Digital Commons Network^™

Detecting Anomalies In Dynamic Attributed Graphs: An Unsupervised Learning Approach, Austin Hamilton

Electronic Theses and Dissertations

Dynamic attributed graphs, which evolve over time and hold node-specific attributes, are essential in fields like social network analysis, where anomalous node detection is a growing area. Vehicular social networks (VSNs), a subset of these graphs, are ad hoc networks in which vehicles exchange data with one another and with infrastructure. In this dynamic context, identifying anomalous nodes is challenging but crucial for maintaining trust within the network. This work presents an unsupervised deep learning approach for anomalous node detection in VSNs. This model achieved an accuracy of 71% while detecting synthetic anomalies in a simulated network based on real-world …

Real-Time Network Simulations For Ml/Dl Ddos Detection Using Docker, Luis D. Garcia

Master's Theses

As the integration of artificial intelligence (AI) within cybersecurity continues to

grow, machine learning (ML) and deep learning (DL) models are increasingly used to

detect cyber attacks. However, these models are rarely evaluated in real-time attack

scenarios to see how subtle changes from the real networking environment can affect

their predictions. To address this issue, we propose a scalable, platform-independent

Docker testbed specifically designed for simulating real-time Distributed Denial of

Service (DDoS) attack scenarios that allows researchers to deploy and evaluate their

pre-trained, ML and DL detection models. Our framework is simple to configure

and can run across Intel and …

End-To-End Learning For A Low-Cost Robotics Arm, Abhishek Chothani

Theses and Dissertations

Robotic manipulation is a cornerstone of automation, with the ultimate goal of developing versatile systems capable of executing a wide range of real-world tasks autonomously. Traditional robotics approaches, while reliable and widely adopted in industrial settings, often struggle with adaptability, perception, and dynamic task execution. This thesis explores the evolution from classical robotics techniques to modern learning-based approaches, leveraging advancements in artificial intelligence to overcome these limitations.

Initially, the thesis presents a pick-and-place pipeline built using the Drake robotics framework and the KUKA iiwa robotic arm. This system employs a pseudoinverse controller for inverse kinematics to perform structured tasks like …

Heterogeneous Collaborative Robotics: Multi-Robot Navigation In Dynamic Environments, Tyler Nicholas Raettig

Theses and Dissertations

Abstract: The challenges of multi-robot navigation in dynamic environments, focusing on uncertainties in obstacle complexities, partial observation, and the transition of policies from simulations to the real world. The proposed approach utilizes a deep reinforcement learning (DRL) framework enabling a Light Detection and Ranging (LiDAR)-equipped robot to communicate with a camera-equipped robot to achieve optimal paths despite their different sensors. The key contributions include the development of a cooperative architecture for information exchange between robots, a DRL-based framework for learning navigation policies, and a training mechanism based on dynamic randomization for enhanced real-world adaptability. Experimental validation using Gazebo simulations demonstrates …

Robust Learning With Probabilistic Relaxation Using Hypothesis-Test-Based Sampling, Zilin Wang

Dissertations and Theses Collection (Open Access)

In recent years, deep learning has been a vital tool in various tasks. The performance of a neural network is usually evaluated by empirical risk minimization. However, robustness issues have gained great concern which can be fatal in safety-critical applications. Adversarial training can mitigate the issue by minimizing the loss of worst-case perturbations of data. It is effective in improving the robustness of the model, but it is too conservative, and the plain performance of the model can be unsatisfying. Probabilistic Robust Learning (PRL) empirically balances the average- and worst-case performance while the robustness of the model is not provable …

Prediction Of Pathological Complete Response To Chemotherapy For Breast Cancer Using Deep Neural Network With Uncertainty Quantification, Bowen Jing, Kai Wang, Erich Schmitz, Shanshan Tang, Yunxiang Li, You Zhang, Jing Wang

Faculty, Staff and Student Publications

Background: The I-SPY 2 trial is a national-wide, multi-institutional clinical trial designed to evaluate multiple new therapeutic drugs for high-risk breast cancer. Previous studies suggest that pathological complete response (pCR) is a viable indicator of long-term outcomes of neoadjuvant chemotherapy for high-risk breast cancer. While pCR can be assessed during surgery after the chemotherapy, early prediction of pCR before the completion of the chemotherapy may facilitate personalized treatment management to achieve an improved outcome. Notably, the acquisition of dynamic contrast-enhanced magnetic resonance (DCEMR) images at multiple time points during the I-SPY 2 trial opens up the possibility of achieving early …

Deep Learning Framework For Inverse Problems In Computational Imaging: A Lensless Imaging And Super-Resolution Magnetic Resonance Imaging Case, Arpan Poudel

Graduate Theses and Dissertations

Inverse problems in computer vision involve reconstructing an original scene or image from incomplete, noisy, or indirect measurements. These problems are critical in tasks such as image denoising, deblurring, super-resolution, and lensless imaging, where the goal is to recover high-quality images from degraded or partial measurements. This thesis introduces novel approaches to address two specific real-world inverse problems: (1) image super-resolution in medical imaging and (2) lensless image reconstruction . In the first part of this work, we tackle the problem of image super-resolution in Magnetic Resonance Imaging (MRI). High-resolution MRI scans are often limited by hardware constraints, patient movement, …

Analyzing And Extending Machine Learning Frameworks On High Risk Domains, Chengbin Hu

USF Tampa Graduate Theses and Dissertations

Machine learning (ML) has become a transformative force in high-risk domains such as genomics and cybersecurity, where accurate predictions and robust defenses are essential. This dissertation advances ML frameworks in these areas by developing methods to enhance predictive power in health applications and assess vulnerabilities in machine learning systems.

In the genomics field, the work addresses challenges in Non-Invasive Prenatal Testing (NIPT) of monogenic disorders by proposing a deep learning model that reconstructs the fetal genome using maternal plasma cell-free DNA (cfDNA) and parental whole-genome sequencing (WGS) data. This model achieves high accuracy in single nucleotide variation (SNV) prediction, surpassing …

Ai-Enabled Methodological Frameworks To Enhance The Robustness Of Machine Learning Models In Various Domains And Dynamic Environments, Mohammad Noroozi

USF Tampa Graduate Theses and Dissertations

This dissertation explores novel approaches to address complex challenges in various domains, focusing on ensuring that machine learning (ML) models can reliably detect both the data they are trained on and unseen data. Reliable detection of unfamiliar inputs is critical for deploying models in real-world applications, where performance can degrade when faced with novel or unexpected information. Each domain addressed in this work introduces a set of contributions aimed at enhancing model performance, broadening generalization through diverse training data, and improving robustness against adversarial attacks. Three sets of contributions are presented, each encapsulated in a chapter.

One of the contributions …

Neutrosophic Logic-Based Crop Yield Prediction And Risk Assessment Using Least Squares Regression, M. Srikanth, R.N.V. Jagan Mohan, M. Chandra Naik

Neutrosophic Systems with Applications

Agriculture faces significant challenges due to climate change and unpredictable environmental factors, which impact crop yields and threaten food security. This study proposes a novel approach to crop yield prediction and risk assessment using neutrosophic logic and least squares regression. By integrating these methods, we aim to improve accuracy in predicting crop losses under uncertain conditions. The model classifies crops based on profitability and environmental risks, utilizing the independence test to evaluate the relationships between crop attributes. Our approach leverages deep learning techniques, such as restricted Boltzmann machines (RBM), to enhance the analysis of crop data and provide farmers with …

Neutrosophic Logic-Based Crop Yield Prediction And Risk Assessment Using Least Squares Regression, M. Srikanth, R.N.V. Jagan Mohan, M. Chandra Naik

Neutrosophic Systems with Applications

Agriculture faces significant challenges due to climate change and unpredictable environmental factors, which impact crop yields and threaten food security. This study proposes a novel approach to crop yield prediction and risk assessment using neutrosophic logic and least squares regression. By integrating these methods, we aim to improve accuracy in predicting crop losses under uncertain conditions. The model classifies crops based on profitability and environmental risks, utilizing the independence test to evaluate the relationships between crop attributes. Our approach leverages deep learning techniques, such as restricted Boltzmann machines (RBM), to enhance the analysis of crop data and provide farmers with …

Sibling Discrimination Using Linear Fusion On Deep Learning Face Recognition Models, Rita Goel, Maida Alamgir, Haroon Wahab, Maria Alamgir, Irfan Mehmood, Hassan Ugail, Amit Sinha

Journal of Informatics and Web Engineering

Facial recognition technology has revolutionised human identification, providing a non-invasive alternative to traditional biometric methods like signatures and voice recognition. The integration of deep learning has significantly enhanced the accuracy and adaptability of these systems, now widely used in criminal identification, access control, and security. Initial research focused on recognising full-frontal facial features, but recent advancements have tackled the challenge of identifying partially visible faces, a scenario that often reduces recognition accuracy. This study aims to identify siblings based on facial features, particularly in cases where only partial features like eyes, nose, or mouth are visible. Utilising advanced deep learning …

Conditional Deployable Biometrics: Matching Periocular And Face In Various Settings, Jihyeon Kim, Tiong-Sik Ng, Andrew Beng Jin Teoh

Journal of Informatics and Web Engineering

In this paper, we introduce the concept of Conditional Deployable Biometrics (CDB), designed to deliver consistent performance across various biometric matching scenarios, including intra-modal, multimodal, and cross-modal applications. The CDB framework provides a versatile and deployable biometric authentication system that ensures reliable matching regardless of the biometric modality being used. To realize this framework, we have developed CDB-Net, a specialized deep neural network tailored for handling both periocular and face biometric modalities. CDB-Net is engineered to handle the unique challenges associated with these different modalities while maintaining high accuracy and robustness. Our extensive experimentation with CDB-Net across five diverse and …

Tumor Detection In Mri Data Using Deep Learning Techniques For Image Classification And Semantic Segmentation, Muhammad Abid, Muhammad Shahid

Sustainable Machine Intelligence Journal

This study aims to apply image classification and semantic segmentation methods using deep learning for accurate and efficient tumor detection in brain MRI data. It looks at ways to get over issues including interpretability of the model and a lack of annotated data. A pre-trained VGG19 model and a basic convolutional neural network (CNN) were trained using a public dataset of 273 brain MRI images for the purpose of image classification. Techniques for regularization, transfer learning, and data augmentation were used. A U-Net architecture was trained using manually created masks from the same dataset for semantic segmentation, with several training …

Clinical Outcome-Guided Deep Temporal Clustering For Disease Progression Subtyping, Dulin Wang, Xiaotian Ma, Paul E Schulz, Xiaoqian Jiang, Yejin Kim

Faculty, Staff and Student Publications

Objective: Complex diseases exhibit heterogeneous progression patterns, necessitating effective capture and clustering of longitudinal changes to identify disease subtypes for personalized treatments. However, existing studies often fail to design clustering-specific representations or neglect clinical outcomes, thereby limiting the interpretability and clinical utility.

Method: We design a unified framework for subtyping longitudinal progressive diseases. We focus on effectively integrating all data from disease progressions and improving patient representation for downstream clustering. Specifically, we propose a clinical Outcome-Guided Deep Temporal Clustering (OG-DTC) that generates representations informed by clustering and clinical outcomes. A GRU-based seq2seq architecture captures the temporal dynamics, and the model …

"Deep Learning For Microscope Image Denoising", Nasreen Buhn, Sriya Adunur, Guy Hagen, Jonathan Ventura

College of Engineering Summer Undergraduate Research Program

In order to avoid damaging live cells, optical microscope imaging must be conducted under low-excitation light intensity and/or short exposure times, resulting in low signal-to-noise ratios (SNR). Deep learning methods offer an effective solution for removing microscope noise, utilizing algorithms that are able to reconstruct finer features in low SNR images. This research explores the denoising capability of several deep learning methods based on PSNR and SSIM. Tested methods include traditional approaches (BMED), supervised learning (CARE and Restormer), and unsupervised methods (Noise2Fast, N2V, SSD-Unsupervised, and SASSID). The Restormer model, which employs an encoder-decoder transformer architecture and progressive learning, stood out …

Multi-Modal Segmentation With Missing Image Data For Automatic Delineation Of Gross Tumor Volumes In Head And Neck Cancers, Yao Zhao, Xin Wang, Jack Phan, Xinru Chen, Anna Lee, Cenji Yu, Kai Huang, Laurence E Court, Tinsu Pan, He Wang, Kareem Abdul Wahid, Abdalah S R Mohamed, Mohamed Naser, Clifton D Fuller, Jinzhong Yang

Faculty, Staff and Student Publications

Background: Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of targets. Multi-modality images, including computed tomography (CT) and positron emission tomography (PET), are used in the routine clinic to assist radiation oncologists for accurate GTV delineation. However, the availability of PET imaging may not always be guaranteed.

Purpose: To develop a deep learning segmentation framework for automated GTV delineation of HN cancers using a combination of PET/CT images, while addressing the challenge of missing PET data.

Methods: Two datasets were included for this study: Dataset I: 524 (training) …

Osteoarthritis Grading A Synthesized Magnetic Resonance Images Technique, Qiu Ruiyun, Siti Khatijah Nor Abdul Rahim, Nursuriati Jamil, Raseeda Hamzah, Xiaoling Fu

Malaysian Journal of Computing (MJoC)

Osteoarthritis (OA) in the knee is a major cause of decreased activity and physical limitations among older people. Identifying and treating knee osteoarthritis in its early stages can help patients delay the progression of the condition. Currently, early detection of knee osteoarthritis involves the use of X-ray images and assessment using the Kellgren-Lawrence (KL) grading system. Doctors' evaluations can be subjective and may differ among different doctors. Similar to a computer systems analyst, the automatic knee OA grading and diagnosis can be a valuable tool for doctors, enabling them to streamline their workload and provide more efficient care. An innovative …

Predicting Covid-19 Trends A Deep Dive Into Time Dependent Sirsd With Deep Learning Technique, Abdul Basit, Jasni Mohamad Zain, Abdul Kadir Jumaat, Nur’Izzati Hamdan, Hafiza Zoya Mojahid

Malaysian Journal of Computing (MJoC)

The COVID-19 pandemic, also known as Coronavirus Disease 2019, has affected over 700 million people globally, resulting in approximately 7 million deaths. Research has proposed multiple mathematical models to institute a disease transmission framework and predict the disease growth. Most of the existing mathematical disease growth prediction models are less effective due to the exclusion of the re-susceptible scenarios and overlooks their timedependent properties, which change continuously during the viral transmission process. Another popular prediction technique is deep learning approaches. However, existing methods often fail to accurately capture the dynamic trends of epidemics during their spreading phases in short-term and …

Retrofitting A Legacy Cutlery Washing Machine Using Computer Vision, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

Industry 4.0, the digitalization of manufacturing promises to lead to lowered cost, efficient processes and even discovery of new business models. However, many of the enterprises have huge investments in legacy machines which are not 'smart'. In this study, we thus designed a cost-efficient solution to retrofit a legacy conveyor belt-based cutlery washing machine with a commodity web camera. We then applied computer vision (using both traditional image processing and deep learning techniques) to infer the speed and utilization of the machine. We detailed the algorithms that we designed for computing both speed andutilization. With the existing operational constraints of …

Metadegron: Multimodal Feature-Integrated Protein Language Model For Predicting E3 Ligase Targeted Degrons, Mengqiu Zheng, Shaofeng Lin, Kunqi Chen, Ruifeng Hu, Liming Wang, Zhongming Zhao, Haodong Xu

Faculty, Staff and Student Publications

Protein degradation through the ubiquitin proteasome system at the spatial and temporal regulation is essential for many cellular processes. E3 ligases and degradation signals (degrons), the sequences they recognize in the target proteins, are key parts of the ubiquitin-mediated proteolysis, and their interactions determine the degradation specificity and maintain cellular homeostasis. To date, only a limited number of targeted degron instances have been identified, and their properties are not yet fully characterized. To tackle on this challenge, here we develop a novel deep-learning framework, namely MetaDegron, for predicting E3 ligase targeted degron by integrating the protein language model and comprehensive …

Training Robust T1-Weighted Magnetic Resonance Imaging Liver Segmentation Models Using Ensembles Of Datasets With Different Contrast Protocols And Liver Disease Etiologies, Nihil Patel, Adrian Celaya, Mohamed Eltaher, Rachel Glenn, Kari Brewer Savannah, Kristy K Brock, Jessica I Sanchez, Tiffany L Calderone, Darrel Cleere, Ahmed Elsaiey, Matthew Cagley, Nakul Gupta, David Victor, Laura Beretta, Eugene J Koay, Tucker J Netherton, David T Fuentes

Faculty, Staff and Student Publications

Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. This manuscript develops a generalizable deep learning model to segment the liver on T1-weighted MR images. In particular, three distinct deep learning architectures (nnUNet, PocketNet, Swin UNETR) were considered using data gathered from six geographically different institutions. A total of 819 T1-weighted MR images were gathered from both public and internal sources. Our experiments compared each architecture's testing performance when trained both …

Deepfake Detection In Video And Audio Clips: A Comprehensive Survey And Analysis, Wurood A. Jbara, Noor Al-Huda K. Hussein, Jamila H. Soud

Mesopotamian Journal of CyberSecurity

Deepfake (DF) technology has emerged as a major concern due to its potential for misuse, including privacy violations, misinformation, and threats to the integrity of digital media. While significant progress has been made in developing deep learning (DL) algorithms to detect DFs, effectively distinguishing between real and manipulated content remains a challenge due to the rapid evolution of DF generation techniques. This study aims to address two key issues: the need for a comprehensive review of current DF detection methods and the challenge of achieving high detection accuracy with low computational cost. We conducted a systematic literature review to evaluate …

Developing Empathetic Ai: Exploring The Potential Of Artificial Intelligence To Understand And Simulate Family Dynamics And Cultural Identity, Emily Barnes, James Hutson

Faculty Scholarship

The rapid advancement of Artificial Intelligence (AI) has significantly impacted various domains. Yet, the exploration of AI's potential to develop a deep understanding of family culture and identity remains underexplored. This study introduces the concept of "a love of grandma and apple pie" to symbolize the potential of various AI to internalize and appreciate familial relationships, cultural traditions, and personal identity. The proposed study would investigate how an advanced deep learning model, trained on diverse unstructured datasets—including multimedia data from 100 families-could learn and reflect human-like emotions, values, and cultural understanding. Utilizing Convolutional Neural Networks (CNNs) for visual data processing …

An Adaptive Hybrid Deep Learning Architecture For Providing Guaranteed Qos In 5g Cellular Networks, Rajilal Mv Ms

Theses and Dissertations

Wireless network systems must have effective resource allocation, particularly in the context of 5G networks when flexibility is needed to meet a range of network requirements. Resource allocation is essential in cellular network contexts to guarantee equitable access to customers, partners, and cellular service users. Since resource distribution determines network performance, it offers significant advantages when executed well. One of the biggest issues with 5G technology is resource allocation, particularly when it comes to the Quality of Service (QoS) for various applications. Resources in wireless networks include items like channels, power, and spectrum; these must all be apportioned according to …

The Impact Of Model Variations On The Robustness Of Deep Learning Models In Adversarial Settings, Firuz Juraev, Mohammed Abuhamad, Simon S. Woo, George K. Thiruvathukal, Tamer Abuhmed

Computer Science: Faculty Publications and Other Works

Rapid advancements of deep learning are accelerating adoption in a wide variety of applications, including safety-critical applications such as self-driving vehicles, drones, robots, and surveillance systems. These advancements include applying variations of sophisticated techniques that improve the performance of models. However, such models are not immune to adversarial manipulations, which can cause the system to misbehave and remain unnoticed by experts. The frequency of modifications to existing deep learning models necessitates thorough analysis to determine the impact on models’ robustness. In this work, we present an experimental evaluation of the effects of model modifications on deep learning model robustness using …

Hybridizing Mechanistic Modeling And Deep Learning For Personalized Survival Prediction After Immune Checkpoint Inhibitor Immunotherapy, Joseph D Butner, Prashant Dogra, Caroline Chung, Eugene J Koay, James W Welsh, David S Hong, Vittorio Cristini, Zhihui Wang

Faculty, Staff and Student Publications

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, …

Design And Development Of A Clinical Decision Support System For The Diagnosis Of Parkinson’S Disease Using Artificial Intelligence, Saravanan S

Theses and Dissertations

Parkinson’s disease (PD) is a degenerative neurological condition marked by motor symptoms like tremors, bradykinesia, and stiffness. It is observed that early and precise diagnosis of this disease is crucial, as that will have a significant impact on effective disease management and intervention. This thesis explores the technical feasibility of applying AI techniques to recognize patterns from spiral and wave drawings, which are usually a unique signature type for PD patients.

The focus of the work is to diagnose the disease through novel deep transfer learning techniques, to diagnose the severity of the disease, and also to develop effective model …

Development And Optimization Of A 1-Dimensional Convolutional Neural Network-Based Keyword Spotting Model For Fpga Acceleration, Trysten E. Dembeck

Masters Theses

Spoken Keyword Spotting (KWS) has steadily remained one of the most studied and implemented technologies in human-facing artificially intelligent systems and has enabled them to detect specific keywords in utterances. Modern machine learning models, such as the variants of deep neural networks, have significantly improved the performance and accuracy of these systems over other rudimentary techniques. However, they often demand substantial computational resources, use large parameter spaces, and introduce latencies that limit their real-time applicability and offline use. These speed and memory requirements have become a tremendous problem where faster and more efficient KWS methods dominate and better meet industry …

Parameter Estimation For Stroke Patients Using Brain Ct Perfusion Imaging With Deep Temporal Convolutional Neural Network, Shake Ibna Abir

Masters Theses & Specialist Projects

Acute ischemic stroke, caused by cerebral artery blockage, is a leading cause of long-term disability and mortality. Effective management relies on accurate, timely assessments from neuroimaging data. Computed tomography perfusion (CTP) imaging is crucial in evaluating stroke patients, offering detailed maps of cerebral perfusion to identify irreversibly damaged tissue and at-risk areas. This detailed assessment is essential for informed therapeutic decisions.

Key perfusion parameters derived from CTP imaging, including cerebral blood volume (CBV), cerebral blood flow (CBF), time to peak (TTP), and mean transit time (MTT), are crucial for understanding the extent and nature of cerebral ischemia, providing valuable insights …