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Full-Text Articles in Systems Engineering
Investigating Applications Of Deep Learning For Diagnosis Of Post Traumatic Elbow Disease, Hugh James
Investigating Applications Of Deep Learning For Diagnosis Of Post Traumatic Elbow Disease, Hugh James
McKelvey School of Engineering Theses & Dissertations
Traumatic events such as dislocation, breaks, and arthritis of musculoskeletal joints can cause the development of post-traumatic joint contracture (PTJC). Clinically, noninvasive techniques such as Magnetic Resonance Imaging (MRI) scans are used to analyze the disease. Such procedures require a patient to sit sedentary for long periods of time and can be expensive as well. Additionally, years of practice and experience are required for clinicians to accurately recognize the diseased anterior capsule region and make an accurate diagnosis. Manual tracing of the anterior capsule is done to help with diagnosis but is subjective and timely. As a result, there is …
Using Reinforcement Learning To Improve Network Reliability Through Optimal Resource Allocation, Henley Wells
Using Reinforcement Learning To Improve Network Reliability Through Optimal Resource Allocation, Henley Wells
Graduate Theses and Dissertations
Networks provide a variety of critical services to society (e.g. power grid, telecommunication, water, transportation) but are prone to disruption. With this motivation, we study a sequential decision problem in which an initial network is improved over time (e.g., by adding or increasing the reliability of edges) and rewards are gained over time as a function of the network’s all-terminal reliability. The actions during each time period are limited due to availability of resources such as time, money, or labor. To solve this problem, we utilized a Deep Reinforcement Learning (DRL) approach implemented within OpenAI-Gym using Stable Baselines. A Proximal …
Supporting The Discovery, Reuse, And Validation Of Cybersecurity Requirements At The Early Stages Of The Software Development Lifecycle, Jessica Antonia Steinmann
Supporting The Discovery, Reuse, And Validation Of Cybersecurity Requirements At The Early Stages Of The Software Development Lifecycle, Jessica Antonia Steinmann
Doctoral Dissertations and Master's Theses
The focus of this research is to develop an approach that enhances the elicitation and specification of reusable cybersecurity requirements. Cybersecurity has become a global concern as cyber-attacks are projected to cost damages totaling more than $10.5 trillion dollars by 2025. Cybersecurity requirements are more challenging to elicit than other requirements because they are nonfunctional requirements that requires cybersecurity expertise and knowledge of the proposed system. The goal of this research is to generate cybersecurity requirements based on knowledge acquired from requirements elicitation and analysis activities, to provide cybersecurity specifications without requiring the specialized knowledge of a cybersecurity expert, and …
Supervised Representation Learning For Improving Prediction Performance In Medical Decision Support Applications, Phawis Thammasorn
Supervised Representation Learning For Improving Prediction Performance In Medical Decision Support Applications, Phawis Thammasorn
Graduate Theses and Dissertations
Machine learning approaches for prediction play an integral role in modern-day decision supports system. An integral part of the process is extracting interest variables or features to describe the input data. Then, the variables are utilized for training machine-learning algorithms to map from the variables to the target output. After the training, the model is validated with either validation or testing data before making predictions with a new dataset. Despite the straightforward workflow, the process relies heavily on good feature representation of data. Engineering suitable representation eases the subsequent actions and copes with many practical issues that potentially prevent the …