Physical Sciences and Mathematics Commons^™

Unsupervised Machine Learning Account Of Magnetic Transitions In The Hubbard Model, Kelvin Ch'ng, Nick Vazquez, Ehsan Khatami

Faculty Publications

We employ several unsupervised machine learning techniques, including autoencoders, random trees embedding, and t-distributed stochastic neighboring ensemble (t-SNE), to reduce the dimensionality of, and therefore classify, raw (auxiliary) spin configurations generated, through Monte Carlo simulations of small clusters, for the Ising and Fermi-Hubbard models at finite temperatures. Results from a convolutional autoencoder for the three-dimensional Ising model can be shown to produce the magnetization and the susceptibility as a function of temperature with a high degree of accuracy. Quantum fluctuations distort this picture and prevent us from making such connections between the output of the autoencoder and …

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 …

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 …

Estimating Meteorological Visibility Range Under Foggy Weather Conditions: A Deep Learning Approach, Hazar Chaabani, Naoufel Werghi, Faouzi Kamoun, Bilal Taha, Fatma Outay, Ansar Ul Haque Yasar

All Works

© 2018 The Authors. Published by Elsevier Ltd. Systems capable of estimating visibility distances under foggy weather conditions are extremely useful for next-generation cooperative situational awareness and collision avoidance systems. In this paper, we present a brief review of noticeable approaches for determining visibility distance under foggy weather conditions. We then propose a novel approach based on the combination of a deep learning method for feature extraction and an SVM classifier. We present a quantitative evaluation of the proposed solution and show that our approach provides better performance results compared to an earlier approach that was based on the combination …

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 …

Old English Character Recognition Using Neural Networks, Sattajit Sutradhar

Electronic Theses and Dissertations

Character recognition has been capturing the interest of researchers since the beginning of the twentieth century. While the Optical Character Recognition for printed material is very robust and widespread nowadays, the recognition of handwritten materials lags behind. In our digital era more and more historical, handwritten documents are digitized and made available to the general public. However, these digital copies of handwritten materials lack the automatic content recognition feature of their printed materials counterparts. We are proposing a practical, accurate, and computationally efficient method for Old English character recognition from manuscript images. Our method relies on a modern machine learning …

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

CCE 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 …

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 …

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 …

Smart Classifiers And Bayesian Inference For Evaluating River Sensitivity To Natural And Human Disturbances: A Data Science Approach, Kristen Underwood

Graduate College Dissertations and Theses

Excessive rates of channel adjustment and riverine sediment export represent societal challenges; impacts include: degraded water quality and ecological integrity, erosion hazards to infrastructure, and compromised public safety. The nonlinear nature of sediment erosion and deposition within a watershed and the variable patterns in riverine sediment export over a defined timeframe of interest are governed by many interrelated factors, including geology, climate and hydrology, vegetation, and land use. Human disturbances to the landscape and river networks have further altered these patterns of water and sediment routing.

An enhanced understanding of river sediment sources and dynamics is important for stakeholders, and …

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 …

Automating The Crowd-Mapping Workflow With Deep Learning, Lasith Niroshan

Theses

Maintaining updated maps in an ever-changing built environment is important for supporting modern society in many ways. The usage of online crowdsourced maps in particular has gained importance in a wide range of recent location-based applications (route planning/navigation, urban planning, real estate, tourism, etc). However, both traditional map production methods and updating today’s online maps suffer from early obsolescence due to their largely manual map production/update workflows. Significant research efforts have focused on refining techniques to identify changes in raster satellite images, aiming to improve and streamline map production processes. Concurrently, the surge in Internet usage has led to a …

Quantitative Forecasting Of Risk For Ptsd Using Ecological Factors: A Deep Learning Application, Nuriel S. Mor, Kathryn L. Dardeck

Journal of Social, Behavioral, and Health Sciences

Forecasting the risk for mental disorders from early ecological information holds benefits for the individual and society. Computational models used in psychological research, however, are barriers to making such predictions at the individual level. Preexposure identification of future soldiers at risk for posttraumatic stress disorder (PTSD) and other individuals, such as humanitarian aid workers and journalists intending to be potentially exposed to traumatic events, is important for guiding decisions about exposure. The purpose of the present study was to evaluate a machine learning approach to identify individuals at risk for PTSD using readily collected ecological risk factors, which makes scanning …

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 …

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 …

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 …

Smart Augmented Reality Instructional System For Mechanical Assembly, Ze-Hao Lai

Masters Theses

"Quality and efficiency are pivotal indicators of a manufacturing company. Many companies are suffering from shortage of experienced workers across the production line to perform complex assembly tasks such as assembly of an aircraft engine. This could lead to a significant financial loss. In order to further reduce time and error in an assembly, a smart system consisting of multi-modal Augmented Reality (AR) instructions with the support of a deep learning network for tool detection is introduced. The multi-modal smart AR is designed to provide on-site information including various visual renderings with a fine-tuned Region-based Convolutional Neural Network, which is …

Anatomy Of Online Hate: Developing A Taxonomy And Machine Learning Models For Identifying And Classifying Hate In Online News Media, Joni Salminen, Hind Almerekhi, Milica Milenkovic, Soon-Gyu Jung, Haewoon Kwak, Haewoon Kwak, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

Online social media platforms generally attempt to mitigate hateful expressions, as these comments can be detrimental to the health of the community. However, automatically identifying hateful comments can be challenging. We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset. Our contribution is twofold: 1) creating a granular taxonomy for hateful online comments that includes both …

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). …

Deep Recurrent Learning For Efficient Image Recognition Using Small Data, Mahbubul Alam

Electrical & Computer Engineering Theses & Dissertations

Recognition is fundamental yet open and challenging problem in computer vision. Recognition involves the detection and interpretation of complex shapes of objects or persons from previous encounters or knowledge. Biological systems are considered as the most powerful, robust and generalized recognition models. The recent success of learning based mathematical models known as artificial neural networks, especially deep neural networks, have propelled researchers to utilize such architectures for developing bio-inspired computational recognition models. However, the computational complexity of these models increases proportionally to the challenges posed by the recognition problem, and more importantly, these models require a large amount of data …

Looping Predictive Method To Improve Accuracy Of A Machine Learning Model, Subramanyam Reddy Pogili

Theses

The topic of this project is an analysis of drug-related tweets. The goal is to build a Machine Learning Model that can distinguish between tweets that indicate drug abuse and other tweets that also contain the name of a drug but do not describe abuse. Drugs can be illegal, such as heroin, or legal drugs with a potential of abuse, such as painkillers. However, building a good Machine Learning Model requires a large amount of training data. For each training tweet, a human expert has determined whether it indicates drug abuse or not. This is difficult work for humans. …

Visual Odometry Using Convolutional Neural Networks, Alec Graves, Steffen Lim, Thomas Fagan, Kevin Mcfall Phd.

The Kennesaw Journal of Undergraduate Research

Visual odometry is the process of tracking an agent's motion over time using a visual sensor. The visual odometry problem has only been recently solved using traditional, non-machine learning techniques. Despite the success of neural networks at many related problems such as object recognition, feature detection, and optical flow, visual odometry still has not been solved with a deep learning technique. This paper attempts to implement several Convolutional Neural Networks to solve the visual odometry problem and compare slight variations in data preprocessing. The work presented is a step toward reaching a legitimate neural network solution.

Machine Learning To Discover And Optimize Materials, Conrad Waldhar Rosenbrock

Theses and Dissertations

For centuries, scientists have dreamed of creating materials by design. Rather than discovery by accident, bespoke materials could be tailored to fulfill specific technological needs. Quantum theory and computational methods are essentially equal to the task, and computational power is the new bottleneck. Machine learning has the potential to solve that problem by approximating material behavior at multiple length scales. A full end-to-end solution must allow us to approximate the quantum mechanics, microstructure and engineering tasks well enough to be predictive in the real world. In this dissertation, I present algorithms and methodology to address some of these problems at …

Ethics And Bias In Machine Learning: A Technical Study Of What Makes Us “Good”, Ashley Nicole Shadowen

Student Theses

The topic of machine ethics is growing in recognition and energy, but bias in machine learning algorithms outpaces it to date. Bias is a complicated term with good and bad connotations in the field of algorithmic prediction making. Especially in circumstances with legal and ethical consequences, we must study the results of these machines to ensure fairness. This paper attempts to address ethics at the algorithmic level of autonomous machines. There is no one solution to solving machine bias, it depends on the context of the given system and the most reasonable way to avoid biased decisions while maintaining the …

Uncovering New Links Through Interaction Duration, Laxmi Amulya Gundala

Boise State University Theses and Dissertations

Link Prediction is the problem of inferring new relationships among nodes in a network that can occur in the near future. Classical approaches mainly consider neighborhood structure similarity when linking nodes. However, we may also want to take into account whether the two nodes we are going to link will benefit from that by having an active interaction over time. For instance, it is better to link two nodes � and � if we know that these two nodes will interact in the social network in the future, rather than suggesting �, who may never interact with �. Thus, the …

A Test Driven Approach To Develop Web-Based Machine Learning Applications, Armin Esmaeilzadeh

UNLV Theses, Dissertations, Professional Papers, and Capstones

The purpose of this thesis is to propose the design and architecture of a testable, scalable, and ef-cient web-based application that models and implements machine learning applications in cancer prediction. There are various components that form the architecture of our web-based application including server, database, programming language, web framework, and front-end design. There are also other factors associated with our application such as testability, scalability, performance, and design pattern. Our main focus in this thesis is on the testability of the system while consid- ering the importance of other factors as well.

The data set for our application is a …

Scalable Online Kernel Learning, Jing Lu

Dissertations and Theses Collection (Open Access)

One critical deficiency of traditional online kernel learning methods is their increasing and unbounded number of support vectors (SV’s), making them inefficient and non-scalable for large-scale applications. Recent studies on budget online learning have attempted to overcome this shortcoming by bounding the number of SV’s. Despite being extensively studied, budget algorithms usually suffer from several drawbacks.
First of all, although existing algorithms attempt to bound the number of SV’s at each iteration, most of them fail to bound the number of SV’s for the final averaged classifier, which is commonly used for online-to-batch conversion. To solve this problem, we propose …

An Integrated Framework For Modeling And Predicting Spatiotemporal Phenomena In Urban Environments, Tuc Viet Le

Dissertations and Theses Collection (Open Access)

This thesis proposes a general solution framework that integrates methods in machine learning in creative ways to solve a diverse set of problems arising in urban environments. It particularly focuses on modeling spatiotemporal data for the purpose of predicting urban phenomena. Concretely, the framework is applied to solve three specific real-world problems: human mobility prediction, trac speed prediction and incident prediction. For human mobility prediction, I use visitor trajectories collected a large theme park in Singapore as a simplified microcosm of an urban area. A trajectory is an ordered sequence of attraction visits and corresponding timestamps produced by a visitor. …

Anomaly Detection For A Water Treatment System Using Unsupervised Machine Learning, Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M. Poskitt, Jun Sun

Research Collection School Of Computing and Information Systems

In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM). These methods are evaluated against data from the Secure Water Treatment (SWaT) testbed, a scaled-down but fully operational raw water purification plant. For both methods, we first train detectors using a log generated by SWaT operating under normal conditions. Then, we evaluate the performance of both methods using a log generated by SWaT operating under 36 …