Digital Commons Network^™

Rnn-Based Generation Of Polyphonic Music And Jazz Improvisation, Andrew Hannum

Electronic Theses and Dissertations

This paper presents techniques developed for algorithmic composition of both polyphonic music, and of simulated jazz improvisation, using multiple novel data sources and the character-based recurrent neural network architecture char-rnn. In addition, techniques and tooling are presented aimed at using the results of the algorithmic composition to create exercises for musical pedagogy.

Recurrent Neural Networks And Their Applications To Rna Secondary Structure Inference, Devin Willmott

Theses and Dissertations--Mathematics

Recurrent neural networks (RNNs) are state of the art sequential machine learning tools, but have difficulty learning sequences with long-range dependencies due to the exponential growth or decay of gradients backpropagated through the RNN. Some methods overcome this problem by modifying the standard RNN architecure to force the recurrent weight matrix W to remain orthogonal throughout training. The first half of this thesis presents a novel orthogonal RNN architecture that enforces orthogonality of W by parametrizing with a skew-symmetric matrix via the Cayley transform. We present rules for backpropagation through the Cayley transform, show how to deal with the Cayley …

A Machine Learning Approach To Assess The Separation Of Seismocardiographic Signals By Respiration, Brian Solar

Honors Undergraduate Theses

The clinical usage of Seismocardiography (SCG) is increasing as it is being shown to be an effective non-invasive measurement for heart monitoring. SCG measures the vibrational activity at the chest surface and applications include non-invasive assessment of myocardial contractility and systolic time intervals. Respiratory activity can also affect the SCG signal by changing the hemodynamic characteristics of cardiac activity and displacing the position of the heart. Other clinically significant information, such as systolic time intervals, can thus manifest themselves differently in an SCG signal during inspiration and expiration. Grouping SCG signals into their respective respiratory cycle can mitigate this issue. …

Modeling Engagement Of Programming Students Using Unsupervised Machine Learning Technique, Hua Leong Fwa, Lindsay Marshall

Research Collection School Of Computing and Information Systems

Engagement is instrumental to students’ learning and academic achievements. In this study, we model the engagement states of students who are working on programming exercises in an intelligent tutoring system. Head pose, keystrokes and action logs of students automatically captured within the tutoring system are fed into a Hidden Markov Model for inferring the engagement states of students. With the modeling of students’ engagement on a moment by moment basis, intervention measures can be initiated automatically by the system when necessary to optimize the students’ learning. This study is also one of the few studies that bypass the need for …

Big Data Analytics And Precision Animal Agriculture Symposium: Machine Learning And Data Mining Advance Predictive Big Data Analysis In Precision Animal Agriculture, Gota Morota, Ricardo V. Ventura, Fabyano F. Silva, Masanori Koyama, Samodha C. Fernando

Department of Animal Science: Faculty Publications

Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the use of tools to routinely monitor and collect information from animals and farms in a less laborious manner than before. These efforts have enabled the animal sciences to embark on information technology-driven discoveries to improve animal agriculture. However, the growing amount and complexity of data generated by fully automated, high-throughput data recording or phenotyping platforms, including digital images, sensor and sound data, unmanned systems, and information obtained …

The Impact Of Data Sovereignty On American Indian Self-Determination: A Framework Proof Of Concept Using Data Science, Joseph Carver Robertson

Electronic Theses and Dissertations

The Data Sovereignty Initiative is a collection of ideas that was designed to create SMART solutions for tribal communities. This concept was to develop a horizontal governance framework to create a strategic act of sovereignty using data science. The core concept of this idea was to present data sovereignty as a way for tribal communities to take ownership of data in order to affect policy and strategic decisions that are data driven in nature. The case studies in this manuscript were developed around statistical theories of spatial statistics, exploratory data analysis, and machine learning. And although these case studies are …

Simulation Of Correlated Variables, Mixture Distributions, And Repeated Measures And Comparison Of Pharmacogenetic Prediction With Machine Learning Methods, Allison Cynthia Fialkowski

All ETDs from UAB

Pharmacogenetics aims to improve individualized medicine by combining genetic information with clinical data when assigning drug treatments. Different diseases are often associated with different factors, each with its own distributional characteristics. For rare or complex diseases, these characteristics may not be well understood and standard analysis methods may not be appropriate. In psychiatric disorders, for example, multiple drug treatments need to be compared across small sample sizes with heterogeneous patient populations, making implementation of standard methods difficult. Simulating data that mirrors clinical data is important because it allows investigators to determine which method is best suited for data from their …

Evaluation Of Machine Learning Techniques For Early Identification Of At-Risk Students, Mansour Hamoud Awaji

CCAC Theses and Dissertations

Student attrition is one of the long-standing problems facing higher education institutions despite the extensive research that has been undertaken to address it. To increase students’ success and retention rates, there is a need for early alert systems that facilitate the identification of at-risk students so that remedial measures may be taken in time to reduce the risk. However, incorporating ML predictive models into early warning systems face two main challenges: improving the accuracy of timely predictions and the generalizability of predictive models across on-campus and online courses. The goal of this study was to develop and evaluate predictive models …

Expanding The Artificial Intelligence-Data Protection Debate, Fred H. Cate, Christopher Kuner, Orla Lynskey, Christopher Millard, Nora Ni Loideain, Dan Jerker B. Svantesson

Articles by Maurer Faculty

No abstract provided.

Clinical Information Extraction From Unstructured Free-Texts, Mingzhe Tao

Legacy Theses & Dissertations (2009 - 2024)

Information extraction (IE) is a fundamental component of natural language processing (NLP) that provides a deeper understanding of the texts. In the clinical domain, documents prepared by medical experts (e.g., discharge summaries, drug labels, medical history records) contain a significant amount of clinically-relevant information that is crucial to the overall well-being of patients. Unfortunately, in many cases, clinically-relevant information is presented in an unstructured format, predominantly consisting of free-texts, making it inaccessible to computerized methods. Automatic extraction of this information can improve accessibility. However, the presence of synonymous expressions, medical acronyms, misspellings, negated phrases, and ambiguous terminologies make automatic extraction …

Modified Stacking Ensemble Approach To Detect Network Intrusion, Necati̇ Demi̇r, Gökhan Dalkiliç

Turkish Journal of Electrical Engineering and Computer Sciences

Detecting intrusions in a network traffic has remained an issue for researchers over the years. Advances in the area of machine learning provide opportunities to researchers to detect network intrusion without using a signature database. We studied and analyzed the performance of a stacking technique, which is an ensemble method that is used to combine different classification models to create a better classifier, on the KDD'99 dataset. In this study, the stacking method is improved by modifying the model generation and selection techniques and by using different classifications algorithms as a combiner method. Model generation is performed using subsets of …

An Adaptive Machine Learning-Based Qoe Approach In Sdn Context For Video-Streaming Services, Asma Ben Letaifa

Turkish Journal of Electrical Engineering and Computer Sciences

In data service applications over the Internet, user perception and satisfaction can be assessed by quality of experience (QoE) metrics. QoE depends both on the users' perception and the used service, which together form end-to-end metrics. While network optimization has traditionally focused on optimizing network properties such as QoS, we focus in this work on optimizing end-to-end QoE metrics with the aim to deliver to the client a good QoE that can be monitored in real time. We argue that end-user QoE is a relevant measurement for network operators and service providers. In this paper, we present a machine learning …

Developing A Recurrent Neural Network With High Accuracy For Binary Sentiment Analysis, Kevin Cunanan

CMC Senior Theses

Sentiment analysis has taken on various machine learning approaches in order to optimize accuracy, precision, and recall. However, Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) account for the context of a sentence by using previous predictions as additional input for future sentence predictions. Our approach focused on developing an LSTM RNN that could perform binary sentiment analysis for positively and negatively labeled sentences. In collaboration with Mariam Salloum, I developed a collection of programs to classify individual sentences as either positive or negative. This paper additionally looks into machine learning, neural networks, data preprocessing, implementation, and resulting comparisons.

Application Of Acoustic Emission And Machine Learning To Detect Codling Moth Infested Apples, Mengxing Li, Nader Ekramirad, Ahmed Rady, Akinbode A. Adedeji

Biosystems and Agricultural Engineering Faculty Publications

Incidence of codling moth (CM) (Cydia pomonella L.) infestation in apples has been a major concern in North America for decades. CM larvae bore deep into the fruit, making it unmarketable. An effective noninvasive method to detect larvae-infested apples is necessary to ensure that apples are CM-free in post-harvest processing. In this study, a novel approach using an acoustic emission (AE) system and subsequent machine learning methods was applied to classify larvae-infested apples from intact apples. 'GoldRush‘ apples were infested with CM neonates and stored at the same conditions as intact apples. The AE system was used to collect …

Mapping Opioid Mortality Rates Across Treatment Capacity To Identify Need And Access, Garrett K. Wong, Justin R. Chang, Chase Greco, Yadunandan Pillai, Mohammad A. Shahrezaei, Melissa H. Burton, Rob Lawrence, Alan Dow

Graduate Research Posters

Background: The opioid and heroin overdose epidemic is a public health emergency in the state of Virginia, resulting in the death of more than 1,100 people in 2016. In order to overcome this epidemic, we need to match the places with the greatest need for services related to substance use disorders with the appropriate healthcare workforce.

Aims: As the data about the overdose outbreak and related socioeconomic factors grow in size and complexity, data scientists have attempted to utilize big data techniques to identify communities and risk factors contributing to addiction.

Methods: Using data obtained from the …

Comparing Various Machine Learning Statistical Methods Using Variable Differentials To Predict College Basketball, Nicholas Bennett

Williams Honors College, Honors Research Projects

The purpose of this Senior Honors Project is to research, study, and demonstrate newfound knowledge of various machine learning statistical techniques that are not covered in the University of Akron’s statistics major curriculum. This report will be an overview of three machine-learning methods that were used to predict NCAA Basketball results, specifically, the March Madness tournament. The variables used for these methods, models, and tests will include numerous variables kept throughout the season for each team, along with a couple variables that are used by the selection committee when tournament teams are being picked. The end goal is to find …

Leveraging Overhead Imagery For Localization, Mapping, And Understanding, Scott Workman

Theses and Dissertations--Computer Science

Ground-level and overhead images provide complementary viewpoints of the world. This thesis proposes methods which leverage dense overhead imagery, in addition to sparsely distributed ground-level imagery, to advance traditional computer vision problems, such as ground-level image localization and fine-grained urban mapping. Our work focuses on three primary research areas: learning a joint feature representation between ground-level and overhead imagery to enable direct comparison for the task of image geolocalization, incorporating unlabeled overhead images by inferring labels from nearby ground-level images to improve image-driven mapping, and fusing ground-level imagery with overhead imagery to enhance understanding. The ultimate contribution of this thesis …

Assesing Completeness Of Solvency And Financial Condition Reports Through The Use Of Machine Learning And Text Classification, Ruairí Nugent

Dissertations

Text mining is a method for extracting useful information from unstructured data through the identification and exploration of large amounts of text. It is a valuable support tool for organisations. It enables a greater understanding and identification of relevant business insights from text. Critically it identifies connections between information within texts that would otherwise go unnoticed. Its application is prevalent in areas such as marketing and political science however, until recently it has been largely overlooked within economics. Central banks are beginning to investigate the benefits of machine learning, sentiment analysis and natural language processing in light of the large …

Water Quality Factor Prediction Using Supervised Machine Learning, Kathleen Joslyn

REU Final Reports

The objective of this research is to explore prediction accuracy of water quality factors, with techniques and algorithms in machine learning consisting of a variation of support vector machines - Support Vector Regression (SVR) and the gradient boosting algorithm Extreme Gradient Boosting (XGBoost). Both the XGBoost and SVR algorithms were used to predict nine different factors with success rates ranging from 79% to 99%. Parameters of these algorithms were also explored to test the prediction accuracy levels of individual water quality factors. These parameters included normalizing the data, filling missing data points, and training and testing on a large set …

Data Visualization And Classification Of Artificially Created Images, Dmytro Dovhalets

All Master's Theses

Visualization of multidimensional data is a long-standing challenge in machine learning and knowledge discovery. A problem arises as soon as 4-dimensions are introduced since we live in a 3-dimensional world. There are methods out there which can visualize multidimensional data, but loss of information and clutter are still a problem. General Line Coordinates (GLC) can losslessly project n-dimensional data in 2- dimensions. A new method is introduced based on GLC called GLC-L. This new method can do interactive visualization, dimension reduction, and supervised learning. One of the applications of GLC-L is transformation of vector data into image data. This novel …

Is Tricking A Robot Hacking?, Ryan Calo, Ivan Evtimov, Earlence Fernandes, Tadayoshi Kohno, David O'Hair

Tech Policy Lab

The authors of this essay represent an interdisciplinary team of experts in machine learning, computer security, and law. Our aim is to introduce the law and policy community within and beyond academia to the ways adversarial machine learning (ML) alter the nature of hacking and with it the cybersecurity landscape. Using the Computer Fraud and Abuse Act of 1986—the paradigmatic federal anti-hacking law—as a case study, we mean to evidence the burgeoning disconnect between law and technical practice. And we hope to explain what is at stake should we fail to address the uncertainty that flows from the prospect that …

Artificial Intelligence And It Professionals, Sunil Mithas, Thomas Kude, Jonathan W. Whitaker

Management Faculty Publications

How will continuing developments in artificial intelligence (AI) and machine learning influence IT professionals? This article approaches this question by identifying the factors that influence the demand for software developers and IT professionals, describing how these factors relate to AI, and articulating the likely impact on IT professionals.

Automated Estimation Of Winter Driving Conditions, Michael E. Baldwin, Kevin D. Burris, Kimberly L. Elmore

JTRP Technical Reports

There is great potential for improvements in traveler safety and satisfaction as new sources of information are incorporated into advanced analytics and prediction systems. It is critical for transportation agencies to be able to monitor weather conditions in real-time as well as over the long-term for purposes of maintenance, planning, and performance evaluation. Travelers also use this information to plan their routes, make decisions on timing, and improve their awareness of potential hazards. A machine learning model was developed in this project to automatically estimate winter driving conditions in real-time using weather information as input. This system can provide rapid …

Deep Learning Methods For Visual Object Recognition, Zeyad Hailat

Wayne State University Dissertations

Convolutional neural networks (CNNs) attain state-of-the-art performance on various classification tasks assuming a sufficiently large number of labeled training examples. Unfortunately, curating sufficiently large labeled training dataset requires human involvement, which is expensive, time-consuming, and susceptible to noisy labels. Semi-supervised learning methods can alleviate the aforementioned problems by employing one of two techniques. First, utilizing a limited number of labeled data in conjunction with sufficiently large unlabeled data to construct a classification model. Second, exploiting sufficiently large noisy label training data to learn a classification model. In this dissertation, we proposed a few new methods to mitigate the aforementioned problems. …

Fuzziness-Based Active Learning Framework To Enhance Hyperspectral Image Classification Performance For Discriminative And Generative Classifiers, Muhammad Ahmad, Stanislav Protasov, Adil Mehmood Khan, Rasheed Hussain, Asad Masood Khattak, Wajahat Ali Khan

All Works

© 2018 Ahmad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal …

Measuring Goal Similarity Using Concept, Context And Task Features, Vahid Eyorokon

Browse all Theses and Dissertations

Goals can be described as the user's desired state of the agent and the world and are satisfied when the agent and the world are altered in such a way that the present state matches the desired state. For physical agents, they must act in the world to alter it in a series of individual atomic actions. Traditionally, agents use planning to create a chain of actions each of which altering the current world state and yielding a new one until the final action yields the desired goal state. Once this goal state has been achieved, the goal is said …

The Feasibility Of Dementia Caregiver Task Performance Measurement Using Smart Gaming Technology, Garrett G. Goodman

Browse all Theses and Dissertations

Dementia caregiver burnout is detrimental to both the familial caregiver and their loved ones with dementia. As the population of older adults increases, both the number of individuals with dementia and their corresponding caregivers increase as well. Thus, we are interested in developing a potential tool to non-invasively detect signs of caregiver burnout using a mobile application combined with machine learning. Hence, the mobile application "Caregiver Assessment using Smart Technology" (CAST) was developed which personalizes a word scramble game. The CAST application utilizes a heuristically constructed Fuzzy Inference System (FIS) optimized via a Genetic Algorithm (GA) to provide an individualized …

High Performance Static Random Access Memory Design For Emerging Applications, Xiaowei Chen

Electrical and Computer Engineering

Memory wall is becoming a more and more serious bottleneck of the processing speed of microprocessors. The mismatch between CPUs and memories has been increasing since three decades ago. SRAM was introduced as the bridge between the main memory and the CPU. SRAM is designed to be on the same die with CPU and stores temporary data and instructions that are to be processed by the CPU. Thus, the performance of SRAMs has a direct impact on the performance of CPUs. With the application of mass amount data to be processed nowadays, there is a great need for high-performance CPUs. …

Applying Machine Learning To Advance Cyber Security: Network Based Intrusion Detection Systems, Hassan Hadi Latheeth Al-Maksousy

Computer Science Theses & Dissertations

Many new devices, such as phones and tablets as well as traditional computer systems, rely on wireless connections to the Internet and are susceptible to attacks. Two important types of attacks are the use of malware and exploiting Internet protocol vulnerabilities in devices and network systems. These attacks form a threat on many levels and therefore any approach to dealing with these nefarious attacks will take several methods to counter. In this research, we utilize machine learning to detect and classify malware, visualize, detect and classify worms, as well as detect deauthentication attacks, a form of Denial of Service (DoS). …

Predicting Similarity Judgments In Intertemporal Choice With Machine Learning, Jeffrey R. Stevens, Leen-Kiat Soh

Jeffrey Stevens Publications

Similarity models of intertemporal choice are heuristics that choose based on similarity judgments of the reward amounts and time delays. Yet, we do not know how these judgments are made. Here, we use machine-learning algorithms to assess what factors predict similarity judgments and whether decision trees capture the judgment outcomes and process. We find that combining small and large values into numerical differences and ratios and arranging them in tree-like structures can predict both similarity judgments and response times. Our results suggest that we can use machine learning to not only model decision outcomes but also model how decisions are …