Digital Commons Network^™

A New Deepfake Detection Method With No-Reference Image Quality Assessment To Resist Image Degradation, Jiajun Jiang, Wen-Chao Yang, Chung-Hao Chen, Timothy Young

Electrical & Computer Engineering Faculty Publications

Deepfake technology, which utilizes advanced AI models such as Generative Adversarial Networks (GANs), has led to the proliferation of highly convincing manipulated media, posing significant challenges for detection. Existing detection methods often struggle with the low-quality or compressed press, which is prevalent on social media platforms. This paper proposes a novel Deepfake detection framework that leverages No-Reference Image Quality Assessment (NRIQA) techniques, specifically, BRISQUE, NIQE, and PIQUE, to extract quality-related features from facial images. These features are then classified using a Support Vector Machine (SVM) with various kernel functions. We evaluate our method under both intra-dataset and cross-dataset settings. For …

A Fast Framework For Generating Radioactive Mixture Spectra And Its Application To Remote High-Performance Mixture Identification, Chiman Kwan, Bulent Ayhan, Adam Stavola, Kazi Aminul Islam, Hongfang Zhang, Jiang Li

Electrical & Computer Engineering Faculty Publications

Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.

Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade

Browse all Theses and Dissertations

Sepsis is a leading cause of pediatric mortality, claiming more lives in the United States annually than all childhood cancers combined. Early identification in Emergency Departments (EDs) remains challenging, as the current Phoenix criteria establishes an updated international consensus definition for sepsis, however is not designed for use as a screening tool. This study aimed to develop predictive models identifying pediatric patients at risk of sepsis within 24 hours of admission. Multiple tree-based and deep learning models were trained utilizing clinical and laboratory data from the initial four hours of presentation. Both the LightGBM and LSTM architectures demonstrated superior performance, …

Dataset Generation For Routing Policy Study In Ad Hoc Wireless Networks, Vishnu Vishnu Priya

Browse all Theses and Dissertations

Ad Hoc wireless networks, with their decentralized architecture and dynamic topology, present challenges in reliable and energy-efficient routing. While machine learning (ML) and reinforcement learning (RL) offer promising solutions, progress is limited by the lack of realistic, high-fidelity datasets. This research introduces a simulation-based framework for generating four diverse datasets representing combinations of node mobility (mobile vs. static) and spatial distribution (random vs. clustered). Each dataset captures critical metrics such as Signal-to-Interference-plus-Noise Ratio (SINR), bottleneck rate, and power consumption across multi-hop paths. A lookahead-based greedy routing algorithm with scenario-aware power control is implemented to emulate practical behavior. Supervised ML models, …

Novel Ai/Ml-Based Frameworks For Protein Conformation Selection In Drug Discovery Applications, Shivangi Gupta

Dissertations

Drug development is a lengthy, expensive process with a high failure rate. The urgency brought on by the COVID-19 pandemic has accelerated the integration of artificial intelligence (AI) and machine learning (ML) to enhance drug discovery by increasing target specificity, reducing toxicity, and optimizing formulation strategies. Building on this momentum, this dissertation introduces novel AI/ML-driven frameworks for protein conformation selection and classification, addressing critical challenges in modern drug discovery. However, designing such a novel AI/ML data-driven framework is a difficult task because most real-world biomedical datasets suffer from class imbalance issues, which can significantly skew AI/ML model training, resulting in …

A Secure Ml-Assisted Framework For Resilient And Efficient Prediction Of Physiotherapy Sequence In Bilateral Carpal Tunnel Syndrome, Pratik Pandurang Kharat

Browse all Theses and Dissertations

Bilateral idiopathic carpal tunnel syndrome (CTS) is a neuromuscular disorder characterized by compression of the median nerve at both wrists, leading to symptoms such as pain, numbness, tingling, and muscle weakness. Unlike unilateral cases, bilateral idiopathic CTS presents distinct therapeutic challenges due to the simultaneous involvement of both hands and the lack of an identifiable underlying cause. This study explores the application of machine learning techniques to predict the optimal sequence of physiotherapeutic interventions Stretching followed by Myofascial Mobilization (S/M) or the reverse (M/S) in female patients with bilateral idiopathic CTS and right hand dominance. Data were drawn from a …

Learning Under Data Scarcity: Reasoning And Negative Distillation For Texts And Graphs, Calvin T. Greenewald

Browse all Theses and Dissertations

Modern machine learning (ML) models rely on large amounts of high-quality labeled data to achieve optimal performance. However, in many real-world domains, such as cyber security, acquiring sufficient labeled data is often infeasible due to cost, privacy concerns, and the rapid evolution of underlying phenomena. This challenge underscores the importance of learning under data scarcity. This thesis addresses this challenge by proposing distinct, modality-specific techniques for text and graph domains, which allow models to generalize effectively with minimal data. For text classification task, we incorporate distilled rationales from large language models and adversarial perturbations into the input space to improve …

Methods In Statistics, Machine Learning, And Deep Learning For Combining Multi-Omics Dataset, Md Mutasim Billah

Dissertations, Master's Theses and Master's Reports

Transcriptome-wide association studies (TWAS) have emerged as a powerful strategy to bridge genome-wide association studies (GWAS) with gene regulatory mechanisms by integrating genotypic data with gene expression data. While early TWAS methods typically rely on linear models and single-tissue expression references, recent advances underscore the need for flexible, multi-tissue approaches that can capture heterogeneous regulatory architectures and tissue-specific expression patterns. This dissertation introduces a three‑part research project that advances multi‑tissue transcriptome‑wide association studies (TWAS) along complementary axes of methodology, statistical power, and modelling flexibility.

In chapter One, TWAS‑CTL introduces a two‑stage cross‑tissue learner that trains any user‑chosen single‑tissue imputers (STLs) …

Mortality Prediction After Major Surgery In A Mixed Population Through Machine Learning: A Multi-Objective Symbolic Regression Approach, Pietro Arina, Davide Ferrari, Nicholas Tetlow, Amy Dewar, Robert Stephens, Daniel Martin, Ramani Moonesinghe, Vasa Curcin, Mervyn Singer, John Whittle, Evangelos B Mazomenos

Peninsula Medical School

INTRODUCTION: Understanding 1-year mortality following major surgery offers valuable insights into patient outcomes and the quality of peri-operative care. Few models exist that predict 1-year mortality accurately. This study aimed to develop a predictive model for 1-year mortality in patients undergoing complex non-cardiac surgery using a novel machine-learning technique called multi-objective symbolic regression.

METHODS: A single-institution database of patients undergoing major elective surgery with previous cardiopulmonary exercise testing was divided into three datasets: pre-operative clinical data; cardiorespiratory and physiological data; and combined. A multi-objective symbolic regression model was developed and compared against existing models. Model performance was evaluated using the …

Transformer-Based Genomic Classification And Simulation Of Heat-Responsive Genes In Citrus Limon, Ali Fahem Neamah, Zahraa A. Abdalkareem, Mostafa A. Mohammed, Jasim Ibrahim Ahmed, Phaklen Ehkan

Mesopotamian Journal of Big Data

Iraqi lemon trees (Citrus limon), vital for regional agriculture and food security, face intensifying threats from extreme heat caused by ongoing climate change in Iraq. Native cultivars often lack thermotolerance due to low expression of protective heat-response genes. This study addresses this critical challenge by developing an AI-assisted framework that integrates real RNA-Seq data, Transformer-based deep learning, and explainable AI to classify and simulate the function of genes associated with heat stress adaptation. The primary objective is to identify key thermotolerance genes and model their biological impact, with a specific focus on indigenous citrus varieties. Using a customized transformer architecture …

Data Mining And Machine Learning-Based Healthcare Monitoring In Cloud-Iot, Sarah Amer, Rania Hazim, Wassan Kader

Mesopotamian Journal of CyberSecurity

Healthcare monitoring Cloud-IoT systems use data mining and machine learning methods to analyse patient data in real-time from linked devices. By offering insights for the early diagnosis of anomalies and individualized treatment suggestions, this strategy improves healthcare management. In this research first the Collect and Load the Clevant Heart Disease Dataset for Data Collection Process. Next, preprocess the loaded data using the Synthetic Minority Oversampling Technique (SMOTE), and then the feature extraction process is done using the Principal Component Analysis (PCA) Method. In this case, the characteristic must be extracted by feeding a specific column. The classification procedure is then …

Department Of Computer Engineering, University Of Diyala, 32001 Diyala, Iraq., Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel

Mesopotamian Journal of CyberSecurity

Android devices are rapidly being used, which makes it easy for the malware threat to rise to higher levels. This ever-growing problem has prompted the need to enhance detection systems as far as these devices are concerned. Standard techniques of machine learning (ML) are sufficient from the point of view of their speed for searching patterns and behaviors of contemporary malware; however, it is more important to have effectively enhanced methods. The purpose of this paper is to expand the utilization of Android malware identification via artificial neural networks (ANNs) and compare its efficiency with that of other ML methods. …

The Integration Of Artificial Intelligence And Ontologies: Transformations In Knowledge Representation And Application, Grazia Serratore, Julaine Clunis

STEMPS Faculty Publications

Artificial Intelligence (AI) is reshaping the landscape of knowledge representation. There is an increasingly strong bidirectional relationship, between AI techniques and ontologies. AI techniques revolutionized traditional, manual ontology development and contribute to automated ontology construction, while ontologies enhance the performance of AI systems and their semantic accuracy. Through a comprehensive review of current literature, this paper aims to examine: i) how Machine Learning (ML) techniques contribute to the automated construction, refinement, and validation of ontologies; ii) the most widely used and effective ML approaches for ontology construction; iii) how domain-specific requirements influence the selection and adaptation of AI techniques for …

Classification Of Variable Stars Using Convolutional Neural Network, Abhina Premachandran Bindu

Dissertations and Theses

This research focuses on developing Convolutional Neural Networks (CNNs), for the process of classifying and identifying variable stars through the analysis of unprocessed light curves from Transiting Exoplanet Survey Satellite (TESS). As astronomical data is becoming increasingly complex, and as advanced missions deploy sophisticated instruments for data collection, both the quality and quantity of the data are improving at a rapid pace. This has created an urgent need to automate the analysis process using efficient and effective methods, such as those based on machine learning. While previous research has explored machine learning approaches, there has been limited focus on implementing …

Perceptions Of Stem Education And Artificial Intelligence: A Twitter (X) Sentiment Analysis, Demetrice Smith-Mutegi, Yoseph Mamo, Jinhee Kim, Helen Crompton, Matthew Mcconnell

Teaching & Learning Faculty Publications

Background, context, and purpose of the study: Artificial intelligence (AI) is becoming increasingly prevalent in science, technology, engineering, and mathematics (STEM) education, holding promising potential for supporting the design and implementation of quality STEM education. However, there is a lack of data-based research studying the diverse perceptions of AI in STEM education as conveyed on social media, the factors that influence those perceptions, or the change in those perceptions over time among public audiences. Results, the main findings: The purpose of this study was to examine public perceptions of AI in STEM education by analyzing X posts (Tweets) between 04/28/2020 …

Machine Learning Models Leveraging Patient-Similarity And Clinical Temporality For Disease Prognoses, Ahmad F. Al Musawi

Theses and Dissertations

Electronic Health Records (EHRs) constitute a comprehensive and high-dimensional repository of clinical data, encompassing a wide array of patient-level information such as diagnoses, procedures, medications, laboratory results, and unstructured clinical narratives. These data hold immense potential for advancing predictive modeling in healthcare, including tasks such as disease progression modeling, hospital readmission prediction, and length of stay (LoS) estimation. However, the intrinsic complexity of EHR data—manifested in its heterogeneity, sparsity, and temporal dynamics—poses significant analytical challenges that limit the generalizability and interpretability of conventional machine learning models. Recent methodological advancements in deep learning and graph-based learning, particularly Graph Neural Networks (GNNs), …

How Artificial Intelligence Will Shape Securities Regulation, Gabriel Rauterberg

Other Publications

How will the increasing prevalence and sophistication of artificial intelligence (AI) change the doctrine and practice of securities law? My main thesis is that it will push securities regulation toward a more systems-oriented approach. This approach will replace securities law's emphasis, in areas like manipulation, on forms of enforcement targeted at specific individuals and accompanied by punitive sanctions with a greater focus on ex ante rules designed to shape an ecology of actors and information.

Delivery Of Medical Supplies To Remote Locations Via Unmanned Aerial Vehicles: Approaches, Challenges, And Solutions, Meshari Aljohani, Ravi Mukkamala, Stephanie Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming more important in improving healthcare logistics, in particular due to their cost effectiveness, minimized risk, and versatile operational capabilities. This study explores the deployment of autonomous UAVs to deliver medical supplies to remote areas. Advances in ledger technology, smart contracts, and machine learning have transformed tasks previously managed by human teams or manually controlled UAVs into fully autonomous missions. We present a comprehensive analysis of the challenges and initial solutions vital for the effective use of autonomous UAVs in the delivery of medical supplies. In addition, we propose a machine-learning model to optimize UAV …

Machine Learning Predicting Acute Pain And Opioid Dose In Radiation Treated Oropharyngeal Cancer Patients, Vivian Salama, Laia Humbert-Vidan, Brandon Godinich, Kareem A Wahid, Dina M Elhabashy, Mohamed A Naser, Renjie He, Abdallah S R Mohamed, Ariana J Sahli, Katherine A Hutcheson, Gary Brandon Gunn, David I Rosenthal, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Introduction: Acute pain is common among oral cavity/oropharyngeal cancer (OCC/OPC) patients undergoing radiation therapy (RT). This study aimed to predict acute pain severity and opioid doses during RT using machine learning (ML), facilitating risk-stratification models for clinical trials.

Methods: A retrospective study examined 900 OCC/OPC patients treated with RT during 2017-2023. Pain intensity was assessed using NRS (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed using combined pain intensity and total MEDD. ML predictive models were developed and validated, including Logistic Regression (LR), Support Vector Machine (SVM), Random …

Bridging Law And Machine Learning: A Cybersecure Model For Classifying Digital Real Estate Contracts In The Metaverse, Faris Kamil Mihna, Hazim Akram Sallal, Lobna Abdalhusen Al-Seedi, Hasan Ali Al- Tameemi, Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel, Dheyaa A. Mohammed

Mesopotamian Journal of Big Data

The metaverse indicates an ever-evolving digital ecosystem where virtual real estate has now become an asset class. These properties, subject to smart contracts on the blockchain and represent as non-fungible tokens (NFTs), gives rise to new legal and cyber issues due to the decentralized and dematerialized nature of these digital assets .This paper proposes a machine learning approach to classify the digital real estate contracts into Ownership and Lease contracts. The study utilizes a dataset of one thousand digital real estate contracts collected from platforms such as Decentraland and The Sandbox. The dataset also included attributes such as plot size, …

Ensemble Machine Learning Approaches For Bathymetry Estimation In Multi-Spectral Images, Kazi A. Islam, Omar Abdul-Hassan, Hongfang Zhang, Victoria Hill, Blake Schaeffer, Richard Zimmerman, Jiang Li

OES Faculty Publications

Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named CatBoostOpt, to estimate bathymetry based on high-resolution WorldView-2 (WV-2) multi-spectral optical satellite images. CatBoostOpt was demonstrated across the Florida Big Bend coastline, where the model learned correlations between in situ sound Navigation and Ranging (Sonar) bathymetry measurements and the corresponding multi-spectral reflectance values in WV-2 images to map bathymetry. We evaluated three different feature transformations as inputs …

Organic Geochemical Evidence For Life In Archean Rocks Identified By Pyrolysis-Gc-Ms And Supervised Machine Learning, Michael L. Wong, Anirudh Prabhu, Conel O'D. Alexander, H. James Cleaves Ii, George D. Cody, Grethe Hystad, Marko Bermanec, Wouter Bleeker, C. Kevin Boyce, Andrea Corpolongo, Andrew D. Czaja, Souvik Das, Robert R. Gaines, Daniel D. Gregory, John A. Jaszczak, Emmanuelle J. Javaux, Jaganmoy Jodder, Andrew H. Knoll, Martin Van Kranendonk, Katie M. Maloney, Nora Noffke, Robert Rainbird, Emersyn Slaughter, Eva E. Stüeken, Roger E. Summons, Frances Westall, Jasmina Wiemann, Shuhai Xiao, Robert M. Hazen

OES Faculty Publications

Throughout Earth’s history, organic molecules from both abiogenic and biogenic sources have been buried in sedimentary rocks. Most of these organic molecules have been significantly altered by geologic processes through deep time. Nonetheless, the nature and distribution of those ancient fragmentary organic remains have the potential to reveal diagnostic biomolecular information after billions of years of burial. Here, we analyzed 406 fossil, modern biological, meteoritic, and synthetic samples using pyrolysis gas chromatography and mass spectrometry. We explored these analytical data via supervised machine-learning methods to discriminate samples of biogenic vs. abiogenic origin, plant vs. animal phylogenetic affinity, and photosynthetic vs. …

Nonlinear Dynamics And Data Driven Modeling, Mattan Tseng

Theses

The research areas of dynamics and data-driven modeling overlap in existing literature. Some research uses dynamical systems as a test bed while developing new modeling methods. Other papers attempt to use data-driven modeling as a tool to better understand, or potentially predict the behavior of a dynamical system. The primary goal of this thesis is to further the symbiotic relationship between these two research areas. Firstly, this paper provides a concrete example of how a basis function decomposition of the Lorenz system can provide a quantitative performance metric for data-driven modeling methods. Secondly, a novel application of an existing modeling …

Application Of Machine Learning To Predict Ultrasound Wave Propagation In Biphasic Fluid–Solid Media, Jahnavi Dandamudi

Theses

The objective of this thesis is to investigate the application of Machine Learning (ML) algorithms to predict ultrasound wave propagation in the cartilage tissue of the knee joint. The learning data necessary for the ML algorithm has been generated through finite-element method (FEM)-based simulations for solving the Biot theory equations governing the propagation of continuous ultrasound through the cartilage. Specifically, we computed the ultrasound-induced dilatations and displacements in the microscale cartilage that is represented as consisting of four zones, namely the chondrocyte cell and its nucleus, the pericellular matrix (PCM) that forms a layer around the chondrocyte, and the extracellular …

A Mutually Exclusive 3d Convolution Based Time Dilation Framework For Future Frame Prediction In Video Streams Of Natural Events, Benjamin Robinson

Theses

In this thesis, a framework for predicting future frames from videos of naturally occurring processes will be presented. This is achieved by learning the underlying physical laws of the natural process through the exploitation of spatiotemporal features from the frames of the video. Our framework defines windows containing frames at varying time intervals, which we call dilations, and uses them to predict future frames using a 3D convolutional neural network (CNN). The network architecture accepts six time-dilated windows of input video frames and computes spatiotemporal features for each window maintaining the exclusion of the resulting features across three phases of …

Predicting Mental Health Disparities Using Machine Learning For African Americans In Southeastern Virginia, Ismail El Moudden, Michael C. Bittner, Matvey V. Karpov, Isaac O. Osunmakinde, Akosua Acheamponmaa, Breshell J. Nevels, Mamadou T. Mbaye, Tonya L. Fields, Karthiga Jordan, Messaoud Bahoura

Department of Obstetrics & Gynecology Faculty Publications

This study examined mental health disparities among African Americans using AI and machine learning for outcome prediction. Analyzing data from African American adults (18–85) in Southeastern Virginia (2016–2020), we found Mood Affective Disorders were most prevalent (41.66%), followed by Schizophrenia Spectrum and Other Psychotic Disorders. Females predominantly experienced mood disorders, with patient ages typically ranging from late thirties to mid-forties. Medicare coverage was notably high among schizophrenia patients, while emergency admissions and comorbidities significantly impacted total healthcare charges. Machine learning models, including gradient boosting, random forest, neural networks, logistic regression, and Naive Bayes, were validated through 100 repeated 5-fold cross-validations. …

Generating Synthetic Malware Samples Using Generative Ai, Tiffany Bao, Kylie Trousil, Quang Duy Tran, Fabio Di Troia, Younghee Park

Faculty Research, Scholarly, and Creative Activity

Malware attacks have a significant negative impact on organizations of varied scales in the field of cybersecurity. Recently, malware researchers have increasingly turned to machine learning techniques to combat sophisticated obfuscation methods used in malware. However, collecting a diverse set of malware samples with various obfuscation techniques is challenging and often takes years, especially for newly developed malware. This issue is further compounded by a well-known limitation of machine learning models: their poor performance when training data is scarce. In this paper, we propose a new system for generating synthetic malware samples to augment imbalanced malware dataset. Our approach decomposes …

Nanotube Spectral Fingerprinting And Machine Learning For Optimized Bioimaging/Sensing And Disease Detection Applications In Als, Rodrigo Monroy Lopez

Open Access Master's Theses

Single-walled carbon nanotubes (SWCNTs) possess unique physicochemical and optical properties that make them ideal candidates for biomedical imaging, biosensing, and disease diagnostics. This thesis explores the potential of SWCNT-based spectral fingerprinting combined with ML (Machine Learning) algorithms to optimize bioimaging, disease detection and prediction, with a specific focus on differentiating between healthy and amyotrophic lateral sclerosis (ALS) lymphoblastic patient samples. By functionalizing SWCNTs with single-stranded DNA, we enhance their stability and target specificity, enabling their application in serum patient samples.

A comprehensive spectral analysis of DNA-SWCNTs was conducted using near-infrared fluorescence spectroscopy and other characterization techniques, including UV-Vis-absorption spectroscopy. The …

Securing The Internet Of Wetland Things (Iowt) Using Machine And Deep Learning Methods: A Survey, Guma Ali, Wamusi Robert, Maad M. Mijwil, Malik Sallam, Jenan Ayad

Mesopotamian Journal of Computer Science

Wetlands are essential ecosystems that provide ecological, hydrological, and economic benefits. However, human activities and climate change are degrading their health and jeopardizing their long-term sustainability. To address these challenges, the Internet of Wetland Things (IoWT) has emerged as an innovative framework integrating advanced sensing, data collection, and communication technologies to monitor and manage wetland ecosystems. Despite its potential, the IoWT faces substantial security and privacy risks, compromising its effectiveness and hindering adoption. This survey explores integrating machine learning (ML) and deep learning (DL) techniques as solutions to address the security threats, vulnerabilities, and challenges inherent in IoWT ecosystems. The …

Leveraging Intrinsic Properties For Classification Of Coal Seams Towards Spontaneous Combustion Proclivity And Predicting Susceptibility Using Machine Learning: Smart And Sustainable Mining Approach, Siddhartha Agarwal, Pradeep K. Gautam, Yuhao Zou, Rishabh Dwivedi, Durga C. Panigrahi, Cihan H. Dagli, A. Singh

Engineering Management and Systems Engineering Faculty Research & Creative Works

Mine fires and other hazards caused by spontaneous coal combustion are a pervasive and longstanding issue in Jharia coalfields, India. This study proposes a novel approach to classify coal seams based on their propensity to spontaneous combustion using the intrinsic properties of 30 coal samples from different seams. This method eliminates the need for expensive and time-consuming experimental determinations of susceptibility indices (SI) such as crossing point temperature (CPT), critical air blast (CAB), and differential thermal analysis (DTA). All clustering models, viz. hierarchical, k-means, and multidimensional scaling, aptly classify coal seams into three categories: highly risky, medium risky, and low …