Engineering Commons^™

Predicting Global Healthcare Supply Chain Delays: A Machine Learning Approach Leveraging Country-Level Logistics Metrics, Jeevan Sai Gali, Nima Molavi, Sepideh Alavi

Journal of International Technology and Information Management

In global healthcare logistics, ensuring the timely delivery of medical commodities is critical, particularly in low- and middle-income countries characterized by infrastructural limitations and operational uncertainties. This research introduces an advanced, data-driven predictive framework designed to forecast delivery delays by synthesizing granular, internal shipment-level data from the USAID Global Health Supply Chain Program (GHSC-PSM) with external country-level logistics capabilities indicators derived from the World Bank’s Logistics Performance Index (LPI). Rather than relying on retrospective trend analyses, this study employs machine learning algorithms such as Random Forest, XGBoost, Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP) to detect …

Advancing Electrical Stimulation: Full-Head Mri Segmentation For Abnormal Brain Anatomy With Tdcs, Andrew Birnbaum

Dissertations and Theses

Evaluating the effectiveness of transcranial direct current stimulation (tDCS) is essential for guiding its integration into therapeutic and performance-enhancement applications. In our laboratory, we investigate the efficacy of tDCS across multiple experimental models, including both animal and human studies. I have contributed significantly to the execution and analysis of these experiments, which include studies in rats and healthy human participants aimed at evaluating whether electrical stimulation of the motor cortex can enhance motor learning. These studies assess improvements in fine motor performance resulting from tDCS. In stroke patients, I contribute to our investigation of tDCS as a rehabilitative intervention, particularly …

Enhancing Ai-Driven Automation For Object Detection And Computer Vision, Nafeeul Alam Walee

College of Graduate Studies: Theses & Dissertations

In recent years, AI-driven automation has revolutionized the field of object detection and computer vision, enabling sophisticated and efficient solutions across various industries. This research explores the latest advances and techniques in improving AI-driven automation for object detection and computer vision applications. We examine state-of-the-art deep learning models and frameworks that have contributed to significant improvements in accuracy and speed and highlight the generative results. The focus is on exploring the real-time processing capabilities that have expanded the applicability of these technologies in real-world scenarios. Furthermore, we investigate image integration and video data to improve precision detection and contextual understanding. …

Predicting Complex Di-Alkylation Kinetics Using Machine Learning, Colin Bailey, Thomas Roper

Graduate Research Posters

Background: Predicting reaction kinetics for complex chemical systems often presents significant challenges due to intricate reaction pathways, multiple product formations, and competing side reactions. Additionally, the transient nature of reaction intermediates and limited experimental data complicate efforts to capture system dynamics accurately. These complexities necessitate advanced modeling approaches and precise experimental techniques to reliably describe and predict chemical behavior.

Methods: This work investigates the kinetics of di-alkylation of 4-Hydroxybenzoic Acid (4-HBA) using multiple machine learning algorithms, including Random Forest Regression, Gradient Boosting, Lasso, Ridge, and XGBoost. By leveraging reaction conditions, such as temperature, sulfuric acid equivalents, and initial concentrations of …

Using Data Science And Sustainability Assessment Tools To Evaluate Wastewater Treatment Technologies, Haidar Shakuir Aldaach

Graduate Theses, Dissertations, and Problem Reports (ETD)

Wastewater management in small communities has faced numerous challenges in recent years, including population growth, limited budgets for operation and maintenance, and stringent effluent standards required by environmental regulations. These challenges motivate enhancing wastewater treatment processes to protect water quality. Wastewater treatment performance can be improved in small towns and rural regions by implementing data-driven strategies. Data science, life cycle assessment (LCA), and life cycle cost analysis (LCCA) are three data-driven decision-making tools that require data collection, modeling, computational models, and statistical analysis that could be utilized to improve wastewater treatment efficiencies. In this study, these three tools were adopted …

Synthetic Data–Driven Early Prediction Framework For Acute Kidney Injury In Patients Receiving Vancomycin And Ceftazidime/Avibactam, Maryam Ramazani

Graduate Theses, Dissertations, and Problem Reports (ETD)

Background: The nephrotoxic risks of combining ceftazidime/avibactam (AVI) with vancomycin (VAN) remain underexplored, despite both agents independently being linked to acute kidney injury (AKI). This study assessed the risk of AKI associated with concurrent VAN and ceftazidime/avibactam (VAN-AVI) therapy and developed synthetic data models to enable early prediction of AKI.

Methods: We conducted a retrospective analysis using electronic health record data from hospitalized adults between 2015 and 2022. The incidence of AKI was compared among patients receiving VAN-AVI or VAN in combination with piperacillin/tazobactam (VAN-TPZ) versus VAN monotherapy. AKI was defined as a composite of de novo and recurrent AKI …

Physics-Informed Neural Network Based Aerodynamic Modeling Framework, Nathaniel E. Michek

Graduate Theses, Dissertations, and Problem Reports (ETD)

Significant advances have been made in developing aerodynamic models over the years. While these advances span many modeling techniques and data collection methods, certain aerodynamic regimes still pose significant challenges. These regimes commonly occur when an aerodynamic body is under extreme flight conditions. Many of these conditions occur simultaneously in the rare and poorly understood case of a tumbling aerodynamic body. The flight of a tumbling aerodynamic body goes through the entire range of aerodynamic angles, alpha and beta, causing significant non-linearities and time-dependent effects associated with flow separation. During tumbling, the aerodynamic body simultaneously rotates about all three axes …

Customer Segmentation And Fuel Economy Prediction Using Telemetry Data From Heavy-Duty Trucks, Batishahe Selimi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Heavy-duty trucks constitute only a modest fraction of on-road vehicles, yet their intensive duty cycles and high fuel demands yield a disproportionately large share of transportation fuel use and greenhouse gas emissions. Addressing this imbalance requires data-driven tools that capture the realities of fleet operation and translate complex telemetry into actionable insight.

This dissertation introduces a unified machine-learning framework that operates exclusively on high resolution time-series data collected from fifty-nine diesel trucks deployed across Southern California. It begins by constructing a multi-modal feature space that blends statistical summaries of key engine signals, static vehicle descriptors, and Mel-Frequency Cepstral Coefficients, thereby …

Ai-Based Dynamic Spectrum Allocation Model For Wireless Network Management, Sai Sashank Peddibhotla

Master's Projects

The growth of wireless communication has introduced challenges in the dynamic and resource contrived space which is the efficient utilization of bandwidth and spectrum. This research presents a model for dynamic spectrum allocation with the help of Convolutional Neural Network (CNN) for feature extraction and the Deep Q-Network (DQN) model’s reinforcement learning architecture. The CNN captures both spatial and temporal features of the network states and gives them to the DQN for optimal allocation decision making. This CNN-DQN architecture effectively implements spectrum resource allocation in wireless networks and adapts to resource allocation changes within performance bounds. The system’s performance is …

Design Of Real-Time And Energy-Efficient Driver Assist Systems Using Electroencephalogram And Neuromorphic Computing, Nathan Alan Lutes

Doctoral Dissertations

Despite the technological breakthroughs in advanced driver assist systems, distracted driving persists as a major challenge to roadway safety. This investigation advances the body of knowledge towards a solution by developing an individualized driver-state detection method using electroencephalogram (EEG) and neuromorphic computing to provide a less invasive and more energy efficient ADAS solution. It furthermore explores the changes in brain functional connectivity under distracted conditions to better understand brain state information that could be used for neuro-feedback intervention systems. The first contribution introduces the concept of using Convolutional Spiking Neural Networks (CSNNs) for recognition of patterns with movement-intention predictive power …

Drone-Based Multimodal Sensing On Vegetations And Data Analytics For Early Detection Of Gas Leakage From Underground Pipelines, Pengfei Ma

Doctoral Dissertations

"This study explored the feasibility of using multimodal remote sensors in the early detection of natural gas leaks from underground pipelines, as vegetation situated above pipelines can discern microbial changes in soil when affected by gas and exhibit physiological stress symptoms on leaves. Three sensors (RBG, thermal, and hyperspectral cameras) were utilized to monitor vegetation stress as an indicator of gas leaks. A laboratory experiment was conducted to evaluate the workability of vegetation for gas leak detection. Regular hyperspectral imagery was collected from test vegetations to identify gas stress and distinguish it from other environmental stressors such as salinity impact, …

Landslide Prediction Using Time-Series Decomposition, Reinforcement Learning-Based Feature Selection And Ml Models, Mohith Sai Venkat Ankem

Master's Projects

Landslides pose significant risks to human life, the community, and the environment, yet their prediction remains a complex and unexplored challenge. Existing prediction models often rely on surface measurements and satellite images, neglecting the critical role, in providing deeper insights into landslide analysis. The literature review highlights a lack of research in time series decomposition techniques, despite their potential to improve prediction accuracy. Similarly, feature selection methods that enhance model robustness and precision have not been adequately addressed. This study presents a novel approach to predicting landslide displacement by combining feature selection through reinforcement learning techniques with advanced time-series machine …

Generalizing Classification Of Pilot Workload: Transfer Learning Versus A Jepa-Inspired Transformer Architecture, Naim Barnett, Shivani Nagrecha, Morgan Glover, Clayton Harper, Justin Wilson, James Maher, Eric C. Larson

International Journal of Aviation, Aeronautics, and Aerospace

Within the context of learning, there poses difficulty when objectively measuring human performance. In this work, we investigate the evaluation of human performance via its relation to the individual's mental capacity by classification of cognitive load within the domain of aviation. By utilizing a mixed virtual and physical flight simulation environment in conjunction with biometric sensing, we create and evaluate the predictive capabilities of a Joint-Embedding Predictive Architecture (JEPA) and compare the architecture and results to traditional methods for transfer learning and domain adaptation. We find that our JEPA inspired architecture can achieve more than 70% accuracy of cognitive workload, …

Quantitative Analysis Of Machine Learning Model Performance And The Need To Consider Explainability, Vishnu S. Pendyala

Open Educational Resources

This presentation, titled "Quantitative analysis of Machine Learning model performance and the need to consider explainability," delves into various metrics used for evaluating machine learning models. It thoroughly examines fundamental classification metrics like accuracy, precision, recall, and F-score, while also discussing more advanced measures such as the Kappa Statistic and Matthews Correlation Coefficient (MCC), particularly highlighting their relevance in scenarios with imbalanced datasets. The presentation underscores the importance of model accuracy in real-world applications and briefly introduces regression metrics like R-squared and F-statistic. Additionally, it addresses challenges related to data imbalance and fairness in ML models, stressing the critical need …

Application Of Digital Rock Physics And Machine Learning To Improve The Understanding Of Fluid Flow Behavior Through Porous Media, Md Irfan Khan

Mechanical Engineering Theses

Accurately estimating reservoir rock properties and understanding capillary trapping mechanisms are crucial for fluid storage and flow modeling in porous media, particularly for applications such as carbon dioxide sequestration (CCS) and underground hydrogen storage (UHS). This thesis presents a comprehensive workflow that combines machine learning techniques and pore-network modeling approach to predict petrophysical properties and assess the impact of microscopic pore structures on capillary trapping. In the first part, a convolutional neural network (CNN) framework is used to predict rock properties—such as porosity, throat area, and pore surface area—from micro-computed tomography (micro-CT) X-ray images. It has been observed that incorporating …

Training Machine Learning Algorithms For The Transient Startup Of A Long-Life Modular Microreactor, Ahmad N. Shaheen

Nuclear Engineering ETDs

There is an increasing interest in developing and deploying small modular nuclear reactors (SMRs) and micro modular reactors (MMRs). They could be operated with minimal on-site personnel or remotely with a high degree of autonomy. This can be enabled through the use and application of Artificial Intelligence (AL) and Machine Learning (ML) algorithms and methods. The objective of the present work is to train machine learning algorithms for remote control mimicking the transient startup of the Very-Small, Long-LIfe, Modular (VSLLIM) microreactor. This walk-away safe microreactor design and a fully integrated transient model have been developed at the University of New …

Performance Evaluation Of Routing Protocols And Ml-Based Enhancements For Uav-Assisted Post-Disaster Communication Networks, Prachi Choudhary

Doctoral Dissertations and Master's Theses

During natural disasters, the existing communication systems collapse, and the disaster-affected areas become disconnected without any means of exchanging information. The collapse of existing communication networks poses challenges for First-Responders (FRs) in locating survivors during Search and Rescue (SAR) operations and for survivors to communicate for emergency aid. To alleviate post-disaster consequences and save lives, Uncrewed Air Vehicles (UAVs), commonly known as drones, can be employed to establish adaptable and reliable emergency communication networks. UAVs offer portability and rapid deployment, making them effective in crises. This thesis presents two main contributions: 1) Evaluation and Comparison of Routing Protocol Performance: The …

Leveraging Machine Learning For Defect Detection In Irrigation Concrete Canal Lining In Egypt: Advancing Construction Quality And Efficiency, Mohamed Nabawy

Civil Engineering

Abstract:

Defects during the construction phase of projects pose significant challenges, particularly in terms of

safety, cost overruns, delays, and labor inefficiencies. In irrigation concrete canal lining construction

with mega investments, early detection of defects is critical to ensuring quality and project success.

This study explores the application of machine learning (ML) for onsite defect detection, focusing

on the development and deployment of an object detection mobile application specifically tailored

for identifying visible defects in concrete canal lining construction. Leveraging the machine learning

capabilities of Microsoft Azure, a custom object detection model was trained and validated to

recognize defects such …

Applying Positive Unlabeled Learning Techniques And Using The Kullback-Leibler Divergence To Improve Geothermal Surveying Assessments, Martín Thomas Rodriguez

Dissertations and Theses

As we face the current climate crisis, the discovery of geothermal energy resources has the potential to greatly reduce our dependence on fossil fuels worldwide. However, the development of any new energy infrastructure is expensive and depends on the willingness of energy agencies and developers to make initial investments based on calculated risk measures. One such measure, called geothermal favorability, is the likelihood that a site has conditions favorable for geothermal systems containing recoverable energy potential. Its prediction from existing geophysical datasets proves to be a nontrivial task. The prediction of geothermal favorability can be framed as a binary classification …

Graph Neural Networks Powered Scientific Paper Recommendation, Junhao Shen

Computer Science and Engineering Theses and Dissertations

Scientific paper recommendation systems aim to help researchers discover relevant papers amidst the vast and ever-growing body of literature. With the exponential yearly increase in scientific publications, the demand for effective paper recommendation solutions has become both critical and increasingly challenging. In recent years, deep learning techniques have revolutionized recommender systems, and scientific paper recommendations have naturally integrated these advancements. In this dissertation, we address these challenges through three progressive contributions.

First, we enhance traditional content-based methods using Graph Neural Networks (GNNs) by introducing a Graph Convolutional Network-strengthened Topic Modeling (GCN-TM) approach. This method improves upon conventional topic modeling techniques …

Development Of A Pressure Sensing System Coupled With Deployable Machine Learning Models For Assessing Residual Limb Fit In Lower Limb Prosthetics, Maxwell D. Lewter

Master's Theses

Lower limb amputations pose significant challenges for patients, with over 150,000 cases annually in the U.S., leading to a high demand for effective prosthetics. However, only 43% of lower limb prosthetic users report satisfaction, primarily due to issues with socket fit, which is critical for comfort, stability, and preventing injury. This study presents a deployable sensing system for potentially real-time monitoring of prosthetic socket fit by using pressure sensors and convolutional neural networks (CNNs) to analyze the pressure distribution within the socket. A novel CNN architecture, utilizing both dilated and strided convolutions, is proposed to effectively capture spatial-temporal patterns in …

Physics-Informed Deep Learning For Pilot Parameter Estimation And Pilot-Induced Oscillation Characterization, Stephen A. Brutch

Doctoral Dissertations and Master's Theses

This thesis investigates the issue of loss-of-control in flight which is a driving contributor to fatal aviation accidents. The two main contributors tackled in this research are human pilot error and categories of pilot-induced-oscillations. The primary objective is to develop models that can capture the mathematical parameters that relate to human pilot control under the widely used McRuer Mathematical pilot model. The goal is that if the mathematical parameters that relate to human pilot control can be monitored during flight then the pilot’s input to the control system and pilot-induced-oscillations (PIO) can be monitored during flight to avoid loss-of-control. This …

Dynamic Optimization Of Directed Energy Deposition Build Conditions Using Real-Time Monitoring Via Closed-Loop Control, Callan Herberger

Open Access Theses & Dissertations

Directed Energy Deposition (DED) is an additive manufacturing process that is being rapidly adopted by industry and is well suited for the fabrication of complex components in various metal alloys. DED provides unique benefits such as design flexibility, the potential for in-situ alloying, and an open environment that allows for unobstructed monitoring within the build chamber. Despite these benefits, fully exploiting additive manufacturing's (AM) potential remains a complex task for designers. This dissertation presents a framework for controlling Directed Energy Deposition process variables through in-situ monitoring. An exploration into modifying AM build conditions through the development and implementation of a …

Uncovering The Light Network Load Performance Penalty Of The Network Link Outlier Factor (Nlof), Michelle Lara

Open Access Theses & Dissertations

This thesis evaluates the effectiveness of the Network Link Outlier Factor with Most Likely Link (NLOF: MLL) algorithm under varying network load conditions. Repeated simulation experiments using Mininet were conducted for four different network-wide load levels: 100 Mbps, 500 Mbps, 1 Gbps, and 5 Gbps. Using statistical inference, our experimental results indicate that NLOF: MLL is ineffective under light load conditions (i.e., 100Mbps load) due to the limited network flow data available for its learning process. This limitation highlights a key challenge in applying the algorithm to lightly loaded networks. A preliminary algorithm was proposed to address this light-load performance …

Application Of Lossy And Lossless Compression To Dicom Files, Yizhe Yang

All Theses

The Digital Imaging and Communications in Medicine (DICOM) standard is widely utilized for the management, storage, and transfer of medical images. However, the substantial file sizes associated with DICOM data present challenges in terms of storage and data transmission. Data reduction techniques help address these challenges by minimizing the size of the data while preserving its integrity. This thesis examines various compression methods aimed at reducing the size of DICOM files. We evaluate five lossless compressors and four lossy compressors on DICOM data to compare and assess their performance. Through an analysis of each compressor’s compression efficiency and resulting image …

Computational Representation, Analysis And Verification Of Requirements In Engineering Design And Systems Engineering, Chandan Kumar Sahu

All Dissertations

Systems are developed to satisfy a set of requirements derived from stakeholders’ needs, defining the problem space for which the system is created as a feasible solution. The system design process begins with eliciting these requirements and concludes with validating whether the created system meets them. Requirements engineering (RE) encompasses elicitation, representation, analysis, documentation, verification, and validation. However, challenges in RE, such as imprecision in natural language (NL), proprietary restrictions, and a lack of standardized quality metrics, hinder the creation of well-formed and comprehensive requirements. These challenges complicate formalization and analysis of requirements.

This dissertation addresses these challenges by proposing …

Learning To Accelerate Globally Optimal Solutions: Applications In The Ac Optimal Power Flow Problem, Muhammet Fatih Cengil

Graduate Theses and Dissertations

The Alternating Current Optimal Power Flow (AC-OPF) problem is a fundamental optimization challenge critical to ensuring the economical and reliable operation of power grids. While fast heuristic methods provide upper-bound solutions, assessing their quality requires lower bounds obtained from relaxations of the AC-OPF problem. This dissertation focuses on finding globally optimal solutions to the AC-OPF problem by enhancing the effectiveness and efficiency of Quadratic Convex (QC) relaxations. Leveraging machine learning techniques, we aim to achieve tighter relaxations faster and improve computational performance, enabling practical scalability for real-time applications.

In Chapter 2, we propose a machine learning-based method to accelerate the …

Identifying Redundant Audio Content Over Cloud Environment Using Deduplication Techniques, Venkatesh K

Theses and Dissertations

Cloud computing has become an integral part of modern internet-based services, with users relying heavily on cloud environments as primary storage solutions. However, the exponential growth in data volume presents a challenge (i.e) the proliferation of duplicated content within cloud repositories. Deduplication techniques provide a promising approach to mitigate this issue. This research focuses on detecting redundant audio content within a cloud environment, specifically targeting the sharing of extensive audio files, such as those in Waveform Audio File Format (WAV). The study proposes the Refined Super Subset Identification Algorithm (RSSIA) to efficiently identify redundant content and segments within existing audio …

Investigating Spatiotemporal Trends Using Precursory Signatures: Implications To Develop Short-Term Earthquake Forecasting Techniques In Sumatra-Andaman Region, Ramya Jeyaraman J

Theses and Dissertations

Earthquake forecasting is a challenging field due to Earth's heterogeneous nature. This research aims to develop a short-term earthquake forecasting model by analyzing spatiotemporal trends and precursory signatures in the Sumatra-Andaman region, known for its high seismic activity and tsunami risk. The study adopts an interdisciplinary approach, integrating solid earth tides (SET), micro shocks, and outgoing longwave radiation (OLR) to gain deeper insights into seismic nucleation processes. The research begins by using Singular Spectral Analysis (SSA) to identify potential seismically vulnerable areas through the analysis of irregularities in SET.

A spatiotemporal analysis of micro shocks is conducted to assess the …

A Comparative Analysis Of Optimizing Classification Techniques On Fruits Images Dataset, Ghada F. Elkabbany, Youssef G. Fouad, Kerollos W. Youssef, Jannah A. Amir, Nourhan Zayed

Mechanical Engineering

Data mining; known as data dredging or data archeology; provides several techniques to extract new and interpretable information from existing datasets. This work presents a comparison of the performance of various classification techniques, specifically Support Vector Machine (SVM), Radial Basis Function (RBF), K-Nearest Neighbors (KNN), and decision trees, using images of fruits. An open-source dataset called fruits360 is used focusing on three specific fruits: pineapple, cocos, and avocado. This selection allows testing the classification techniques in different ways, as pineapples and cocos are similar in color, while cocos and avocados resemble each other in shape due to their oval-like geometry. …