Digital Commons Network^™

Detection Of Data Leakage And Disruption Of Covert Timing Channel In Secure Drone Communication Using Machine And Deep Learning, Jonathan Walatkiewicz

Master's Theses and Doctoral Dissertations

The utilization of recreational drones has experienced a substantial increase in both the United States and globally. However, it is noteworthy that most drones, classified as Internet of Things devices, are produced with a limited security lifecycle. This study's findings are of paramount importance, as traditional computing exploits can be applied to drones, designating them as high- value targets. This study examines the detectability and disruptability of covert timing channel traffic in secure drones. The investigation aims to ascertain the effects of multiple interarrival times, distances ranging from 1 to 330 feet, various detection algorithms, and stream sizes between 32-bit …

Unveiling Explainable Ai In Healthcare: Current Trends, Challenges, And Future Directions, Abdul Aziz Noor, Awais Manzoor, Muhammad Deedahwar Mazhar Qureshi, M. Atif Qureshi, Wael Rashwan

Research Outputs: 2025-Present

This overview investigates the evolution and current landscape of eXplainable Artificial Intelligence (XAI) in healthcare, highlighting its implications for researchers, technology developers, and policymakers. Following the PRISMA protocol, we analyzed 89 publications from January 2000 to June 2024, spanning 19 medical domains, with a focus on Neurology and Cancer as the most studied areas. Various data types are reviewed, including tabular data, medical imaging, and clinical text, offering a comprehensive perspective on XAI applications. Key findings identify significant gaps, such as the limited availability of public datasets, suboptimal data preprocessing techniques, insufficient feature selection and engineering, and the limited utilization …

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, …

Ai-Generated Messaging For Life Events Using Structured Prompts: A Comparative Study Of Gpt With Human Experts And Machine Learning, Christopher Lynch, Erik Jensen, Ross Gore, Virginia Zamponi, Kevin O'Brien, Brandon Feldhaus, Katherine Smith, Joseph Martínez, Madison H. Munro, Timur E. Ozkose, Tugce B. Gundogdu, Ann Marie Reinhold, Hamdi Kavak, Barry Ezell

VMASC Publications

Large Language Models (LLMs) play an increasingly integrated and pivotal role in generating diverse types of texts, such as social media messages, emails, narratives, and technical reports, among other textual communication forms. As AI-generated messaging filters into human communication, a systematic exploration of their effectiveness for mimicking human-like communication of life events is needed. In this study, we employ a zero-shot structured narrative prompt to generate 24,000 life event messages for birth, death, hiring, and firing events using OpenAI's GPT-4. From this dataset, we manually classify 2880 messages and evaluate their validity in conveying these life events through the form …

Qos-Aware Link Adaptation For Beyond 5g Networks: A Deep Reinforcement Learning Approach, Ali Parsa, Neda Moghim, Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

Modern wireless communication systems face increasingly complex challenges due to rapidly changing channel conditions and the growing diversity of application-specific Quality of Service (QoS) requirements. Traditional link adaptation mechanisms primarily aim to maximize throughput and often lack the flexibility to support emerging applications, such as Extended Reality (XR) and Virtual Reality (VR), which demand simultaneous guarantees for high data rates, ultra low latency, and high reliability. These stringent and multidimensional QoS needs call for more intelligent and adaptive solutions. In this paper, we propose QDRLLA (QoS-aware Deep Reinforcement Learning-based Link Adaptation), a novel framework that employs deep reinforcement learning to …

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 …

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. …

Analyzing Visual Attention In Virtual Crime Scene Investigations Using Eye-Tracking And Vr: Insights For Cognitive Modeling, Wen-Chao Yang, Chih-Hung Shih, Jiajun Jiang, Sergio Pallas Enguita, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

Understanding human perceptual strategies in high-stakes environments, such as crime scene investigations, is essential for developing cognitive models that reflect expert decision-making. This study presents an immersive experimental framework that utilizes virtual reality (VR) and eye-tracking technologies to capture and analyze visual attention during simulated forensic tasks. A 360° panoramic crime scene, constructed using the Nikon KeyMission 360 camera, was integrated into a VR system with HTC Vive and Tobii Pro eye-tracking components. A total of 46 undergraduate students aged 19 to 24–23, from the National University of Singapore in Singapore and 23 from the Central Police University in Taiwan—participated …

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 …

Harnessing The Power Of Gradient-Based Simulations For Multi-Objective Optimization In Particle Accelerators, Kishansingh Rajput, Malachi Schram, Auralee Edelen, Jonathan Colen, Armen Kasparian, Ryan Roussel, Adam Carpenter, He Zhang, Jay Benesch

Data Science Faculty Publications

Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-objective optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithms (GAs), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems in particle accelerators using a deep differentiable reinforcement learning (DDRL) algorithm. We compare the DDRL algorithm with model-free reinforcement learning (MFRL), GA, and Bayesian optimization (BO) for simultaneous optimization of heat load and trip rates in the continuous electron beam accelerator facility. The …

Fares On Fairness: Using A Total Error Framework To Examine The Role Of Measurement And Representation In Training Data On Model Fairness And Bias, Patrick Oliver Schenk, Christoph Kern, Trent D. Buskirk

Data Science Faculty Publications

Data-driven decisions, often based on predictions from machine learning (ML) models are becoming ubiquitous. For these decisions to be just, the underlying ML models must be fair, i.e., work equally well for all parts of the population such as groups defined by gender or age. What are the logical next steps if, however, a trained model is accurate but not fair? How can we guide the whole data pipeline such that we avoid training unfair models based on inadequate data, recognizing possible sources of unfairness early on? How can the concepts of data-based sources of unfairness that exist in the …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

Optiselect And Enshap: Integrating Machine Learning And Game Theory For Ischemic Stroke Prediction, Pritam Chakraborty, Anjan Bandyopadhyay, Sricheta Parul, Sujata Swain, Partha Sarathy Banerjee, Tapas Si, Hong Qin, Saurav Mallik

Computer Science Faculty Publications

Stroke analysis using game theory and machine learning techniques. The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. For each sample, the top 3, 4, and 5 features are evaluated and selected to evaluate their performance. The Shapley value method was used to rank the models using their best four features based on their predictive capabilities. As a result, better-performing …

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 …

Point Cloud-Based Diffusion Models For The Electron-Ion Collider, Jack Y. Araz, Vinicius Mikuni, Felix Ringer, Nobuo Sato, Fernando Torales Acosta, Richard Whitehill

Physics Faculty Publications

At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standard Model, and inference tasks. In particular, it has been demonstrated that score-based diffusion models can generate high-fidelity and accurate samples of jets or collider events. This work expands on previous generative models in three distinct ways. First, our model is trained to generate entire collider events, including all particle species with complete kinematic information. We quantify how well the model learns event-wide constraints such as the conservation of momentum and discrete quantum numbers. We …

Digital Health Using Data Science, Padmapriya Velupillai Mekandan, Paramita Basak Upama, Masud Rabbani, Amity Ali, Sheikh Iqbal Ahamed

Computer Science Faculty Research and Publications

The digitization of healthcare has led to an unprecedented growth in health-related data, offering new opportunities to transform clinical decision-making, disease prediction, and patient engagement. However, extracting actionable insights from diverse data sources such as electronic health records, wearable devices, and mobile health apps requires a fusion of domain knowledge in healthcare and technical expertise in data science. This paper presents a structured, interdisciplinary framework for digital health that emphasizes practical strategies for data acquisition, preprocessing, feature engineering, machine learning, and ethical data use. The model promotes a holistic understanding of digital health challenges and opportunities, preparing future professionals to …

Quantifying Multidimensional Effects Of Physicochemical Parameters On Pfas Adsorption Using A Hybrid Response Surface Methodology-Machine Learning Approach, Harsh V. Patel, Jazmin Green, John Park, Stephanie Luster-Teasley Pass, Renzun Zhao

Engineering Management & Systems Engineering Faculty Publications

Per- and polyfluoroalkyl substances (PFAS) contamination has posed a significant environmental and public health challenge due to their ubiquitous nature. Adsorption has emerged as a promising remediation technique, yet optimizing adsorption efficiency remains complex due to the diverse physicochemical properties of PFAS and the wide range of adsorbent materials. Traditional modeling approaches, such as response surface methodology (RSM), struggled to capture nonlinear interactions, while standalone machine learning (ML) models required extensive datasets. This study addressed these limitations by developing hybrid RSM-ML models to improve the prediction and optimization of PFAS adsorption. A comprehensive dataset was constructed using experimental adsorption data, …

Predictive Maintenance In Naval Vessel Propulsion Systems For Enhanced Marine Operations Using A Bigmm-Hmm Framework With Divergence-Based Clustering, Farshid Javadnejad, Hyoshin John Park, Samuel Kovacic, Andres Sousa-Poza

Engineering Management & Systems Engineering Faculty Publications

This study introduces a BiGMM-HMM Integration Framework designed to improve predictive maintenance strategies for naval vessel propulsion systems, addressing the need for efficient and reliable operation in marine engineering applications. The framework effectively manages multimodal sensor data by leveraging a unique combination of Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) in a bidirectional architecture. It analyses the dynamic interactions between sensors and subsystems. Two preprocessing methods are evaluated: Method 1 focuses on subsystem interactions, employing divergence-based root cause analysis to identify key sensor variables by clustering of sensors and subsystems. In contrast, Method 2 processes the entire dataset …

Optimizing Port Logistics Through Generative Ai: Revolutionizing Efficiency And Resilience In The Maritime Industry, Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

The maritime industry faces growing challenges in optimizing port logistics due to increasing trade volumes, environmental regulations, and supply chain disruptions. This comprehensive literature review examines the transformative role of artificial intelligence (AI), with particular focus on generative AI, in enhancing efficiency and resilience in port operations. Through systematic analysis of 23 peer-reviewed studies published between 2021-2025, this review synthesizes advancements in real-time data integration, machine learning, digital twins, IoT, and autonomous systems that collectively improve operational decision-making, risk management, and environmental sustainability. Key findings reveal that machine learning applications achieve 90% effectiveness ratings in operational optimization, while predictive analytics …

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 …

Multiphysics Modeling Of Solid Oxide Fuel Cells For Gradient Minimization And Inductive Loop Analysis In Impedance Spectroscopy Using Machine Learning-Based Microstructural Property Estimation, Muhammad Usman Khan

College of Graduate Studies: Theses & Dissertations

Solid oxide fuel cells have significant advantages in renewable energy utilization due to their high efficiency, fuel flexibility, and low emissions. However, despite the numerous efforts of technology, thermal and current density gradients and impedance behavior fluctuations are still causing performance degradation. A combined computational framework that integrates machine learning and three-dimensional Multiphysics modeling is needed to investigate and optimize the performance of solid oxide fuel cells. A machine learning model, trained on synthetic microstructure data by percolation analysis, is used to predict important microstructural parameters like triple phase boundary density and geometric tortuosity. These are then employed in a …

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Computer Science Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …

Predicting Food Prices In Kenya Using Machine Learning: A Hybrid Model Approach With Xgboost And Gradient Boosting, Benard O. Ogol, Evans Omondi, John Olukuru, Betsy Muriithi, Kennedy Senagi

All Peer-Reviewed Publications

Introduction: Food price volatility continues to be a significant concern in Kenya's economic development, posing challenges to the country's economic stability. Methodology: This study examines the application of machine learning methods, employing a hybrid approach that combines XGBoost and gradient boosting, to predict food prices in Kenya. The food prices data from the World Food Programme, covering the period from January 2006 to September 2024, as well as currency exchange rates data from the Central Bank of Kenya in US dollars (USD) and inflation rates data, were collated and preprocessed to be ready for analytics and machine learning. The augmented …

Implementing An Advanced Friction Testing Program For Seamless Coverage Of Indot System, Chengcheng Tao, Yizhou Lin, Junyi Duan, Cheng Peng, Jieyi Bao, Yi Jiang, Shuo Li

JTRP Technical Reports

The interaction between tires and the road surface is crucial for maintaining vehicle stability and safety. The frictional forces at the tire-road interface, along with the coefficient of friction, affect vehicle dynamics, influencing transportation safety. Rapid advancements in transportation technology, infrastructure demands, and safety regulations pose complex transportation challenges, requiring adaptive solutions. The goal of this study is to provide actionable insights for highway agencies, offering improved friction assessment methodologies that support safer and more effective roadway maintenance and design. This study investigates and verify Locked Wheel Skid Tester (LWST) friction measurement on horizontal curves using Sideway-force Coefficient Routine Investigation …

Insider Threat Agent: A Behavioral Based Zero Trust Access Control Using Machine Learning Agent, Michael Fojude

College of Graduate Studies: Theses & Dissertations

Hybrid work, cloud adoption, and freely available AI‑enabled attack tools have exposed critical weaknesses in perimeter‑centric security. Current breach reports attribute more than one‑third of incidents to insider misuse or credential compromise, yet many organizations still depend on static Role‑ or Attribute‑Based Access Control that neither verifies intent continuously nor adapts to subtle behavioral change. This research addresses that gap by designing and validating a behavioral based Zero Trust Access Control (ZTAC) Agent. A five‑year enterprise log Dataset was extracted and cleansed to establish a high‑fidelity baseline of normal user behavior. Feature engineering captured temporal regularity (login sequence, session duration), …

Temporal Analysis Of Construction Safety Incidents In Southeastern U.S. Using Machine Learning Techniques, Mayowa O. Oladele

College of Graduate Studies: Theses & Dissertations

Construction safety incidents remain a significant concern, particularly in the Southeastern U.S. due to the high-risk nature of the industry. Analyzing patterns in these incidents can help improve safety practices and reduce accidents. Machine learning (ML) techniques were employed in this study to identify temporal patterns in construction safety incidents, aiming to enhance proactive safety management. The machine learning methods used in this research included logistic regression, decision trees, random forest, support vector machine (SVM), and K nearest neighbors (KNN).

The objective of the study was to analyze temporal trends in safety incidents and identify the most effective machine learningtechnique …

Multi-Objective Inverse Optimization Of High-Speed Interconnects Using Cascaded Deep Neural Network, Yicheng Zhang, Ling Zhang, Hyunwook Park, Bo Pu, Xiao Ding Cai, Chulsoon Hwang, Bidyut Sen, Jun Fan, Er Ping Li, James L. Drewniak

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel multi-objective inverse optimization method for high-speed interconnects based on a cascaded deep neural network (DNN) structure, which can efficiently optimize characteristic impedance, insertion loss, and far-end crosstalk (FEXT) simultaneously. Parameter optimization for high-speed interconnects is essential to the signal integrity and electrical performance of complex designs such as multilayer printed circuit boards (PCBs) and chiplets. Conventional optimization approaches often rely on numerous optimization iterations, which is highly time-consuming, especially in high dimensional parameter spaces. This paper proposes a novel DNN based method by cascading an inverse-prediction network and a forward-prediction network to achieve multi-objective optimization …