Digital Commons Network^™

Crash Severity Analysis Of Rear-End Crashes In California Using Statistical And Machine Learning Classification Methods, Alidad Ahmadi, Arash Jahangiri, Vincent Berardi, Sahar Ghanipoor Machiani

Psychology Faculty Articles and Research

Investigating drivers’ injury level and detecting contributing factors that aggravate the damage level imposed on drivers and vehicles is a critical subject in the field of crash analysis. In this study, a comprehensive vehicle-by-vehicle crash data set is developed by integrating 5 years of data from California crash, vehicles involved, and road databases. The data set is used to model the severity of rear-end crashes for comparing three analytic techniques: multinomial logit, mixed multinomial logit, and support vector machine (SVM). The results of the crash severity models and the role of contributing factors to the severity outcome of rear-end crashes …

A Comparison Of Machine Learning Techniques For Taxonomic Classification Of Teeth From The Family Bovidae, Gregory J. Matthews, Juliet K. Brophy, Maxwell Luetkemeier, Hongie Gu, George K. Thiruvathukal

Faculty Publications

This study explores the performance of machine learning algorithms on the classification of fossil teeth in the Family Bovidae. Isolated bovid teeth are typically the most common fossils found in southern Africa and they often constitute the basis for paleoenvironmental reconstructions. Taxonomic identification of fossil bovid teeth, however, is often imprecise and subjective. Using modern teeth with known taxons, machine learning algorithms can be trained to classify fossils. Previous work by Brophy et al. [Quantitative morphological analysis of bovid teeth and implications for paleoenvironmental reconstruction of plovers lake, Gauteng Province, South Africa, J. Archaeol. Sci. 41 (2014), pp. 376–388] uses …

Responsibility And Technology, Sarah Moss

The Voice

No abstract provided.

Hidden In Plain Sight: A Machine Learning Approach For Detecting Prostitution Activity In Phoenix, Arizona, Edward Helderop, Jessica Huff, Fred Morstatter, Anthony Grubesic, Danielle Wallace

Criminology and Criminal Justice Faculty Publications

Prostitution has been a topic of study for decades, yet many questions remain about where prostitution occurs. Difficulty in identifying prostitution activity is often attributed to the hidden and seemingly victimless nature of the crime. Despite numerous challenges associated with policing street prostitution, these encounters become more difficult to identify when they take place indoors, especially in locations away from public view, such as hotels. The purpose of this paper is to develop a strategy for identifying hotel facilities and surrounding areas that may be experiencing elevated levels of prostitution activity using high-volume, user-generated data, namely hotel reviews written by …

Machine Learning Tools For Optimization Of Fuel Consumption At Signalized Intersections In Connected/Automated Vehicles Environment, Saleh Ragab Mousa

LSU Doctoral Dissertations

Researchers continue to seek numerous techniques for making the transportation sector more sustainable in terms of fuel consumption and greenhouse gas emissions. Among the most effective techniques is Eco-driving at signalized intersections. Eco-driving is a complex control problem where drivers approaching the intersections are guided, over a period of time, to optimize fuel consumption. Eco-driving control systems reduce fuel consumption by optimizing vehicle trajectories near signalized intersections based on information of the SpaT (Signal Phase and Timing). Developing Eco-driving applications for semi-actuated signals, unlike pre-timed, is more challenging due to variations in cycle length resulting from fluctuations in traffic demand. …

Using Chronicling America’S Images To Explore Digitized Historic Newspapers & Imagine Alternative Futures, Elizabeth Lorang, Leen-Kiat Soh

University of Nebraska-Lincoln Libraries: Presentations

This presentation situates the work of the Aida team broadly as well as hinges this work on some very specific challenges for digital libraries. In doing so demonstrate the many types of questions and domains to be explored in digitized newspapers.

Hr500k – A Reference Training Corpus Of Croatian., Nikola Ljubešić, Željko Agić, Filip Klubicka, Vuk Batanović, Tomaž Erjavec

Conference papers

In this paper we present hr500k, a Croatian reference training corpus of 500 thousand tokens, segmented at document, sentence and word level, and annotated for morphosyntax, lemmas, dependency syntax, named entities, and semantic roles. We present each annotation layer via basic label statistics and describe the final encoding of the resource in CoNLL and TEI formats. We also give a description of the rather turbulent history of the resource and give insights into the topic and genre distribution in the corpus. Finally, we discuss further enrichments of the corpus with additional layers, which are already underway.

Innovation In Rangeland Monitoring: Annual, 30 M, Plant Functional Type Percent Cover Maps For U.S. Rangelands, 1984-2017, Matthew O. Jones, Brady W. Allred, David E. Naugle, Jeremy D. Maestas, Patrick Donnelly, Loretta J. Metz, Jason Karl, Rob Smith, Brandon Bestelmeyer, Chad Boyd, Jay D. Kerby, James D. Mciver

Articles

Innovations in machine learning and cloud‐based computing were merged with historical remote sensing and field data to provide the first moderate resolution, annual, percent cover maps of plant functional types across rangeland ecosystems to effectively and efficiently respond to pressing challenges facing conservation of biodiversity and ecosystem services. We utilized the historical Landsat satellite record, gridded meteorology, abiotic land surface data, and over 30,000 field plots within a Random Forests model to predict per‐pixel percent cover of annual forbs and grasses, perennial forbs and grasses, shrubs, and bare ground over the western United States from 1984 to 2017. Results were …

Approaching Arabic Automatic Speech Recognition With Usage, James Longstaff, Deryle Lonsdale

Journal of Undergraduate Research

The purpose of this project is to improve Arabic automatic speech recognition (ASR) by distinguishing between different dialects with the use of machine learning. Machine learning is the teaching of computers to recognize and distinguish between categories by themselves. Machine learning works off of statistical and mathematical algorithms implemented with computer programming.

Domain-Specific Use Cases For Knowledge-Enabled Social Media Analysis, Soon Jye Kho, Swati Padhee, Goonmeet Bajaj, Krishnaprasad Thirunarayan, Amit Sheth

Publications

No abstract provided.

Refining Statistical Magnesium Models Via Machine Learning, Andrew Orme, Dr. David Fullwood

Journal of Undergraduate Research

Magnesium is a potential replacement for steels and aluminum in strength applications. Despite desirable strength and weight properties, magnesium is costly to manufacture. To reduce manufacturing costs, extensive research has been done on is a phenomenon called twinning, where a large group of magnesium atoms collectively reorient from a base orientation to a new orientation. This reorientation caused by twinning has the potential to enable easier material deformation, allowing for less costly manufacturing. Our research group pursued a novel approach to twinning research by using data mining and machine learning algorithms. Data collected from samples of magnesium using a scanning …

Toward Audio Beehive Monitoring: Deep Learning Vs. Standard Machine Learning In Classifying Beehive Audio Samples, Vladmir Kulyukin, Sarbajit Mukherjee, Prakhar Amlathe

Computer Science Faculty and Staff Publications

Electronic beehive monitoring extracts critical information on colony behavior and phenology without invasive beehive inspections and transportation costs. As an integral component of electronic beehive monitoring, audio beehive monitoring has the potential to automate the identification of various stressors for honeybee colonies from beehive audio samples. In this investigation, we designed several convolutional neural networks and compared their performance with four standard machine learning methods (logistic regression, k-nearest neighbors, support vector machines, and random forests) in classifying audio samples from microphones deployed above landing pads of Langstroth beehives. On a dataset of 10,260 audio samples where the training and testing …

An Efficient Radio Map Updating Algorithm Based On K-Means And Gaussian Process Regression, Jianli Zhao, Xiang Gao, Xin Wang, Chunxiu Li, Min Song, Qiuxia Sun

Michigan Tech Publications, Part 1

Fingerprint-based indoor localisation suffers from influences such as fingerprint pre-collection, environment changes and expending a lot of manpower and time to update the radio map. To solve the problem, we propose an efficient radio map updating algorithm based on K-Means and Gaussian Process Regression (KMGPR). The algorithm builds a Gaussian Process Regression (GPR) predictive model based on a Gaussian mean function and realises the update of the radio map using K-Means. We have conducted experiments to evaluate the performance of the proposed algorithm and results show that GPR using the Gaussian mean function improves localisation accuracy by about 13·76% compared …

Big Data In Economics, Matthew Harding, Jonathan Hersh

Economics Faculty Articles and Research

Big Data refers to data sets of much larger size, higher frequency, and often more personalized information. Examples include data collected by smart sensors in homes or aggregation of tweets on Twitter. In small data sets, traditional econometric methods tend to outperform more complex techniques. In large data sets, however, machine learning methods shine. New analytic approaches are needed to make the most of Big Data in economics. Researchers and policymakers should thus pay close attention to recent developments in machine learning techniques if they want to fully take advantage of these new sources of Big Data.

Music For Ai Reports: Dual Prospects In Music Production, Achim Koh

Dissertations, Theses, and Capstone Projects

Recent developments in artificial intelligence (AI) technology have led to industrial attempts at applying AI to music making, namely AI music. In the context of the history of music technology, AI music raises the prospect of a new phase that extends digital technology’s role as central mode of music production. The computer has become an essential metamedium in contemporary cultural production, leading in the field of music to the digitization of tools and content and the digitalization of social institutions and relationships. This technological change had the dual effect of decentralizing music production while reinforcing capitalist logic in it. The …

Low-Shot Learning For The Semantic Segmentation Of Remote Sensing Imagery, Ronald Kemker

Theses

Deep-learning frameworks have made remarkable progress thanks to the creation of large annotated datasets such as ImageNet, which has over one million training images. Although this works well for color (RGB) imagery, labeled datasets for other sensor modalities (e.g., multispectral and hyperspectral) are minuscule in comparison. This is because annotated datasets are expensive and man-power intensive to complete; and since this would be impractical to accomplish for each type of sensor, current state-of-the-art approaches in computer vision are not ideal for remote sensing problems. The shortage of annotated remote sensing imagery beyond the visual spectrum has forced researchers to embrace …

Micro-Manipulation Using Learned Model, Matthew A. Lyng, Benjamin V. Johnson, David J. Cappelleri

The Summer Undergraduate Research Fellowship (SURF) Symposium

Microscale devices can be found in applications ranging from sensors to structural components. The dominance of surface forces at the microscale hinders the assembly processes through nonlinear interactions that are difficult to model for automation, limiting designs of microsystems to primarily monolithic structures. Methods for modeling surface forces must be presented for viable manufacturing of devices consisting of multiple microparts. This paper proposes the implementation of supervised machine learning models to aid in automated micromanipulation tasks for advanced manufacturing applications. The developed models use sets of training data to implicitly model surface interactions and predict end-effector placement and paths that …

Supervised Sparse Learning With Applications In Bioinformatics, Kin Ming Puk

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

In machine learning and mathematical optimization, sparse learning is the use of mathematical norms such as L1-norm, group norm and L21-norm in order to seek a trade-off between the goodness-of-fit measure and sparsity of the result. Sparsity of result leads to a parsimonious learning model - in other words, only few features from the data matrix are required to build the learning model and for further interpretation. The motivations of employing sparse learning in bioinformatics are two-fold: firstly, a parsimonious learning model enhances the explanatory power; and secondly, a parsimonious model generally allows better prediction and generalizes better to new …

Inverse Analysis Of Heat Generating Body For Safety Applications, Sandeep Patil

Mechanical and Aerospace Engineering Dissertations - Archive

Inverse thermal analysis and its applications have been applied to numerous fields of science and engineering. Historically during the 1950's and early 1960's, space programs played a significant role in the advancement of solution techniques for Inverse Heat transfer Problems(IHTP). It was applied to measure the surface temperature of thermal shield of a space vehicle during its re-entry into atmosphere. Inverse analysis was also used in the estimation of thermo-physical properties of the shield at high operating temperatures. Besides thermal application, inverse technique was also used in other engineering applications such as estimation of alloy specification, design of a shape …

Prediction Of 1p/19q Codeletion Status In Diffuse Glioma Patients Using Preoperative Multiparametric Magnetic Resonance Imaging, Donnie Kim

Dissertations and Theses (Open Access)

A complete codeletion of chromosome 1p/19q is strongly correlated with better overall survival of diffuse glioma patients, hence determining the codeletion status early in the course of a patient’s disease would be valuable in that patient’s care. The current practice requires a surgical biopsy in order to assess the codeletion status, which exposes patients to risks and is limited in its accuracy by sampling variations. To overcome such limitations, we utilized four conventional magnetic resonance imaging sequences to predict the 1p/19q status. We extracted three sets of image-derived features, namely texture-based, topology-based, and convolutional neural network (CNN)-based, and analyzed each …

Machine Learning For Internet Of Things Data Analysis: A Survey, Mohammad Saeid Mahdavinejad, Mohammadreza Rezvan, Mohammadamin Barekatain, Peyman Adibi, Payam Barnaghi, Amit Sheth

Publications

Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the …

Novel Detection And Analysis Using Deep Variational Autoencoders, Tucker B. Graydon

Theses

This paper presents a Novel Identification System which uses generative modeling techniques and Gaussian Mixture Models (GMMs) to identify the main process variables involved in a novel event from multivariate data. Features are generated and subsequently dimensionally reduced by using a Variational Autoencoder (VAE) supplemented by a denoising criterion and a β disentangling method. The GMM parameters are learned using the Expectation Maximization(EM) algorithm on features collected from only normal operating conditions. A one-class classification is achieved by thresholding the likelihoods by a statistically derived value. The Novel Identification method is verified as a detection method on existing Radio Frequency …

Cryptovisor: A Cryptocurrency Advisor Tool, Matthew Baldree, Paul Widhalm, Brandon Hill, Matteo Ortisi

SMU Data Science Review

In this paper, we present a tool that provides trading recommendations for cryptocurrency using a stochastic gradient boost classifier trained from a model labeled by technical indicators. The cryptocurrency market is volatile due to its infancy and limited size making it difficult for investors to know when to enter, exit, or stay in the market. Therefore, a tool is needed to provide investment recommendations for investors. We developed such a tool to support one cryptocurrency, Bitcoin, based on its historical price and volume data to recommend a trading decision for today or past days. This tool is 95.50% accurate with …

Localized Indoor Temperature Estimation Using Smartphone And Laptop Internal Sensors, Mostafa Rafaie, Mehari Tesfay, Fadi Alsaleem

International High Performance Buildings Conference

This paper investigates a mechanism in which indoor air temperature can be predicted using the temperature sensor on the battery and the CPU of smartphones or laptops, where data is easily accessible and ubiquitous. As a case study, several machine-learning methods were used to build models from a MacBook Pro’s data and the measured surrounding air temperature. The effects of the machine learning type and input feature size (by including other parameters such as CPU processing usage and battery charge percentage) on the model accuracy were investigated. The goal is to determine a set of feature combinations that can be …

Design Of Experiments For Learning Personalized Visual Preferences Of Occupants In Private Offices, Nimish Awalgaonkar Awalgaonkar, Jie Xiong, Ilias Bilionis, Athanasios Tzempelikos, Panagiota Karava

International High Performance Buildings Conference

Occupants working in offices with controllable shading and lighting systems often perform adaptive actions (raising/lowering shades, increasing/decreasing light levels) in an attempt to restore their preferred state of the room. This preference for different room conditions (states) has been shown to vary from person to person and may be affected by exterior conditions. This paper presents an online data-driven methodology which actively queries a new occupant for learning their personalized visual preferences. Preference is governed by a latent preference relation equivalent to a scalar utility function (the higher the utility, the higher the preference for that state). Information about user …

Methods And Algorithms For Inference Problems In Population Genetics, Jingwen Pei

Doctoral Dissertations

Inference of population history is a central problem of population genetics. The advent of large genetic data brings us not only opportunities on developing more accurate methods for inference problems, but also computational challenges. Thus, we aim at developing accurate method and fast algorithm for problems in population genetics.

Inference of admixture proportions is a classical statistical problem. We particularly focus on the problem of ancestry inference for ancestors. Standard methods implicitly assume that both parents of an individual have the same admixture fraction. However, this is rarely the case in real data. We develop a Hidden Markov Model (HMM) …

Identifying Elderlies At Risk Of Becoming More Depressed With Internet-Of-Things, Jiajue Ou, Huiguang Liang, Hwee Xian Tan

Research Collection School Of Computing and Information Systems

Depression in the elderly is common and dangerous. Current methods to monitor elderly depression, however, are costly, time-consuming and inefficient. In this paper, we present a novel depression-monitoring system that infers an elderly’s changes in depression level based on his/her activity patterns, extracted from wireless sensor data. To do so, we build predictive models to learn the relationship between depression level changes and behaviors using historical data. We also deploy the system for a group of elderly, in their homes, and run the experiments for more than one year. Our experimental study gives encouraging results, suggesting that our IoT system …

Non-Destructive Evaluation For Composite Material, Desalegn Temesgen Delelegn

Electrical & Computer Engineering Theses & Dissertations

The Nondestructive Evaluation Sciences Branch (NESB) at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) has conducted impact damage experiments over the past few years with the goal of understanding structural defects in composite materials. The Data Science Team within the NASA LaRC Office of the Chief Information Officer (OCIO) has been working with the Non-Destructive Evaluation (NDE) subject matter experts (SMEs), Dr. Cheryl Rose, from the Structural Mechanics & Concepts Branch and Dr. William Winfree, from the Research Directorate, to develop computer vision solutions using digital image processing and machine learning techniques that can help identify …

Gekko Optimization Suite, Logan Beal, Daniel Hill, Ronald Abraham Martin, John Hedengren

Faculty Publications

This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and …

Deep Reinforcement Learning For Optimal Operation Of Multipurpose Reservoir Systems, Matthew E. Peacock, John W. Labadie Dr.

International Congress on Environmental Modelling and Software

Dynamic programming (DP) is considered the ideal optimization method for solving multipurpose reservoir system operational problems since it realistically addresses their complex nonlinear, dynamic, and stochastic characteristics. The only drawback to DP is the so-called “curse of dimensionality” that has plagued the method since its inception by Richard Bellman in the 1950’s. Dimensionality issues arise from the need to discretize the state-action space and random variates which leads to an explosion in computational and memory requirements with increased state-space dimensionality. DP also requires development of spatial-temporal stochastic hydrologic models for reservoir system operations, which may be difficult under complex climatic …