Computer Science

As more machine learning agents interact with humans, it is increasingly a prospect that an agent trained to perform a task optimally - using only a measure of task performance as feedback--can violate societal norms for acceptable behavior or cause harm. Consequently, it becomes necessary to prioritize task performance and ensure that AI actions do not have detrimental effects. Value alignment is a property of intelligent agents, wherein they solely pursue goals and activities that are non-harmful and beneficial to humans. Current approaches to value alignment largely depend on imitation learning or learning from demonstration methods. However, the dynamic nature …

Novel Architectures And Optimization Algorithms For Training Neural Networks And Applications, Vasily I. Zadorozhnyy

Theses and Dissertations--Mathematics

The two main areas of Deep Learning are Unsupervised and Supervised Learning. Unsupervised Learning studies a class of data processing problems in which only descriptions of objects are known, without label information. Generative Adversarial Networks (GANs) have become among the most widely used unsupervised neural net models. GAN combines two neural nets, generative and discriminative, that work simultaneously. We introduce a new family of discriminator loss functions that adopts a weighted sum of real and fake parts, which we call adaptive weighted loss functions. Using the gradient information, we can adaptively choose weights to train a discriminator in the direction …

Effective Systems For Insider Threat Detection, Muhanned Qasim Jabbar Alslaiman

Browse all Theses and Dissertations

Insider threats to information security have become a burden for organizations. Understanding insider activities leads to an effective improvement in identifying insider attacks and limits their threats. This dissertation presents three systems to detect insider threats effectively. The aim is to reduce the false negative rate (FNR), provide better dataset use, and reduce dimensionality and zero padding effects. The systems developed utilize deep learning techniques and are evaluated using the CERT 4.2 dataset. The dataset is analyzed and reformed so that each row represents a variable length sample of user activities. Two data representations are implemented to model extracted features …

Computer Vision In Adverse Conditions: Small Objects, Low-Resoltuion Images, And Edge Deployment, Raja Sunkara

Masters Theses

"Computer vision based on deep learning is an essential field that plays a significant role in object detection, image classification, semantic segmentation, instance segmentation, and other applications. However, these models face significant challenges in adverse conditions, such as small objects, low-resolution images, and edge deployment. These challenges limit the accuracy and efficiency of computer vision algorithms, making it difficult to obtain reliable results.

The primary objective of this thesis is to assess the performance of deep learning- based computer vision models in challenging conditions and provide viable solutions to overcome the obstacles. The study will specifically address three key challenges, …

Liquid Tab, Nathan Hulet

Williams Honors College, Honors Research Projects

Guitar transcription is a complex task requiring significant time, skill, and musical knowledge to achieve accurate results. Since most music is recorded and processed digitally, it would seem like many tools to digitally analyze and transcribe the audio would be available. However, the problem of automatic transcription presents many more difficulties than are initially evident. There are multiple ways to play a guitar, many diverse styles of playing, and every guitar sounds different. These problems become even more difficult considering the varying qualities of recordings and levels of background noise.

Machine learning has proven itself to be a flexible tool …

Dynamic Predictions Of Thermal Heating And Cooling Of Silicon Wafer, Hitesh Kumar

Master's Projects

Neural Networks are now emerging in every industry. All the industries are trying their best to exploit the benefits of neural networks and deep learning to make predictions or simulate their ongoing process with the use of their generated data. The purpose of this report is to study the heating pattern of a silicon wafer and make predictions using various machine learning techniques. The heating of the silicon wafer involves various factors ranging from number of lamps, wafer properties and points taken in consideration to capture the heating temperature. This process involves dynamic inputs which facilitates the heating of the …

Enhancing Facial Emotion Recognition Using Image Processing With Cnn, Sourabh Deokar

Master's Projects

Facial expression recognition (FER) has been a challenging task in computer vision for decades. With recent advancements in deep learning, convolutional neural networks (CNNs) have shown promising results in this field. However, the accuracy of FER using CNNs heavily relies on the quality of the input images and the size of the dataset. Moreover, even in pictures of the same person with the same expression, brightness, backdrop, and stance might change. These variations are emphasized when comparing pictures of individuals with varying ethnic backgrounds and facial features, which makes it challenging for deep-learning models to classify. In this paper, we …

Federated Learning For Protecting Medical Data Privacy, Abhishek Reddy Punreddy

Master's Projects

Deep learning is one of the most advanced machine learning techniques, and its prominence has increased in recent years. Language processing, predictions in medical research and pattern recognition are few of the numerous fields in which it is widely utilized. Numerous modern medical applications benefit greatly from the implementation of machine learning (ML) models and the disruptive innovations in the entire modern health care system. It is extensively used for constructing accurate and robust statistical models from large volumes of medical data collected from a variety of sources in contemporary healthcare systems [1]. Due to privacy concerns that restrict access …

Classification Of Darknet Traffic By Application Type, Shruti Sharma

Master's Projects

The darknet is frequently exploited for illegal purposes and activities, which makes darknet traffic detection an important security topic. Previous research has focused on various classification techniques for darknet traffic using machine learning and deep learning. We extend previous work by considering the effectiveness of a wide range of machine learning and deep learning technique for the classification of darknet traffic by application type. We consider the CICDarknet2020 dataset, which has been used in many previous studies, thus enabling a direct comparison of our results to previous work. We find that XGBoost performs the best among the classifiers that we …

Algorithmic Improvements In Deep Reinforcement Learning, Norman L. Tasfi

Electronic Thesis and Dissertation Repository

Reinforcement Learning (RL) has seen exponential performance improvements over the past decade, achieving super-human performance across many domains. Deep Reinforcement Learning (DRL), the combination of RL methods with deep neural networks (DNN) as function approximators, has unlocked much of this progress. The path to generalized artificial intelligence (GAI) will depend on deep learning (DL) and RL. However, much work is required before the technology reaches anything resembling GAI. Therefore, this thesis focuses on a subset of areas within RL that require additional research to advance the field, specifically: sample efficiency, planning, and task transfer. The first area, sample efficiency, refers …

Investigating Applications Of Deep Learning For Diagnosis Of Post Traumatic Elbow Disease, Hugh James

McKelvey School of Engineering Theses & Dissertations

Traumatic events such as dislocation, breaks, and arthritis of musculoskeletal joints can cause the development of post-traumatic joint contracture (PTJC). Clinically, noninvasive techniques such as Magnetic Resonance Imaging (MRI) scans are used to analyze the disease. Such procedures require a patient to sit sedentary for long periods of time and can be expensive as well. Additionally, years of practice and experience are required for clinicians to accurately recognize the diseased anterior capsule region and make an accurate diagnosis. Manual tracing of the anterior capsule is done to help with diagnosis but is subjective and timely. As a result, there is …

Enhancing The Performance Of The Mtcnn For The Classification Of Cancer Pathology Reports: From Data Annotation To Model Deployment, Kevin De Angeli

Doctoral Dissertations

Information contained in electronic health records (EHR) combined with the latest advances in machine learning (ML) have the potential to revolutionize the medical sciences. In particular, information contained in cancer pathology reports is essential to investigate cancer trends across the country. Unfortunately, large parts of information in EHRs are stored in the form of unstructured, free-text which limit their usability and research potential. To overcome this accessibility barrier, cancer registries depend on expert personnel who read, interpret, and extract relevant information. Naturally, as the number of stored pathology reports increases every day, depending on human experts presents scalability challenges. Recently, …