Digital Commons Network^™

Bikebot: A Study In Reinforcement Learning, April L. Kutz, Kristen A. Brennan, Jeremy Dixon, Nathan Bogdonas

Honors Capstones

In the United States, there is a lack of young adults interested in studying Science, Technology, Engineering, and Math (STEM). One way to help spark interest in STEM is a classroom aid that demonstrates how STEM can be fun and how it is used to push the limits of modern technology. BikeBot was designed for just this purpose. BikeBot is a robotic bicycle that utilizes an algorithm called Q-learning. Q-Learning is a type of reinforcement machine learning program, a basic form of artificial intelligence, that will allow BikeBot to balance without any explicit controls. The algorithm was tested and proven …

Graph-Based Data Modeling And Analysis For Data Fusion In Remote Sensing, Lei Fan

Theses

Hyperspectral imaging provides the capability of increased sensitivity and discrimination over traditional imaging methods by combining standard digital imaging with spectroscopic methods. For each individual pixel in a hyperspectral image (HSI), a continuous spectrum is sampled as the spectral reflectance/radiance signature to facilitate identification of ground cover and surface material. The abundant spectrum knowledge allows all available information from the data to be mined. The superior qualities within hyperspectral imaging allow wide applications such as mineral exploration, agriculture monitoring, and ecological surveillance, etc. The processing of massive high-dimensional HSI datasets is a challenge since many data processing techniques have a …

Analog Spiking Neuromorphic Circuits And Systems For Brain- And Nanotechnology-Inspired Cognitive Computing, Xinyu Wu

Boise State University Theses and Dissertations

Human society is now facing grand challenges to satisfy the growing demand for computing power, at the same time, sustain energy consumption. By the end of CMOS technology scaling, innovations are required to tackle the challenges in a radically different way. Inspired by the emerging understanding of the computing occurring in a brain and nanotechnology-enabled biological plausible synaptic plasticity, neuromorphic computing architectures are being investigated. Such a neuromorphic chip that combines CMOS analog spiking neurons and nanoscale resistive random-access memory (RRAM) using as electronics synapses can provide massive neural network parallelism, high density and online learning capability, and hence, paves …

Using Machine Learning To Predict Student Achievement On The State Of Texas Assessment Of Academic Readiness Examination In Charter Schools, Christopher D. Gonzalez

Theses and Dissertations

The purpose of this study was to research and develop a way to use machine learning algorithms (MLAs) to predict student achievement on the State of Texas Assessment of Academic Readiness (STAAR), specifically in the charter school setting. Charter schools have the disadvantage of a constant influx in students, so providing historical student data in order to analyze trends proves difficult. This study expands on previous research done on students in secondary and post-secondary school and determining features that indicate success in these settings. The data used is from the district of IDEA Public Schools who focuses on providing education …

Psychophysiological Analysis Of A Pedagogical Agent And Robotic Peer For Individuals With Autism Spectrum Disorders., Mohammad Nasser Saadatzi

Electronic Theses and Dissertations

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, …

Collective Personalized Change Classification With Multiobjective Search, Xin Xia, David Lo, Xinyu Wang, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Many change classification techniques have been proposed to identify defect-prone changes. These techniques consider all developers' historical change data to build a global prediction model. In practice, since developers have their own coding preferences and behavioral patterns, which causes different defect patterns, a separate change classification model for each developer can help to improve performance. Jiang, Tan, and Kim refer to this problem as personalized change classification, and they propose PCC+ to solve this problem. A software project has a number of developers; for a developer, building a prediction model not only based on his/her change data, but also on …

Adaptive Classification Framework For Control Myoelectric Prostheses During Muscle Fatigue, Hamdi Allbunashee Allbunashee

Theses and Dissertations

A myoelectric-controlled prosthesis holds the promise of renewed quality of life for the amputee population. When it comes to prosthesis control, muscle fatigue presents a significant challenge for all task discrimination algorithms due to changed muscle activation patterns and significant shifts in the spectral contents of the surface electromyogram (sEMG) signals. The effect of muscle fatigue on the classification accuracy of two supervised machine-learning algorithms, i.e. the linear discriminant analysis (LDA), and the muscle synergy based task discrimination (MSD) was investigated. Due to muscle fatigue, the classification accuracies declined from (>90%) to 41% and 59% for the LDA and …

An Exploration Of Style Transfer Using Deep Neural Networks, Cameron Y. Smith

Computer Science ETDs

Convolutional Neural Networks and Graphics Processing Units have been at the core of a paradigm shift in computer vision research that some researchers have called `'the algorithmic perception revolution." This thesis presents the implementation and analysis of several techniques for performing artistic style transfer using a Convolutional Neural Network architecture trained for large-scale image recognition tasks. We present an implementation of an existing algorithm for artistic style transfer in images and video. The neural algorithm separates and recombines the style and content of arbitrary images. Additionally, we present an extension of the algorithm to perform weighted artistic style transfer.

Approaches For Capturing Time-Varying Functional Network Connectivity With Application To Normative Development And Mental Illness, Barnaly Rashid

Electrical and Computer Engineering ETDs

Since the beginning of medical science, the human brain has remained an unsolved puzzle; an illusive organ that controls everything- from breathing to heartbeats, from emotion to anger, and more. With the power of advanced neuroimaging techniques, scientists have now started to solve this nearly impossible puzzle, piece by piece. Over the past decade, various in vivo techniques, including functional magnetic resonance imaging (fMRI), have been increasingly used to understand brain functions. fMRI is extensively being used to facilitate the identification of various neuropsychological disorders such as schizophrenia (SZ), bipolar disorder (BP) and autism spectrum disorder (ASD). These disorders are …

Spiteful, One-Off, And Kind: Predicting Customer Feedback Behavior On Twitter, Agus Sulistya, Abhishek Sharma, David Lo

Research Collection School Of Computing and Information Systems

Social media provides a convenient way for customers to express their feedback to companies. Identifying different types of customers based on their feedback behavior can help companies to maintain their customers. In this paper, we use a machine learning approach to predict a customer’s feedback behavior based on her first feedback tweet. First, we identify a few categories of customers based on their feedback frequency and the sentiment of the feedback. We identify three main categories: spiteful, one-off, and kind. Next, we build a model to predict the category of a customer given her first feedback. We use profile and …

Comparing Machine Learning And Logistic Regression Methods For Predicting Hypertension Using A Combination Of Gene Expression And Next-Generation Sequencing Data, Elizabeth Held, Joshua Cape, Nathan L. Tintle

Faculty Work Comprehensive List

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically …

Data Analytics For Fault Localization In Complex Networks, Maggie X. Cheng, Wei Biao Wu

Computer Science Faculty Research & Creative Works

We consider the problem of identifying the source of failure in a network after receiving alarms or having observed symptoms. To locate the root cause accurately and timely in a large communication system is challenging because a single fault can often result in a large number of alarms, and multiple faults can occur concurrently. In this paper, we present a new fault localization method using a machine-learning approach. We propose to use logistic regression to study the correlation among network events based on end-to-end measurements. Then based on the regression model, we develop fault hypothesis that best explains the observed …

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 …

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 …

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 …

Distributed Cognitive Rat Selection In 5g Heterogeneous Networks: A Machine Learning Approach, Juan Samuel Perez Rodriguez

Electrical and Computer Engineering ETDs

The leading role of the HetNet (Heterogeneous Networks) strategy as the key Radio Access Network (RAN) architecture for future 5G networks poses serious challenges to the current cell selection mechanisms used in cellular networks. The max-SINR algorithm, although effective historically for performing the most essential networking function of wireless networks, is inefficient at best and obsolete at worst in 5G HetNets. The foreseen embarrassment of riches and diversified propagation characteristics of network attachment points spanning multiple Radio Access Technologies (RAT) requires novel and creative context-aware system designs. The association and routing decisions, in the context of single-RAT or multi-RAT connections, …

Landscape Ecology Of Eastern Wild Turkeys In Mississippi, Annie Moriah Davis

Theses and Dissertations

The effects of landscape structure on wildlife populations have drawn more attention from ecologists and wildlife managers as landscapes have rapidly changed worldwide. The objectives of this study were to (1) conduct a statewide habitat suitability assessment for wild turkeys (Meleagris gallopavo) in Mississippi using machine learning methods; (2) determine landscape-abundance relationships of wild turkeys at 2 spatial scales; and (3) measure genetic distinction of wild turkey populations in Mississippi. I found that habitat suitability for wild turkeys was positively related to amount of forest cover. Wild turkey relative abundance peaked at an optimal hardwood forest proportion of 0.29 and …

Methodology To Predict Daily Groundwater Levels By The Implementation Of Machine Learning And Crop Models, Sandra Milena Guzman Gutierrez

Theses and Dissertations

The continuous decline of groundwater levels caused by variations in climatic conditions and crop water demands is an increased concern for the agricultural community. It is necessary to understand the factors that control these changes in groundwater levels so that we can better address declines and develop improved conservation practices that will lead to a more sustainable use of water. In this study, two machine learning techniques namely support vector regression (SVR) and the nonlinear autoregressive with exogenous inputs (NARX) neural network were implemented to predict daily groundwater levels in a well located in the Mississippi Delta Region (MDR). Results …

Reward Modulated Spike Timing Dependent Plasticity Based Learning Mechanism In Spiking Neural Networks, Shrihari Sridharan, Gopalakrishnan Srinivasan, Kaushik Roy

The Summer Undergraduate Research Fellowship (SURF) Symposium

Spiking Neural Networks (SNNs) are one of the recent advances in machine learning that aim to further emulate the computations performed in the human brain. The efficiency of such networks stems from the fact that information is encoded as spikes, which is a paradigm shift from the computing model of the traditional neural networks. Spike Timing Dependent Plasticity (STDP), wherein the synaptic weights interconnecting the neurons are modulated based on a pair of pre- and post-synaptic spikes is widely used to achieve synaptic learning. The learning mechanism is extremely sensitive to the parameters governing the neuron dynamics, the extent of …

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 …

Computational Labeling, Partitioning, And Balancing Of Molecular Networks, Biaobin Jiang

Open Access Dissertations

Recent advances in high throughput techniques enable large-scale molecular quantification with high accuracy, including mRNAs, proteins and metabolites. Differential expression of these molecules in case and control samples provides a way to select phenotype-associated molecules with statistically significant changes. However, given the significance ranking list of molecular changes, how those molecules work together to drive phenotype formation is still unclear. In particular, the changes in molecular quantities are insufficient to interpret the changes in their functional behavior. My study is aimed at answering this question by integrating molecular network data to systematically model and estimate the changes of molecular functional …

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 …

Teacher + Technology = Blended Learning: How Important Is The Teacher In This Equation?, Catherine A. Doom

Department of Educational Administration: Dissertations, Theses, and Student Research

This mixed method study investigates teacher belief, knowledge action and technology adoption rate in a blended learning setting and the impact those variables have on student comprehension measures. Surveys and data collected from the blended learning program were used to gather data in pursuit of answers to the following research questions: (a) Does the teacher’s adoption of technology rate impact reading comprehension growth in a blended learning environment? (b) Does the teacher’s knowledge, belief and action in a blended learning environment impact student reading comprehension growth? (c) Does the teacher’s adoption of technology rate combined with the teacher’s knowledge, attitude, …

Learning In The Real World: Constraints On Cost, Space, And Privacy, Matt J. Kusner

McKelvey School of Engineering Graduate Student Theses & Dissertations

The sheer demand for machine learning in fields as varied as: healthcare, web-search ranking, factory automation, collision prediction, spam filtering, and many others, frequently outpaces the intended use-case of machine learning models. In fact, a growing number of companies hire machine learning researchers to rectify this very problem: to tailor and/or design new state-of-the-art models to the setting at hand.

However, we can generalize a large set of the machine learning problems encountered in practical settings into three categories: cost, space, and privacy. The first category (cost) considers problems that need to balance the accuracy of a machine learning model …

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 …

Application Of Machine Learning To Prediction Of Vegetation Health, Emily Burchfield, John J. Nay, Jonathan Gilligan

Environment and Society Faculty Publications

This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. …

Location Inference Of Social Media Posts At Hyper-Local Scale, Brian D. Mcclanahan

Master's Theses

This paper describes an approach to infer the location of a social media post at a hyper-local scale based on its content, conditional to the knowledge that the post originates from a larger area such as a city or even a state. The approach comprises three components: (i) a discriminative classifier, namely, Logistic Regression (LR) which selects from a set of most probable sub-regions from where 1 a post might have originated; (ii) a clustering technique, namely, k-means, that adaptively partitions the larger geographic region into sub-regions based on the density of the posts; and (iii) a range of techniques …

Spectral Target Detecting Using Schroedinger Eigenmaps, Leidy P. Dorado-Munoz

Theses

Applications of optical remote sensing processes include environmental monitoring, military monitoring, meteorology, mapping, surveillance, etc. Many of these tasks include the detection of specific objects or materials, usually few or small, which are surrounded by other materials that clutter the scene and hide the relevant information. This target detection process has been boosted lately by the use of hyperspectral imagery (HSI) since its high spectral dimension provides more detailed spectral information that is desirable in data exploitation. Typical spectral target detectors rely on statistical or geometric models to characterize the spectral variability of the data. However, in many cases these …

“When ‘Bad’ Is ‘Good’”: Identifying Personal Communication And Sentiment In Drug-Related Tweets, Raminta Daniulaityte, Lu Chen, Francois R. Lamy, Robert G. Carlson, Krishnaprasad Thirunarayan, Amit Sheth

Population and Public Health Sciences Faculty Publications

Background: To harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content. Objectives: The objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid-related tweets. Methods: Tweets were collected using Twitter streaming Application Programming Interface and filtered through the eDrugTrends platform using keywords related to cannabis, marijuana edibles, marijuana concentrates, and synthetic …