Digital Commons Network^™

On Performance Optimization And Prediction Of Parallel Computing Frameworks In Big Data Systems, Haifa Alquwaiee

Dissertations

A wide spectrum of big data applications in science, engineering, and industry generate large datasets, which must be managed and processed in a timely and reliable manner for knowledge discovery. These tasks are now commonly executed in big data computing systems exemplified by Hadoop based on parallel processing and distributed storage and management. For example, many companies and research institutions have developed and deployed big data systems on top of NoSQL databases such as HBase and MongoDB, and parallel computing frameworks such as MapReduce and Spark, to ensure timely data analyses and efficient result delivery for decision making and business …

Statistics-Based Anomaly Detection And Correction Method For Amazon Customer Reviews, Ishani Chatterjee

Dissertations

People nowadays use the Internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source of gathering information for data analytics, sentiment analysis, natural language processing, etc. The most critical challenge is interpreting this data and capturing the sentiment behind these expressions. Sentiment analysis is analyzing, processing, concluding, and inferencing subjective texts with the views. Companies use sentiment analysis to understand public opinions, perform market research, analyze brand reputation, recognize customer experiences, and study social media influence. According to the different needs for aspect granularity, …

On Resource-Efficiency And Performance Optimization In Big Data Computing And Networking Using Machine Learning, Wuji Liu

Dissertations

Due to the rapid transition from traditional experiment-based approaches to large-scale, computational intensive simulations, next-generation scientific applications typically involve complex numerical modeling and extreme-scale simulations. Such model-based simulations oftentimes generate colossal amounts of data, which must be transferred over high-performance network (HPN) infrastructures to remote sites and analyzed against experimental or observation data on high-performance computing (HPC) facility. Optimizing the performance of both data transfer in HPN and simulation-based model development on HPC is critical to enabling and accelerating knowledge discovery and scientific innovation. However, such processes generally involve an enormous set of attributes including domain-specific model parameters, network transport …

Parameter Estimation And Inference Of Spatial Autoregressive Model By Stochastic Gradient Descent, Gan Luan

Dissertations

Stochastic gradient descent (SGD) is a popular iterative method for model parameter estimation in large-scale data and online learning settings since it goes through the data in only one pass. While SGD has been well studied for independent data, its application to spatially-correlated data largely remains unexplored. This dissertation develops SGD-based parameter estimation and statistical inference algorithms for the spatial autoregressive (SAR) model, a common model for spatial lattice data.

This research contains three parts. (I) The first part concerns SGD estimation and inference for the SAR mean regression model. A new SGD algorithm based on maximum likelihood estimator (MLE) …

Machine Learning And Computer Vision In Solar Physics, Haodi Jiang

Dissertations

In the recent decades, the difficult task of understanding and predicting violent solar eruptions and their terrestrial impacts has become a strategic national priority, as it affects the life of human beings, including communication, transportation, the power grid, national defense, space travel, and more. This dissertation explores new machine learning and computer vision techniques to tackle this difficult task. Specifically, the dissertation addresses four interrelated problems in solar physics: magnetic flux tracking, fibril tracing, Stokes inversion and vector magnetogram generation.

First, the dissertation presents a new deep learning method, named SolarUnet, to identify and track solar magnetic flux elements in …

Long Term Predictive Modeling On Big Spatio-Temporal Data, Yong Zhuang

Graduate Doctoral Dissertations

In the era of massive data, one of the most promising research fields involves the analysis of large-scale Spatio-temporal databases to discover exciting and previously unknown but potentially useful patterns from data collected over time and space. A modeling process in this domain must take temporal and spatial correlations into account, but with the dimensionality of the time and space measurements increasing, the number of elements potentially contributing to a target sharply grows, making the target's long-term behavior highly complex, chaotic, highly dynamic, and hard to predict. Therefore, two different considerations are taken into account in this work: one is …

Energy Consumption Forecasting Using Machine Learning, Mahdi Mohammadigohari

Theses

Forecasting electricity demand and consumption accurately is critical to the optimal and costeffective operation system, providing a competitive advantage to companies. In working with seasonal data and external variables, the traditional time-series forecasting methods cannot be applied to electricity consumption data. In energy planning for a generating company, accurate power forecasting for the electrical consumption prediction, as a technique, to understand and predict the market electricity demand is of paramount importance. Their power production can be adjusted accordingly in a deregulated market. As data type is seasonal, Persistence Models (Naïve Models), Seasonal AutoRegressive Integrated Moving Averages with eXogenous regressors (SARIMAX), …

Sentiment Analysis Of News Tweets, Haya Fathim

Theses

Sentiment Analysis is a process of extracting information from a large amount of data and classifying them into different classes called sentiments. Python is a simple yet powerful, high-level, interpreted, and dynamic programming language, which is well known for its functionality of processing natural language data by using NLTK (Natural Language Toolkit). NLTK is a library of python, which provides a base for building programs and classification of data. NLTK also provides a graphical demonstration for representing various results or trends and it also provides sample data to train and test various classifiers respectively. Sentiment classification aims to automatically predict …

Exploiting Building Demand Flexibility Through Machine Learning For Building-To-Grid Integration, Hannah Charlene Fontenot

Dissertations - ALL

Demand flexibility – the ability to adjust a building's load profile across different timescales – is a key aspect of the ongoing effort to increase interconnectivity between buildings and the power grid. By harnessing their demand flexibility, buildings can provide significant benefits to the grid and bolster grid resilience and reliability. To facilitate the transition toward the "smart grid", new and intelligent control approaches are required that can seamlessly integrate building, occupant, and grid data and effectively control multiple building assets to provide grid services while maintaining occupants' required thermal comfort levels and reducing the building's overall energy consumption and …

Correlating Sentiment In Reddit’S Wallstreetbets With The Stock Market Using Machine Learning Techniques, Sultan Ali Alzaabi

Theses

The issue that this study addresses is to observe whether there exists a statistical relation between the stock market and Reddit’s wallstreetbets. Previous research mainly focused on the relation between the stock market and Twitter. To gather data for the study, comments were scrapped from the subreddit wallstreetbets for a period of four months, Jan 1, 2021, till April 30, 2021. Different sentiment classifiers were, then, applied on a sample of the data to observe the most accurate classifier for the study. The study concluded that the most accurate sentiment classifier was an SVM classifier trained on 80% of Reddit …

A Framework For Characterising Performance In Multi-Class Classification Problems With Applications In Cancer Single Cell Rna Sequencing, Erik R. Christensen

Electronic Thesis and Dissertation Repository

In many real-world scenarios, we need to use multi-class classifiers to properly identify all classes in a dataset. To evaluate performance of multi-class classifiers, we need to take various parameters into account. I created a framework that can be used to drill into the differences between algorithms in specific scenarios and better compare multiple classifiers. This allows researchers to better identify strengths and weaknesses of particular classifiers. Single-cell RNA-seq allows cancer researchers to define complex cell types (i.e. classes) in the tumour micro-environments (TME). Using eight datasets, I assessed performance of 26 methods from different perspectives, such as the ability …

Deep Learning For Automatic Microscopy Image Analysis, Shenghua He

McKelvey School of Engineering Theses & Dissertations

Microscopy imaging techniques allow for the creation of detailed images of cells (or nuclei) and have been widely employed for cell studies in biological research and disease diagnosis in clinic practices.Microscopy image analysis (MIA), with tasks of cell detection, cell classification, and cell counting, etc., can assist with the quantitative analysis of cells and provide useful information for a cellular-level understanding of biological activities and pathology. Manual MIA is tedious, time-consuming, prone to subject errors, and are not feasible for the high-throughput cell analysis process. Thus, automatic MIA methods can facilitate all kinds of biological studies and clinical tasks. Conventional …

Infant Cry Signal Processing, Analysis, And Classification With Artificial Neural Networks, Chunyan Ji

Computer Science Dissertations

As a special type of speech and environmental sound, infant cry has been a growing research area covering infant cry reason classification, pathological infant cry identification, and infant cry detection in the past two decades. In this dissertation, we build a new dataset, explore new feature extraction methods, and propose novel classification approaches, to improve the infant cry classification accuracy and identify diseases by learning infant cry signals.

We propose a method through generating weighted prosodic features combined with acoustic features for a deep learning model to improve the performance of asphyxiated infant cry identification. The combined feature matrix captures …

Predictive Computational Materials Modeling With Machine Learning: Creating The Next Generation Of Atomistic Potential Using Neural Networks, Mashroor Shafat Nitol

Theses and Dissertations

Machine learning techniques using artificial neural networks (ANNs) have proven to be effective tools to rapidly mimic first principles calculations. These tools are capable of sub meV/atom accuracy while operating with linear scaling with respect to the system size. Here novel interatomic potentials are constructed based on the rapid artificial neural network (RANN) formalism. This approach generates precise force fields for various metals that have historically been difficult to describe at the atomic scale. These force fields can be utilized in molecular dynamics simulations to provide new physical insights. The RANN formalism, which is incorporated into a LAMMPS molecular dynamics …

Detecting Malware In Memory With Memory Object Relationships, Demarcus M. Thomas Sr.

Theses and Dissertations

Malware is a growing concern that not only affects large businesses but the basic consumer as well. As a result, there is a need to develop tools that can identify the malicious activities of malware authors. A useful technique to achieve this is memory forensics. Memory forensics is the study of volatile data and its structures in Random Access Memory (RAM). It can be utilized to pinpoint what actions have occurred on a computer system.

This dissertation utilizes memory forensics to extract relationships between objects and supervised machine learning as a novel method for identifying malicious processes in a system …

Using A Systemic Skills Model To Build An Effective 21st Century Workforce: Factors That Impact The Ability To Navigate Complex Systems, Morteza Nagahi

Theses and Dissertations

The growth of technology and the proliferation of information made modern complex systems more fragile and vulnerable. As a result, competitive advantage is no longer achieved exclusively through strategic planning but by developing an influential cadre of technical people who can efficiently manage and navigate modern complex systems. The dissertation aims to provide educators, practitioners, and organizations with a model that helps to measure individuals’ systems thinking skills, complex problem solving, personality traits, and the impacting demographic factors such as managerial and work experience, current occupation type, organizational ownership structure, and education level. The intent is to study how these …

A Machine Learning Method For The Prediction Of Melt Pool Geometries Created By Laser Powder Bed Fusion, Jonathan Ciaccio

University of New Orleans Theses and Dissertations

A machine learning model is created to predict melt pool geometries of Ti-6Al-4V alloy created by the laser powder bed fusion process. Data is collected through an extensive literature survey, using results from both experiments and CFD modeling. The model focuses on five key input parameters that influence melt pool geometries: laser power, scanning speed, spot size, powder density, and powder layer thickness. The two outputs of the model are melt pool width and melt pool depth. The model is trained and tested by using the k fold cross validation technique. Multiple regression models are then applied to find the …

Machine-Learning-Based Approach To Decoding Physiological And Neural Signals, Elnaz Lashgari

Computational and Data Sciences (PhD) Dissertations

In recent years, machine learning algorithms have been developing rapidly, becoming increasingly powerful tools in decoding physiological and neural signals. The aim of this dissertation is to develop computational tools, and especially machine learning techniques, to identify the most effective methods for feature extraction and classification of these signals. This is particularly challenging due to the highly non-linear, non-stationery, and artifact- and noise-prone nature of these signals.

Among basic human-control tasks, reaching and grasping are ubiquitous in everyday life. I investigated different linear and non-linear dimensionality reduction techniques for feature extraction and classification of electromyography (EMG) during a reach-grasp-lift task. …

Trajectory Generation For A Multibody Robotic System: Modern Methods Based On Product Of Exponentials, Aryslan Malik

Doctoral Dissertations and Master's Theses

This work presents several trajectory generation algorithms for multibody robotic systems based on the Product of Exponentials (PoE) formulation, also known as screw theory. A PoE formulation is first developed to model the kinematics and dynamics of a multibody robotic manipulator (Sawyer Robot) with 7 revolute joints and an end-effector.

In the first method, an Inverse Kinematics (IK) algorithm based on the Newton-Raphson iterative method is applied to generate constrained joint-space trajectories corresponding to straight-line and curvilinear motions of the end effector in Cartesian space with finite jerk. The second approach describes Constant Screw Axis (CSA) trajectories which are generated …

On Predicting Omnidirectional Honey Bee Traffic Using Weather And Electromagnetic Radiation, Daniel G. Hornberger

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Honey bees are responsible for pollinating many important crops in the United States. However, honey bee populations have declined significantly since 1961. While some causes of this decline are known, others are not. By utilizing electronic bee hive monitoring (EBM) systems, bee keepers and researchers have an added resource in determining the causes of these declines so that the issues can be remedied. For nearly five months (May through October) during the 2020 honey bee foraging season in Logan, Utah, USA, we collected on-site weather and electromagnetic radiation (EMR) readings and videos of the hive entrances of six bee hives …

Intelligent Resource Prediction For Hpc And Scientific Workflows, Benjamin Shealy

All Dissertations

Scientific workflows and high-performance computing (HPC) platforms are critically important to modern scientific research. In order to perform scientific experiments at scale, domain scientists must have knowledge and expertise in software and hardware systems that are highly complex and rapidly evolving. While computational expertise will be essential for domain scientists going forward, any tools or practices that reduce this burden for domain scientists will greatly increase the rate of scientific discoveries. One challenge that exists for domain scientists today is knowing the resource usage patterns of an application for the purpose of resource provisioning. A tool that accurately estimates these …

Quantum State Estimation And Tracking For Superconducting Processors Using Machine Learning, Shiva Lotfallahzadeh Barzili

Computational and Data Sciences (PhD) Dissertations

Quantum technology has been rapidly growing; in particular, the experiments that have been performed with superconducting qubits and circuit QED have allowed us to explore the light-matter interaction at its most fundamental level. The study of coherent dynamics between two-level systems and resonator modes can provide insight into fundamental aspects of quantum physics, such as how the state of a system evolves while being continuously observed. To study such an evolving quantum system, experimenters need to verify the accuracy of state preparation and control since quantum systems are very fragile and sensitive to environmental disturbance. In this thesis, I look …

Machine Learning For Lifespan Inference From Time-Lapse Microfluidic Images Of Dividing Yeast Cells, Mehran Ghafari

Masters Theses and Doctoral Dissertations

High-throughput microfluidics-based assays can potentially increase the speed and quality of yeast replicative lifespan measurements that are related to aging. One major challenge is to efficiently convert large volumes of time-lapse images into quantitative measurements of yeast cell lifespan measurements. To address these issues, we developed several deep learning methods to analyze a large number of images collected from microfluidic experiments. First, we compared three deep learning architectures to classify microfluidic time-lapse images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. Second, we evaluated convolutional neural networks for detecting cells from microfluidic …

Impacts Of Environmental And Anthropogenic Stressors On Amphibian Welfare, Diversity, And Distribution In The Upper Missouri River Basin, Kaitlyn Campbell

Dissertations and Theses

Climate change and anthropogenic stressors have contributed to rapid declines among various taxonomic groups; however, amphibian declines have been particularly intense and primarily stemmed from warming temperatures, habitat loss, exposure to contaminants, disease, and their subsequent interactions. Several climate mitigation strategies, like Bioenergy with Carbon Capture and Storage, have been proposed to alleviate the impact of rising temperatures; however, these proposals often fail to recognize and quantify the true impact on fauna, including changes in species distributions. To address this critical gap in knowledge, this research identified current amphibian distributions in the Upper Missouri River Basin and projected distribution changes …

Factors Influencing Intent To Take A Covid-19 Test In The United States, Sheila Rutto

Theses and Dissertations

In 2020, COVID-19 became the first pandemic in the world’s history that brought the entire world to an abrupt and unexpected halt. Since the first reported case of the disease to date, the novel coronavirus has been able to wreak havoc in literary every corner of the globe and left an ever-growing number of unprecedented fatalities. The normal way of life has been disrupted, and the level of uncertainty about the end of this pandemic continues to manifest to many. Due to the urgency to bring this pandemic under control, medical officers have been able to recommend actions that people …

Determining States Of Movement In Humans Using Minimally Processed Eeg Signals And Various Classification Methods, Maurice Barnett

All Theses

Electroencephalography (EEG) is a non-invasive technique used in both clinical and research settings to record neuronal signaling in the brain. The location of an EEG signal as well as the frequencies at which its neuronal constituents fire correlate with behavioral tasks, including discrete states of motor activity. Due to the number of channels and fine temporal resolution of EEG, a dense, high-dimensional dataset is collected. Transcranial direct current stimulation (tDCS) is a treatment that has been suggested to improve motor functions of Parkinson’s disease and chronic stroke patients when stimulation occurs during a motor task. tDCS is commonly administered without …

Using Custom Ner Models To Extract Dod Specific Entities From Contracts, Kayla P. Haberstich

Theses and Dissertations

The Air Force Sustainment Center collected 3.7 million contracts onto the Air Force Research Laboratory’s high power computers. They are in the format of a .pdf or scanned document, making them unstructured data. The Data Analytics Resource Team extracted the documents into a textual format for use in further analysis. This thesis looks to extract four DOD specific entities (NSN, Part Number, CAGE Code, and Supplier Name) from the contracts using custom NER models. This newly extracted information will allow the Air Force to identify what parts are supplied by which vendors. This information along with historical CLIN pricing for …

Camera-Based Deep Learning Ai Assistant System For Basketball Training, Guangkun Zeng

Theses

The YOLO, a Computer Vision Algorithms, is brought out to analyze the basketball player’s status as a dataset. It can record the players’ behavior on the court including dribbling, shooting, and running. In this way, the app could collect the field goal you made and missed. First, you should use this app to record a video of your shoot training. After that, the AI would analyze and brings out a 3d virtual diagram to interpret your performance. This diagram will show the hot zone and cold zone for your field goal. Also, the track of your ball will be displayed …

Network Traffic Analysis Using Local Outlier Factor, Khalifa Almheiri

Theses

The issue that this study addresses is the high rate of false positives, high maintenance, and lack of stability and precision that the existing network intrusion detection algorithm faces. To address this problem, we proposed a Local Outlier Factor (LOF) Algorithm that locates outliers and anomalies by comparing the deviation of one data point with respect to its neighbors. To gather data, we will use DARPA’s KDDCup99 as well as questions towards analysts. This data will help determine whether the LOF algorithm is more effective than existing solutions that are presented in the network intrusion detection space.

Developing Risk Assessment Tool For Patients’ In-Hospital Falls Using Predictive Modeling, Rasika Patil

Theses

Inpatient falls are a serious cause of fatal and non-fatal injuries among patients of all ages leading to disability and stillness. The post-fall treatment comes with rising medical costs and a stressful recovery phase. The present assessment tools align with analyzing causes of falls from historical data instead of present conditions. The key focus area of this research is to develop general-purpose fall risk assessment tools using machine learning-based predictive modeling. We used performance metrics to compare the accuracy and suggest the best suitable model for each shift. This general-purpose fall risk assessment tool can be used for all age …