The Importance Of Text Representation For Neural Networks Through Natural Language Processing Techniques,
2024
University of Arkansas, Fayetteville
The Importance Of Text Representation For Neural Networks Through Natural Language Processing Techniques, William Parsley
Data Science Undergraduate Honors Theses
Text representation is a fundamental aspect of natural language processing (NLP) when it comes to the performance of neural networks. Free-form text fields are being utilized in more and more industries. Anything from a description of an item on a web store to tracking service events to military-grade aircraft is being collected in free-form text. The goal of the thesis is to highlight best practices and discuss trends in data to prepare text for a neural network. It will demonstrate various techniques for representing free-form text in the context of neural networks, focusing on data preparation decisions, embedding techniques, and …
Examining The Impact Of Customer Rfp Characteristics On Award Compliance,
2024
University of Arkansas, Fayetteville
Examining The Impact Of Customer Rfp Characteristics On Award Compliance, Laasya Ravipati
Data Science Undergraduate Honors Theses
In the context of intermodal transportation, understanding the dynamics of award compliance holds significant importance for operational efficiency and strategic decision-making. Award compliance refers to the percentage of awarded freight volume that is realized, indicating the extent to which contractual agreements are fulfilled. This analysis delves into the intricate relationship between customer characteristics and award compliance, aiming to provide valuable insights into the variability and predictability of compliance rates. By analyzing Request for Pricing (RFP) data and primary awarded freight volumes, the study seeks to address the need for more accurate volume estimations, crucial for sales planning, revenue projections, and …
Examining Award Compliance To Inform Resource Allocation,
2024
University of Arkansas, Fayetteville
Examining Award Compliance To Inform Resource Allocation, Jacob Haarala
Data Science Undergraduate Honors Theses
This project focuses on JB Hunt Transport Inc's intermodal business unit (JBI) by focusing on the challenges associated with Published Pricing and Contractual Pricing. The primary issue revolves around the variance between the awarded freight volumes in Requests for Pricing (RFPs) and the actual volumes realized when the freight is shipped. This discrepancy poses challenges for effective sales planning, revenue goals, and optimal freight network management within JBI. Reporting tools, such as PowerBI, are currently used by JBI to provide insights into award compliance on a weekly basis. However, our goal with this project was to provide a deeper understanding …
Cost-Risk Analysis Of The Ercot Region Using Modern Portfolio Theory,
2024
The University of Southern Mississippi
Cost-Risk Analysis Of The Ercot Region Using Modern Portfolio Theory, Megan Sickinger
Master's Theses
In this work, we study the use of modern portfolio theory in a cost-risk analysis of the Electric Reliability Council of Texas (ERCOT). Based upon the risk-return concepts of modern portfolio theory, we develop an n-asset minimization problem to create a risk-cost frontier of portfolios of technologies within the ERCOT electricity region. The levelized cost of electricity for each technology in the region is a step in evaluating the expected cost of the portfolio, and the historical data of cost factors estimate the variance of cost for each technology. In addition, there are several constraints in our minimization problem to …
Modeling Of Multi-Period Disaster Logistics Planning In The State Of South Carolina,
2024
Clemson University
Modeling Of Multi-Period Disaster Logistics Planning In The State Of South Carolina, Emma Simon
All Theses
South Carolina is one of the most vulnerable states in the United States to the impact of hurricanes. Currently, when threatened with a natural disaster such as a hurricane, the state government makes many vital decisions based on knowledge and experience. In this study, the distribution of disaster relief commodities to meet immediate needs is analyzed through two models for the case of South Carolina to generate an optimal logistics strategy that considers the social vulnerability of affected populations. The first model is a multi-objective pre-disaster logistics model that uses a four-index formulation for the multiple trip vehicle routing problem. …
Denoising Diffusion Probabilistic Models Based Accelerated Mri,
2024
Florida Institute of Technology
Denoising Diffusion Probabilistic Models Based Accelerated Mri, Alexander Francis Bugielski
Theses and Dissertations
Magnetic Resonance Imaging (MRI) is a cornerstone in obtaining intricate visualizations of anatomy and physiological processes within the human body. However, its extensive scan duration not only causes patient discomfort but also increases the likelihood of motion-induced artifacts in the images. To address such a challenge, this study investigates deep neural network models for reconstructing high-resolution MRI images from noisy and significantly undersampled data in a supervised learning manner. Specifically, it compares three models: a conventional U-Net, a self-attentive U-Net, and an innovative probabilistic diffusion model that builds upon the self-attentive U-Net architecture. These models are evaluated on their ability …
Adopting Information System Technologies In Construction Project Management,
2024
Harrisburg University of Science and Technology
Adopting Information System Technologies In Construction Project Management, Hadi Haikal
Harrisburg University Dissertations and Theses
The construction sector has remained late in integrated information systems' adopting process to perform the system improving processes. Lack of adoption of technology has been a root cause of the early issues they faced, such as budgetary and scheduling overshoots, quality defects, safety occurrences, and generally poor communication and coordination among the stakeholders. This research looks into the benefits and challenges, the key success factors, and the implications of using an array of information systems, including building information modeling (BIM), artificial intelligence (AI), Internet of Things (IoT) sensing, and enterprise systems, to comprehensively address issues in project management in construction …
Analyzing The Impact Of Socioeconomic Factors On Cancer Clinical Trials Accessibility In The U.S. Using Machine Learning,
2024
Arkansas Tech University
Analyzing The Impact Of Socioeconomic Factors On Cancer Clinical Trials Accessibility In The U.S. Using Machine Learning, Krysta L. Ray, Hiromi Honda
ATU Scholars Symposium
While cancer impacts all segments of the United States population, specific groups experience a disproportionate burden of the disease due to social, environmental, and economic disadvantages. This research examines the correlation between socioeconomic factors and the accessibility of cancer clinical trials across U.S. counties, employing a comprehensive dataset, County-Level Socioeconomic and Cancer Clinical Trial Data from Noah Ripper, and advanced machine-learning techniques. Our findings, derived from regression analysis and machine learning models like gradient boosting, highlight significant disparities in trial availability linked to socioeconomic indicators, including poverty rates, population estimates, median income, incidence rates, and mortality rates. Many regression models …
Optimization Of Human Interactions In The College Campus Model Via Simio Integration,
2024
Embry-Riddle Aeronautical University
Optimization Of Human Interactions In The College Campus Model Via Simio Integration, Benjamin E. Chaback
Doctoral Dissertations and Master's Theses
College campuses are a significant part of life in some cities. Many students each year attend university, pursuing additional knowledge from faculty members. Both staff and faculty members rely on these students to have successful jobs and to ensure the university functions. Yet recently, more and more students are attending, leading to overcrowding, lower admission rates, and difficulty getting into good programs. Previous work exists on qualitative student affairs and quantitative retention data, yet little on using simulations to model this problem. This work aimed to (a) Determine the ability to successfully model human interactions/people flow on a college campus, …
Understanding The Impact Of Emergent Conflict On Communication And Team Cognition: A Multilevel Study In Engineering Teams,
2024
Old Dominion University
Understanding The Impact Of Emergent Conflict On Communication And Team Cognition: A Multilevel Study In Engineering Teams, Francisco Cima
Engineering Management & Systems Engineering Theses & Dissertations
The development of team cognition is crucial for fostering high-performing teams. In cognitive-intensive fields like engineering, effective communication serves as a primary precursor to team knowledge development, enabling group members to effectively retrieve and utilize each other's expertise. Despite the critical role of communication, there is a lack of empirical research examining how conflict situations, which are critical emerging factors inherent to teamwork, interact with communication processes to constrain team knowledge development and utilization. This study, rooted in information processing theory, investigates how emerging conflict shapes multilevel team knowledge structures by interacting with communication processes in engineering project teams. Prior …
An Assignment Model For Lateral Transfers Matching Base Repair Facilities To Xf3 Coded Nsns Requiring Repair,
2024
Air Force Institute of Technology
An Assignment Model For Lateral Transfers Matching Base Repair Facilities To Xf3 Coded Nsns Requiring Repair, George D. Valaika
Theses and Dissertations
This research addresses challenges in efficiently distributing reparable National Stock Numbers (NSNs) to base-level repair facilities, aiming to ease strain on depot resources. It establishes a network integrating bases with similar repair capabilities for NSNs and allocates NSNs to balance repair capacities. Drawing from USAF reparable inventory modeling and inventory management theory, it generates data mimicking historical data to maximize total expected part repairs by assigning NSNs based on the best percentage of base repair (PBR).
Factors Affecting The Retention Of Active Duty Airmen,
2024
Air Force Institute of Technology
Factors Affecting The Retention Of Active Duty Airmen, Gregory A. Picardi
Theses and Dissertations
This study investigates the factors influencing the early exit of active duty Airmen, particularly in the context of the recruitment challenges faced by the USAF in Fiscal Year 2023. The research highlights the significant impact of the implementation of MHS Genesis, a healthcare administration program, on recruitment processes and the broader issues affecting military recruitment, including physical and emotional trauma concerns among potential recruits. Through a survey conducted at Wright-Patterson Air Force Base involving 251 participants, the study utilizes a Chi-square test to explore the primary and secondary reasons for leaving active duty, with family pressure, stability, and financial reasons …
A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence,
2024
Air Force Institute of Technology
A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae
Theses and Dissertations
A growing demand exists for interpretable artificial intelligence models, leading to extensive research efforts to enhance the explainability and transparency of policies generated by reinforcement learning (RL) methods. This research develops random forest-based RL algorithms as a logical progression in this academic pursuit. The algorithms are evaluated using three standard benchmark environments from OpenAI gym — CartPole, MountainCar, and LunarLander — and compared to implementations of the Deep Q-learning Network (DQN) and Double DQN (DDQN) algorithms for various metrics, including performance, robustness, efficiency, and interpretability. The random forest-based algorithms exhibit superior performance to both neural network-based algorithms in two out …
Enhancing Port Efficiency And Lead Time Reduction Through Predictive Analysis: A Case Study Of Container Management At Khalifa Bin Salman Port,
2024
Air Force Institute of Technology
Enhancing Port Efficiency And Lead Time Reduction Through Predictive Analysis: A Case Study Of Container Management At Khalifa Bin Salman Port, Abdulaziz A. Aljalahma
Theses and Dissertations
Khalifa bin Salman Port (KBSP), a key pillar in Bahrain's maritime infrastructure, is the focal point of this study, highlighting the significant role of predictive analytics in optimizing port operations. This thesis analyzes container throughput data from 2017 to 2022, provided by Bahrain's Ministry of Transportation database. This data forms the basis for forecasting the 2023 throughput. The study thoroughly compares these predictions with the actual 2023 data, assessing the predictive model's accuracy. The findings underscore the importance of predictive analytics in strategic decision-making for port management, focusing on enhancing operational efficiency and reducing lead times. This research offers a …
Optimizing Deployment Kit, Introducing Acceptance Threshold,
2024
Air Force Institute of Technology
Optimizing Deployment Kit, Introducing Acceptance Threshold, Saleh A. Alshuhri
Theses and Dissertations
This article addresses the optimization of a specialized deployment kit crucial for air force operations, emphasizing the need for rapid and efficient aircraft deployment. Through a comprehensive analysis of historical data, the study aims to streamline the kit's contents without compromising effectiveness. The research suggests a potential reduction of 7.8%, with an impressive 60.5% decrease if a minimal 5% threshold is deemed acceptable. While the primary focus is on air force deployment, the broader implications extend to military entities, such as armies and navies, highlighting the applicability of the findings in enhancing deployment efficiency across various defense sectors.
Electric Vehicle Support Equipment Deployment At Military Installations: A Mixed-Integer Linear Programming Approach,
2024
Air Force Institute of Technology
Electric Vehicle Support Equipment Deployment At Military Installations: A Mixed-Integer Linear Programming Approach, Katelyn M. Barton
Theses and Dissertations
This research provides insights into a mixed integer linear programming model that finds the ideal number and type of Electric Vehicle Support Equipment (EVSE) required to meet U.S. military installations’ electric energy demands. Executive Order No. 14057 (2021) requires federal agencies to transition to electric non-tactical vehicles by 2035. This research determines minimum cost solutions to implement the transition incorporating real-world constraints, including the weekly vehicle mileage demand, EVSE cost, charging time, and EVSE capacity. This study contributes to the broader effort of the U.S. military to combat climate change and enhances the understanding of efficient EVSE deployment strategies in …
Federated Medical Scoring Systems,
2024
Air Force Institute of Technology
Federated Medical Scoring Systems, Jacob F. Bryant
Theses and Dissertations
Federated Learning (FL) is a recent framework of machine learning implementation that trains models on a distributed network of clients as opposed to housing and analyzing this data centrally. This has data communication and practical data privacy advantages, the latter of which is particularly attractive to the medical community where patient privacy is closely safeguarded. We apply FL to a family of sparse linear integer models called Medical Scoring Systems (MSSs). We create a novel methodology for creating these MSSs in a simulated federated environment that involves an lo constrained Logistic Regression (LR), loss-surface examination, and rounding procedure. We tested …
On Intrinsic Dimensionality Of Data Sets And Neural Networks,
2024
Air Force Institute of Technology
On Intrinsic Dimensionality Of Data Sets And Neural Networks, Ori Chachmo
Theses and Dissertations
The concept of Intrinsic Dimensionality (ID) is of special interest in the field of Neural Networks (NNs) since it promotes both (a) a deeper understanding of the underlying mechanisms, and (b) embraces parsimonious modeling (that is, building the right-sized model for the task) with associated benefits to processing speed and storage requirements. This thesis explores the concept of ID via two separate, but related, questions. First, we study the potential of NN ID prediction by exploiting easily obtained quantities measured on the data. We then explore NN ID as an independent concept by comparing the results of different methods for …
Systematic Review Of Supply Chain Control Tower Critical Success Factors And Resilience Effects,
2024
Air Force Institute of Technology
Systematic Review Of Supply Chain Control Tower Critical Success Factors And Resilience Effects, Clay H. Chaffin
Theses and Dissertations
Supply chain control towers (SCCTs) are emerging as a vital component of modern supply chain management (SCM); however, research on SCCTs is limited and disjointed. This paper aims to uncover the critical success factors (CSFs) necessary for high-performing SCCTs, their relationship to the enablers and phases of supply chain resilience (SCRES), and the underlying theoretical framework of this relationship.
Evaluation Of Vtol-Capable Cargo Uavs For Dispersible Airfield Logistics In The Usindopacom Aor,
2024
Air Force Institute of Technology
Evaluation Of Vtol-Capable Cargo Uavs For Dispersible Airfield Logistics In The Usindopacom Aor, Maria C. Chedzoy
Theses and Dissertations
This research models and analyzes the ability of commercial cargo UAVs to rapidly evacuate logistics from an airfield to proximal, outlying destinations, particularly in the USINDOPACOM AOR. This is a tenet of Agile Combat Employment by the USAF, which seeks to mitigate the effect of kinetic threats by near-peer adversaries. The analysis sets forth a binary linear program to minimize the total time to evacuate a fixed amount of logistics from an airfield. Parameters include the cargo UAV with its performance specifications, number of cargo loading points at the airfield, number of destinations for cargo evacuation, and subset of destinations …
