Physical Sciences and Mathematics Commons^™

Multiple Imputation Of Missing Data In Structural Equation Models With Mediators And Moderators Using Gradient Boosted Machine Learning, Robert J. Milletich Ii

Psychology Theses & Dissertations

Mediation and moderated mediation models are two commonly used models for indirect effects analysis. In practice, missing data is a pervasive problem in structural equation modeling with psychological data. Multiple imputation (MI) is one method used to estimate model parameters in the presence of missing data, while accounting for uncertainty due to the missing data. Unfortunately, commonly used MI methods are not equipped to handle categorical variables or nonlinear variables such as interactions. In this study, we introduce a general MI framework that uses the Bayesian bootstrap (BB) method to generate posterior inferences for indirect effects and gradient boosted machine …

A Study Of The Impact Of Interaction Mechanisms And Population Diversity In Evolutionary Multiagent Systems, Sadat U. Chowdhury

Dissertations, Theses, and Capstone Projects

In the Evolutionary Computation (EC) research community, a major concern is maintaining optimal levels of population diversity. In the Multiagent Systems (MAS) research community, a major concern is implementing effective agent coordination through various interaction mechanisms. These two concerns coincide when one is faced with Evolutionary Multiagent Systems (EMAS).

This thesis demonstrates a methodology to study the relationship between interaction mechanisms, population diversity, and performance of an evolving multiagent system in a dynamic, real-time, and asynchronous environment. An open sourced extensible experimentation platform is developed that allows plug-ins for evolutionary models, interaction mechanisms, and genotypical encoding schemes beyond the one …

A Novel Machine Learning Classifier Based On A Qualia Modeling Agent (Qma), Sandra L. Vaughan

Theses and Dissertations

This dissertation addresses a problem found in supervised machine learning (ML) classification, that the target variable, i.e., the variable a classifier predicts, has to be identified before training begins and cannot change during training and testing. This research develops a computational agent, which overcomes this problem. The Qualia Modeling Agent (QMA) is modeled after two cognitive theories: Stanovich's tripartite framework, which proposes learning results from interactions between conscious and unconscious processes; and, the Integrated Information Theory (IIT) of Consciousness, which proposes that the fundamental structural elements of consciousness are qualia. By modeling the informational relationships of qualia, the QMA allows …

Creating And Automatically Grading Annotated Questions, Alicia Crowder Wood

Theses and Dissertations

We have created a question type that allows teachers to easily create questions, helps provide an intuitive user experience for students to take questions, and reduces the time it currently takes teachers to grade and provide feedback to students. This question type, or an "annotated" question, will allow teachers to test students' knowledge in a particular subject area by having students "annotate" or mark text and video sources to answer questions. Through user testing we determined that overall the interface and the implemented system decrease the time it would take a teacher to grade annotated quiz questions. However, there are …

Soft Confidence-Weighted Learning, Jialei Wang, Peilin Zhao, Hoi, Steven C. H.

Research Collection School Of Computing and Information Systems

Online learning plays an important role in many big datamining problems because of its high efficiency and scalability. In theliterature, many online learning algorithms using gradient information havebeen applied to solve online classification problems. Recently, more effectivesecond-order algorithms have been proposed, where the correlation between thefeatures is utilized to improve the learning efficiency. Among them,Confidence-Weighted (CW) learning algorithms are very effective, which assumethat the classification model is drawn from a Gaussian distribution, whichenables the model to be effectively updated with the second-order informationof the data stream. Despite being studied actively, these CW algorithms cannothandle nonseparable datasets and noisy datasets very …

Real Time Activity Recognition Of Treadmill Usage Via Machine Learning, Nathan Blank, Matt Buckner, Christian Owen, Anna Scott

Rose-Hulman Undergraduate Research Publications

Our objective is to provide real-time classification of treadmill usage patterns based on accelerometer and magnetometer measurements. We collected data from treadmills in the Rose-Hulman Student Recreation Center (SRC) using Shimmer3 sensor units. We identified useful data features and classifiers for predicting treadmill usage patterns. We also prototyped a proof of concept wireless, real-time classification system.

Prediction And Optimal Scheduling Of Advertisements In Linear Television, Mark J. Panaggio, Pak-Wing Fok, Ghan S. Bhatt, Simon Burhoe, Michael Capps, Christina J. Edholm, Fadoua El Moustaid, Tegan Emerson, Star-Lena Estock, Nathan Gold, Ryan Halabi, Madelyn Houser, Peter R. Kramer, Hsuan-Wei Lee, Qingxia Li, Weiqiang Li, Dan Lu, Yuzhou Qian, Louis F. Rossi, Deborah Shutt, Vicky Chuqiao Yang, Yingxiang Zhou

Mathematical Sciences Faculty Research

Advertising is a crucial component of marketing and an important way for companies to raise awareness of goods and services in the marketplace. Advertising campaigns are designed to convey a marketing image or message to an audience of potential consumers and television commercials can be an effective way of transmitting these messages to a large audience. In order to meet the requirements for a typical advertising order, television content providers must provide advertisers with a predetermined number of "impressions" in the target demographic. However, because the number of impressions for a given program is not known a priori and because …

Classifying Pattern Formation In Materials Via Machine Learning, Lukasz Burzawa, Shuo Liu, Erica W. Carlson

The Summer Undergraduate Research Fellowship (SURF) Symposium

Scanning probe experiments such as scanning tunneling microscopy (STM) and atomic force microscopy (AFM) on strongly correlated materials often reveal complex pattern formation that occurs on multiple length scales. We have shown in two disparate correlated materials that the pattern formation is driven by proximity to a disorder-driven critical point. We developed new analysis concepts and techniques that relate the observed pattern formation to critical exponents by analyzing the geometry and statistics of clusters observed in these experiments and converting that information into critical exponents. Machine learning algorithms can be helpful correlating data from scanning probe experiments to theoretical models …

Personalization And Data Relation Exploration Using Predictive Analytics For The Production And Distributed Analysis System (Panda), Mikhail Titov

Computer Science and Engineering Dissertations - Archive

Efficient data distribution among computing centers is one of the biggest challenges in large-scale scientific distributed computing systems. Such data distribution issues include: i) the rational utilization of storage and computing resources, ii) the minimization of the completion time for data processing (which requires a reduction in redundant data transfers, and intelligent allocation of processing tasks), and iii) user experience enhancement, i.e., availability and fast access to the desired data, and discovery of new relevant data. In the literature and in practice, there have been significant new approaches to the improvement of workflow management to address the above described issues, …

A Study Of Security Issues Of Mobile Apps In The Android Platform Using Machine Learning Approaches, Lei Cen

Open Access Dissertations

Mobile app poses both traditional and new potential threats to system security and user privacy. There are malicious apps that may do harm to the system, and there are mis-behaviors of apps, which are reasonable and legal when not abused, yet may lead to real threats otherwise. Moreover, due to the nature of mobile apps, a running app in mobile devices may be only part of the software, and the server side behavior is usually not covered by analysis. Therefore, direct analysis on the app itself may be incomplete and additional sources of information are needed. In this dissertation, we …

Data Driven Low-Bandwidth Intelligent Control Of A Jet Engine Combustor, Nathan L. Toner

Open Access Dissertations

This thesis introduces a low-bandwidth control architecture for navigating the input space of an un-modeled combustor system between desired operating conditions while avoiding regions of instability and blow-out. An experimental procedure is discussed for identifying regions of instability and gathering sufficient data to build a data-driven model of the system's operating modes. Regions of instability and blow-out are identified experimentally and a data-driven operating point classifier is designed. This classifier acts as a map of the operating space of the combustor, indicating regions in which the flame is in a "good" or "bad" operating mode. A data-driven predictor is also …

Learning From Data: Plant Breeding Applications Of Machine Learning, Alencar Xavier

Open Access Dissertations

Increasingly, new sources of data are being incorporated into plant breeding pipelines. Enormous amounts of data from field phenomics and genotyping technologies places data mining and analysis into a completely different level that is challenging from practical and theoretical standpoints. Intelligent decision-making relies on our capability of extracting from data useful information that may help us to achieve our goals more efficiently. Many plant breeders, agronomists and geneticists perform analyses without knowing relevant underlying assumptions, strengths or pitfalls of the employed methods. The study endeavors to assess statistical learning properties and plant breeding applications of supervised and unsupervised machine learning …

Social Sentiment And Stock Trading Via Mobile Phones, Kwansoo Kim, Sang Yong Lee, Robert John Kauffman

Research Collection School Of Computing and Information Systems

What happens when uninformed investors trade stocks via mobile phones? Do they react to social sentiment differently than more informed traders in traditional trading? Based on 16,817 data observations and econometric analysis for the trading of 251 equities in Korea over 39 days, we present evidence of herding behavior among uninformed traders in the mobile channel. The results indicate that mobile traders seem more easily swayed by changing social sentiment. In addition, stock trading in the traditional channel probably influences sentiment formation in the market overall. Mobile traders follow signals in social media suggesting that they engage in less beneficial …

Lidar-Assisted Extraction Of Old Growth Baldcypress Stands Along The Black River Of North Carolina, Weston Pierce Murch

Graduate Theses and Dissertations

The remnants of ancient baldcypress forests continue to grow across the Southeastern United States. These long lived trees are invaluable for biodiversity along riverine ecosystems, provide habitat to a myriad of animal species, and augment the proxy climate record for North America. While extensive logging of the areas along the Black River in North Carolina has mostly decimated ancient forests of many species including the baldcypress, conservation efforts from The Nature Conservancy and other partners are under way. In order to more efficiently find and study these enduring stands of baldcypress, some of which are estimated to be more than …

A Framework For Collecting, Extracting And Managing Event Identity Information From Textual Content In Social Media, Debanjan Mahata

Theses and Dissertations

With the popularity of social media platforms such as Facebook, Twitter and Google Plus, there has been voluminous growth in the digital footprints of real-life events on the Internet. The user-generated colloquial and concise textual content related to different types of real-life events, available in these websites, acts as an extremely useful source for researchers and organizations for extracting valuable and insightful information. There has been significant improvement in natural language processing techniques for mining formal and long textual content commonly found in newspapers. It is still a challenging task to mine textual information from the social media channels producing …

Designing Human-Centered Collective Intelligence, Ivor Addo

Dissertations (1934 -)

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting …

Machine Learning Methods For Brain Image Analysis, Ahmed Fakhry

Computer Science Theses & Dissertations

Understanding how the brain functions and quantifying compound interactions between complex synaptic networks inside the brain remain some of the most challenging problems in neuroscience. Lack or abundance of data, shortage of manpower along with heterogeneity of data following from various species all served as an added complexity to the already perplexing problem. The ability to process vast amount of brain data need to be performed automatically, yet with an accuracy close to manual human-level performance. These automated methods essentially need to generalize well to be able to accommodate data from different species. Also, novel approaches and techniques are becoming …

Machine Learning Methods For Medical And Biological Image Computing, Rongjian Li

Computer Science Theses & Dissertations

Medical and biological imaging technologies provide valuable visualization information of structure and function for an organ from the level of individual molecules to the whole object. Brain is the most complex organ in body, and it increasingly attracts intense research attentions with the rapid development of medical and bio-logical imaging technologies. A massive amount of high-dimensional brain imaging data being generated makes the design of computational methods for efficient analysis on those images highly demanded. The current study of computational methods using hand-crafted features does not scale with the increasing number of brain images, hindering the pace of scientific discoveries …

Where Is The Goldmine? Finding Promising Business Locations Through Facebook Data Analytics, Jovian Lin, Richard Oentaryo, Ee-Peng Lim, Casey Vu, Adrian Vu, Agus Kwee

Research Collection School Of Computing and Information Systems

If you were to open your own cafe, would you not want to effortlessly identify the most suitable location to set up your shop? Choosing an optimal physical location is a critical decision for numerous businesses, as many factors contribute to the final choice of the location. In this paper, we seek to address the issue by investigating the use of publicly available Facebook Pages data-which include user "check-ins", types of business, and business locations-to evaluate a user-selected physical location with respect to a type of business. Using a dataset of 20,877 food businesses in Singapore, we conduct analysis of …

Determining The Effectiveness Of Soil Treatment On Plant Stress Using Smart-Phone Cameras, Anurag Panwar

USF Tampa Graduate Theses and Dissertations

Plants are vital to the health of our biosphere, and effectively sustaining their growth is fundamental to the existence of life on this planet. A critical aspect, which decides the sustainability of plant growth is the quality of soil. All other things being fixed, the quality of soil greatly impacts the plant stress, which in turn impacts overall health. Although plant stress manifests in many ways, one of the clearest indicators are colors of the leaves. In this thesis, we conducted an experimental study in a greenhouse for detecting plant stress caused by nutrient deficienceies in soil using smartphone cameras, …

Machine Learning For Disease Prediction, Abraham Jacob Frandsen

Theses and Dissertations

Millions of people in the United States alone suffer from undiagnosed or late-diagnosed chronic diseases such as Chronic Kidney Disease and Type II Diabetes. Catching these diseases earlier facilitates preventive healthcare interventions, which in turn can lead to tremendous cost savings and improved health outcomes. We develop algorithms for predicting disease occurrence by drawing from ideas and techniques in the field of machine learning. We explore standard classification methods such as logistic regression and random forest, as well as more sophisticated sequence models, including recurrent neural networks. We focus especially on the use of medical code data for disease prediction, …

Categorizing Blog Spam, Brandon Bevans

Master's Theses

The internet has matured into the focal point of our era. Its ecosystem is vast, complex, and in many regards unaccounted for. One of the most prevalent aspects of the internet is spam. Similar to the rest of the internet, spam has evolved from simply meaning ‘unwanted emails’ to a blanket term that encompasses any unsolicited or illegitimate content that appears in the wide range of media that exists on the internet.

Many forms of spam permeate the internet, and spam architects continue to develop tools and methods to avoid detection. On the other side, cyber security engineers continue to …

Exploring Data Mining Techniques For Tree Species Classification Using Co-Registered Lidar And Hyperspectral Data, Julia K. Marrs

Theses and Dissertations

NASA Goddard’s LiDAR, Hyperspectral, and Thermal imager provides co-registered remote sensing data on experimental forests. Data mining methods were used to achieve a final tree species classification accuracy of 68% using a combined LiDAR and hyperspectral dataset, and show promise for addressing deforestation and carbon sequestration on a species-specific level.

A General Framework Of Large-Scale Convex Optimization Using Jensen Surrogates And Acceleration Techniques, Soysal Degirmenci

McKelvey School of Engineering Graduate Student Theses & Dissertations

In a world where data rates are growing faster than computing power, algorithmic acceleration based on developments in mathematical optimization plays a crucial role in narrowing the gap between the two. As the scale of optimization problems in many fields is getting larger, we need faster optimization methods that not only work well in theory, but also work well in practice by exploiting underlying state-of-the-art computing technology.

In this document, we introduce a unified framework of large-scale convex optimization using Jensen surrogates, an iterative optimization method that has been used in different fields since the 1970s. After this general treatment, …

Revelation Of Yin-Yang Balance In Microbial Cell Factories By Data Mining, Flux Modeling, And Metabolic Engineering, Gang Wu

McKelvey School of Engineering Graduate Student Theses & Dissertations

The long-held assumption of never-ending rapid growth in biotechnology and especially in synthetic biology has been recently questioned, due to lack of substantial return of investment. One of the main reasons for failures in synthetic biology and metabolic engineering is the metabolic burdens that result in resource losses. Metabolic burden is defined as the portion of a host cells resources either energy molecules (e.g., NADH, NADPH and ATP) or carbon building blocks (e.g., amino acids) that is used to maintain the engineered components (e.g., pathways). As a result, the effectiveness of synthetic biology tools heavily dependents on cell capability to …

Comparison Of Machine Learning Algorithms In Suggesting Candidate Edges To Construct A Query On Heterogeneous Graphs, Rohit Ravi Kumar Bhoopalam

Computer Science and Engineering Theses - Archive

Querying graph data can be difficult as it requires the user to have knowledge of the underlying schema and the query language. Visual query builders allow users to formulate the intended query by drawing nodes and edges of the query graph, which can be translated into a database query. Visual query builders help users formulate the query without requiring the user to have knowledge of the query language and the underlying schema. To the best of our knowledge, none of the currently available visual query builders suggest users what nodes/edges to include into their query graph. We provide suggestions to …

Data Driven Sample Generator Model With Application To Classification, Alvaro Emilio Ulloa Cerna

Mathematics & Statistics ETDs

Despite the rapidly growing interest, progress in the study of relations between physiological abnormalities and mental disorders is hampered by complexity of the human brain and high costs of data collection. The complexity can be captured by machine learning approaches, but they still may require significant amounts of data. In this thesis, we seek to mitigate the latter challenge by developing a data driven sample generator model for the generation of synthetic realistic training data. Our method greatly improves generalization in classification of schizophrenia patients and healthy controls from their structural magnetic resonance images. A feed forward neural network trained …

Exploring Privacy Leakage From The Resource Usage Patterns Of Mobile Apps, Amin Rois Sinung Nugroho

Graduate Theses and Dissertations

Due to the popularity of smart phones and mobile apps, a potential privacy risk with the usage of mobile apps is that, from the usage information of mobile apps (e.g., how many hours a user plays mobile games in each day), private information about a user’s living habits and personal activities can be inferred. To assess this risk, this thesis answers the following research question: can the type of a mobile app (e.g., email, web browsing, mobile game, music streaming, etc.) used by a user be inferred from the resource (e.g., CPU, memory, network, etc.) usage patterns of the mobile …

A Comparative Approach To Question Answering Systems, Josue Balandrano Coronel

Theses and Dissertations

In this paper I will analyze three different algorithms and approaches to implement Question Answering Systems (QA-Systems). I will analyze the efficiency, strengths, and weaknesses of multiple algorithms by explaining them in detail and comparing them with each other. The overarching aim of this thesis is to explore ideas that can be used to create a truly open context QA-System. Open context QA-Systems remain an open problem.

The various algorithms and approaches presented in this work will be focused on complex questions. Complex questions are usually verbose and the context of the question is equally important to answer the query …

Mobile Big Data Analytics Using Deep Learning And Apache Spark, Mohammad Abu Alsheikh, Dusit Niyato, Shaowei Lin, Hwee-Pink Tan, Zhu Han

Research Collection School Of Computing and Information Systems

The proliferation of mobile devices, such as smartphones and Internet of Things gadgets, has resulted in the recent mobile big data era. Collecting mobile big data is unprofitable unless suitable analytics and learning methods are utilized to extract meaningful information and hidden patterns from data. This article presents an overview and brief tutorial on deep learning in mobile big data analytics and discusses a scalable learning framework over Apache Spark. Specifically, distributed deep learning is executed as an iterative MapReduce computing on many Spark workers. Each Spark worker learns a partial deep model on a partition of the overall mobile, …