Physical Sciences and Mathematics Commons^™

Pedestrian Leadership And Egress Assistance Simulation Environment (Please), Kyle D. Feuz

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Pedestrian simulation models are used in many different applications including, the design of safer buildings, the validation of fire codes, and automatic video surveillance and tracking. By improving the simulation model used, each of the application areas can experience similar improvements in accuracy. Current simulation models fail to address key concerns in representing pedestrian knowledge and in accurately modeling group formation. This project has at its core the goal of bringing attention these areas of concern and providing an initial look at ways to solve these problems.

The Pedestrian Leadership and Egress Assistance Simulation Environment (PLEASE) is developed specifically to …

A Dynamic State Metabolic Journey: From Mass Spectrometry To Network Analysis Via Estimation Of Kinetic Parameters, Arockia Ranjitha Dhanasekaran

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

The term "metabolism" refers to the chemical processes occurring in a living organism to convert the food consumed into the energy needed to maintain a living state. Metabolism consists of two states, namely, a dynamic state and a steady state. In the dynamic state, the rate of chemical conversion of a substance is proportional to the amount of substance available, whereas in the steady state this rate is constant and independent of the amount of substance present (Chapter 4 Figure 1). Like all other fields of engineering, metabolic engineering involves the analysis and synthesis of metabolism. Molecular biological tools for …

Test Data Extraction And Comparison With Test Data Generation, Ali Raza

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Testing an integrated information system that relies on data from multiple sources can be a challenge, particularly when the data is confidential. This thesis describes a novel test data extraction approach, called semantic-based test data extraction for integrated systems (iSTDE) that solves many of the problems associated with creating realistic test data for integrated information systems containing confidential data. iSTDE reads a consistent cross-section of data from the production databases, manipulates that data to obscure individual identities while still preserving overall semantic data characteristics that are critical to thorough system testing, and then moves that test data to …

A Fully Automatic Segmentation Method For Breast Ultrasound Images, Juan Shan

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Breast cancer is the second leading cause of death of women worldwide. Accurate lesion boundary detection is important for breast cancer diagnosis. Since many crucial features for discriminating benign and malignant lesions are based on the contour, shape, and texture of the lesion, an accurate segmentation method is essential for a successful diagnosis. Ultrasound is an effective screening tool and primarily useful for differentiating benign and malignant lesions. However, due to inherent speckle noise and low contrast of breast ultrasound imaging, automatic lesion segmentation is still a challenging task.

This research focuses on developing a novel, effective, and fully automatic …

Graph Kernels And Applications In Bioinformatics, Marco Alvarez Vega

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Nowadays, machine learning techniques are widely used for extracting knowledge from data in a large number of bioinformatics problems. It turns out that in many of such problems, data observations can be naturally represented by discrete structures such as graphs, networks, trees, or sequences. For example, a protein can be seen as a cloud of interconnected atoms lying on a 3-dimensional space. The focus of this dissertation is on the development and application of machine learning techniques to bioinformatics problems wherein the data can be represented by graphs. In particular, we focus our attention on proteins, which are essential elements …

Pond-Hindsight: Applying Hindsight Optimization To Partially-Observable Markov Decision Processes, Alan Olsen

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Partially-observable Markov decision processes (POMDPs) are especially good at modeling real-world problems because they allow for sensor and effector uncertainty. Unfortunately, such uncertainty makes solving a POMDP computationally challenging. Traditional approaches, which are based on value iteration, can be slow because they find optimal actions for every possible situation. With the help of the Fast Forward (FF) planner, FF- Replan and FF-Hindsight have shown success in quickly solving fully-observable Markov decision processes (MDPs) by solving classical planning translations of the problem. This thesis extends the concept of problem determination to POMDPs by sampling action observations (similar to how FF-Replan samples …

Understanding Teacher Users Of A Digital Library Service: A Clustering Approach, Beijie Xu

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

This research examined teachers' online behaviors while using a digital library service—the Instructional Architect (IA)—through three consecutive studies. In the first two studies, a statistical model called latent class analysis (LCA) was applied to cluster different groups of IA teachers according to their diverse online behaviors. The third study further examined relationships between teachers' demographic characteristics and their usage patterns. Several user clusters emerged from the LCA results of Study I. These clusters were named isolated islanders, lukewarm teachers, goal-oriented brokers, window shoppers, key brokers, beneficiaries, classroom practitioners, and dedicated sticky users. …

Novel Application Of Neutrosophic Logic In Classifiers Evaluated Under Region-Based Image Categorization System, Wen Ju

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Neutrosophic logic is a relatively new logic that is a generalization of fuzzy logic. In this dissertation, for the first time, neutrosophic logic is applied to the field of classifiers where a support vector machine (SVM) is adopted as the example to validate the feasibility and effectiveness of neutrosophic logic. The proposed neutrosophic set is integrated into a reformulated SVM, and the performance of the achieved classifier N-SVM is evaluated under an image categorization system. Image categorization is an important yet challenging research topic in computer vision. In this dissertation, images are first segmented by a hierarchical two-stage self organizing …