Data Science Commons^™

Secure Human Action Recognition By Encrypted Neural Network Inference, Miran Kim, Xiaoqian Jiang, Kristin Lauter, Elkhan Ismayilzada, Shayan Shams

Faculty, Staff and Student Publications

Advanced computer vision technology can provide near real-time home monitoring to support "aging in place" by detecting falls and symptoms related to seizures and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social benefits. However, it has not been deployed in practice because of privacy concerns. In this paper, we propose a strategy that uses homomorphic encryption to resolve this dilemma, which guarantees information confidentiality while retaining action detection. Our protocol for secure inference can distinguish falls from activities of daily living with 86.21% sensitivity and 99.14% specificity, with an average …

Measuring And Controlling Medical Record Abstraction (Mra) Error Rates In An Observational Study, Maryam Y Garza, Tremaine Williams, Sahiti Myneni, Susan H Fenton, Songthip Ounpraseuth, Zhuopei Hu, Jeannette Lee, Jessica Snowden, Meredith N Zozus, Anita C Walden, Alan E Simon, Barbara Mcclaskey, Sarah G Sanders, Sandra S Beauman, Sara R Ford, Lacy Malloch, Amy Wilson, Lori A Devlin, Leslie W Young

Faculty, Staff and Student Publications

BACKGROUND: Studies have shown that data collection by medical record abstraction (MRA) is a significant source of error in clinical research studies relying on secondary use data. Yet, the quality of data collected using MRA is seldom assessed. We employed a novel, theory-based framework for data quality assurance and quality control of MRA. The objective of this work is to determine the potential impact of formalized MRA training and continuous quality control (QC) processes on data quality over time.

METHODS: We conducted a retrospective analysis of QC data collected during a cross-sectional medical record review of mother-infant dyads with Neonatal …

Mathematical Models Yield Insights Into Cnns: Applications In Natural Image Restoration And Population Genetics, Ryan Cecil

Electronic Theses and Dissertations

Due to a rise in computational power, machine learning (ML) methods have become the state-of-the-art in a variety of fields. Known to be black-box approaches, however, these methods are oftentimes not well understood. In this work, we utilize our understanding of model-based approaches to derive insights into Convolutional Neural Networks (CNNs). In the field of Natural Image Restoration, we focus on the image denoising problem. Recent work have demonstrated the potential of mathematically motivated CNN architectures that learn both `geometric' and nonlinear higher order features and corresponding regularizers. We extend this work by showing that not only can geometric features …

Immunotherapy For Type 1 Diabetes Mellitus By Adjuvant-Free Schistosoma Japonicum-Egg Tip-Loaded Asymmetric Microneedle Patch (Stamp), Haoming Huang, Dian Hu, Zhuo Chen, Jiarong Xu, Rengui Xu, Yusheng Gong, Zhengming Fang, Ting Wang, Wei Chen

Faculty, Staff and Student Publications

BACKGROUND: Type 1 diabetes mellitus (T1DM) is an autoimmune disease mediated by autoreactive T cells and dominated by Th1 response polarization. Insulin replacement therapy faces great challenges to this autoimmune disease, requiring highly frequent daily administration. Intriguingly, the progression of T1DM has proven to be prevented or attenuated by helminth infection or worm antigens for a relatively long term. However, the inevitable problems of low safety and poor compliance arise from infection with live worms or direct injection of antigens. Microneedles would be a promising candidate for local delivery of intact antigens, thus providing an opportunity for the clinical immunotherapy …

Automated Identification Of Astronauts On Board The International Space Station: A Case Study In Space Archaeology, Rao Hamza Ali, Amir Kanan Kashefi, Alice C. Gorman, Justin St. P. Walsh, Erik J. Linstead

Art Faculty Articles and Research

We develop and apply a deep learning-based computer vision pipeline to automatically identify crew members in archival photographic imagery taken on-board the International Space Station. Our approach is able to quickly tag thousands of images from public and private photo repositories without human supervision with high degrees of accuracy, including photographs where crew faces are partially obscured. Using the results of our pipeline, we carry out a large-scale network analysis of the crew, using the imagery data to provide novel insights into the social interactions among crew during their missions.

Three Case Studies Of Using Hybrid Model Machine Learning Techniques In Educational Data Mining To Improve The Classification Accuracies, Sujan Poudyal

Theses and Dissertations

A multitude of data is being produced from the increase in instructional technology, e-learning resources, and online courses. This data could be used by educators to analyze and extract useful information which could be beneficial to both instructors and students. Educational Data Mining (EDM) extracts hidden information from data contained within the educational domain. In data mining, hybrid method is the combination of various machine learning techniques. Through this dissertation, the novel use of machine learning hybrid techniques was explored in EDM using three educational case studies. First, in consideration for the importance of students’ attention, on and off-task data …

Debiasing Cyber Incidents – Correcting For Reporting Delays And Under-Reporting, Seema Sangari

Doctor of Data Science and Analytics Dissertations

This research addresses two key problems in the cyber insurance industry – reporting delays and under-reporting of cyber incidents. Both problems are important to understand the true picture of cyber incident rates. While reporting delays addresses the problem of delays in reporting due to delays in timely detection, under-reporting addresses the problem of cyber incidents frequently under-reported due to brand damage, reputation risk and eventual financial impacts.

The problem of reporting delays in cyber incidents is resolved by generating the distribution of reporting delays and fitting modeled parametric distributions on the given domain. The reporting delay distribution was found to …

Quantifying Aboveground Biomass In A Tropical Forest Using A Lidar Waveform Weighted Allometric Model, Alejandro Rojas

Theses and Dissertations

Our knowledge of the distribution and amount of terrestrial above ground biomass (AGB) has increased using lidar technology. Recent advancements in satellite lidar has enabled global mapping of forest biomass and structure. However, there are large biases in satellite lidar estimates which impacts our understanding of carbon dynamics, particularly in tropical forests.

Ni-Meister et al. (2022) developed a lidar full waveform weighted height-based allometric model which produced very good results in temperate deciduous/conifer forest in the continental US. The purpose of this study was to evaluate this biomass model in an African tropical forest using the Land Vegetation and Ice …

Evaluating The Variable Stride Algorithm In The Identification Of Diabetic Retinopathy, Ying Zheng, Brian Danaher, Matthew Brown

Beyond: Undergraduate Research Journal

An experiment was performed to investigate a modified pooling method for use in convolutional neural networks for image recognition. This algorithm–Variable Stride–allows the user to segment an image and change the amount of subsampling in each region. This control allows for the user to maintain a higher amount of data retention in more important regions of the image, while more aggressively subsampling the less important regions to increase training speed. Three Variable Stride methods were compared to the preexisting pooling algorithms, Maximum Pool and Average Pool, in three different network configurations tasked with classifying Diabetic Retinopathy images between its early …

Perturbation Modeling For Molecular Design Of Protein Tyrosine Kinase Inhibitors Using Unsupervised Machine Learning, Keerthi Krishnan

Computational and Data Sciences (MS) Theses

The field of computational drug discovery and development has grown, with the aid of new computational tools for novel molecule discovery. In specific, generative deep learning models have excelled as tools to aid in navigating the large space of known molecules and in the creation of new molecules. These models are fed various representations of molecules as inputs and learn to perform a variety of things, such as the optimization of these molecules towards a targeted property. Ultimately, these generative learning models allow us to build bridges between chemical and continuous spaces to understand the compromise between invoking small incremental …

Data Collection And Machine Learning Methods For Automated Pedestrian Facility Detection And Mensuration, Joseph Bailey Luttrell Iv

Dissertations

Large-scale collection of pedestrian facility (crosswalks, sidewalks, etc.) presence data is vital to the success of efforts to improve pedestrian facility management, safety analysis, and road network planning. However, this kind of data is typically not available on a large scale due to the high labor and time costs that are the result of relying on manual data collection methods. Therefore, methods for automating this process using techniques such as machine learning are currently being explored by researchers. In our work, we mainly focus on machine learning methods for the detection of crosswalks and sidewalks from both aerial and street-view …

State-Based Biological Communication, Nathan Clement

All Theses

Allostery (1) is the process through which proteins self-regulate in response to various stimuli. Allosteric interactions occur between nonadjacent spatially distant residues (1), and they are exhibited through the correlated motions (2) and momenta of participating residues. The location of allosteric sites in proteins can be determined experimentally but computational methods to predict the location of allosteric sites are being developed as well (2-4, 10). Experimental and computational methodologies for locating allosteric sites can be used to design specific targeted drug delivery (5-6, 19), but these methods have not yet …

Ensemble Tree-Based Machine Learning For Imaging Data, Reza Iranzad

Graduate Theses and Dissertations

In particular medical imaging data, such as positron emission tomography (PET), computed tomography (CT), and fluorescence intravital microscopy (IVM), have become prevalent for use in a wide variety of applications, from diagnostic purposes, tracking diseases' progress, and monitoring the effectiveness of treatments to decision-making processes. The detailed information generated by medical imaging has enabled physicians to provide more comprehensive care. Although numerous machine learning algorithms, especially those used for imaging data, have been developed, dealing with unique structures in imaging data remained a big challenge. In this dissertation, we are proposing novel statistical tree-based methods with more efficient and more …

Parallel Algorithms For Scalable Graph Mining: Applications On Big Data And Machine Learning, Naw Safrin Sattar

LSU New Orleans Theses and Dissertations

Parallel computing plays a crucial role in processing large-scale graph data. Complex network analysis is an exciting area of research for many applications in different scientific domains e.g., sociology, biology, online media, recommendation systems and many more. Graph mining is an area of interest with diverse problems from different domains of our daily life. Due to the advancement of data and computing technologies, graph data is growing at an enormous rate, for example, the number of links in social networks is growing every millisecond. Machine/Deep learning plays a significant role for technological accomplishments to work with big data in modern …

Ocean Wave Prediction And Characterization For Intelligent Maritime Transportation, Pujan Pokhrel

LSU New Orleans Theses and Dissertations

The national Earth System Prediction (ESPC) initiative aims to develop the predictions
for the next generation predictions of atmosphere, ocean, and sea-ice interactions in the scale of days to decades. This dissertation seeks to demonstrate the methods we can use to improve the ESPC models, especially the ocean prediction model. In the application side of the weather forecasts, this dissertation explores imitation learning with constraints to solve combinatorial optimization problems, focusing on the weather routing of surface vessels. Prediction of ocean waves is essential for various purposes, including vessel routing, ocean energy harvesting, agriculture, etc. Since the machine learning approaches …

Building Consensus For A Shared Definition Of Adverse Events: A Case Study In The Profession Of Dentistry, Amy Franklin, Elsbeth Kalenderian, Nutan Hebballi, Veronique Delattre, Jini Etoule, Joel White, Ram Vaderhobli, Denice Stewart, Karla Kent, Alfa Yansane, Muhammad Walji

Faculty, Staff and Student Publications

BACKGROUND: To achieve high-quality health care, adverse events (AEs) must be proactively recognized and mitigated. However, there is often ambiguity in applying guidelines and definitions. We describe the iterative calibration process needed to achieve a shared definition of AEs in dentistry. Our alignment process includes both independent and consensus building approaches.

OBJECTIVE: We explore the process of defining dental AEs and the steps necessary to achieve alignment across different care providers.

METHODS: Teams from 4 dental institutions across the United States iteratively reviewed patient records after identification of charts using an automated trigger tool. Calibration across teams was supported through …

Solving The Challenges Of Concept Drift In Data Stream Classification., Hanqing Hu

Electronic Theses and Dissertations

The rise of network connected devices and applications leads to a significant increase in the volume of data that are continuously generated overtime time, called data streams. In real world applications, storing the entirety of a data stream for analyzing later is often not practical, due to the data stream’s potentially infinite volume. Data stream mining techniques and frameworks are therefore created to analyze streaming data as they arrive. However, compared to traditional data mining techniques, challenges unique to data stream mining also emerge, due to the high arrival rate of data streams and their dynamic nature. In this dissertation, …

Predicting Order Status Using Xgboost, Kegan J. Penovich

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Invista, a Koch subsidiary, is a multinational producer of fibers, resins, and intermediaries, particularly nylon. To keep the company operating required them to take over 1.5 million orders over the course of - years, less than a third of which arrived on-time. Orders arriving other than when expected can cause many problems for any company. While arriving late is a clear problem, it also troublesome for them to arrive early. In the face of this, it becomes important to be able to tell a-priori if an order will arrive on-time or not.

To address this problem, we made use of …

Estimating The Health Effects Of Adding Bicycle And Pedestrian Paths At The Census Tract Level: Multiple Model Comparison, Ross J. Gore, Christopher Lynch, Craig Jordan, Andrew Collins, R. Michael Robinson, Gabrielle Fuller, Pearson Ames, Prateek Keerthi, Yash Kandukuri

VMASC Publications

Background: Adding additional bicycle and pedestrian paths to an area can lead to improved health outcomes for residents over time. However, quantitatively determining which areas benefit more from bicycle and pedestrian paths, how many miles of bicycle and pedestrian paths are needed, and the health outcomes that may be most improved remain open questions.

Objective: Our work provides and evaluates a methodology that offers actionable insight for city-level planners, public health officials, and decision makers tasked with the question “To what extent will adding specified bicycle and pedestrian path mileage to a census tract improve residents’ health outcomes over time?” …

Knowledge Representation And Management: Notable Contributions In 2021, Licong Cui, Ferdinand Dhombres, Jean Charlet

Faculty, Staff and Student Publications

OBJECTIVES: To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2021.

METHODS: Following the International Medical Informatics Association (IMIA) Yearbook guidelines, a comprehensive and standardized review of the biomedical informatics literature was performed to select the best KRM papers published in 2021, based on PubMed queries.

RESULTS: A total of 1,231 publications were retrieved from PubMed. We nominated 15 candidate best papers, and four of them were finally selected as the best papers in the KRM section. The topics covered by these papers include knowledge graph, ontology development, ontology alignment, …