Digital Commons Network^™

Mint: Model Integration Through Knowledge-Powered Data And Process Composition, Yolanda Gil, Kelly Cobourn, Ewa Deelman, Chris Duffy, Rafael Ferreira Da Silva, Armen Kemanian, Craig Knoblock, Vipin Kumar, Scott Peckham, Lucas Augusto Carvalho, Yao-Yi Chang, Daniel Garijo, Ankush Khandelwal, Deborah Khider, Minh Pahm, Jay Pujara, Varun Ratnakar, Maria Stoica, Binh Vu

International Congress on Environmental Modelling and Software

Major societal and environmental challenges require forecasting how natural processes and human activities affect one another. Model integration across natural and social science disciplines to study these problems requires resolving semantic, spatio-temporal, and execution mismatches, which are largely done by hand today and may take more than two years of human effort. We are developing the Model INTegration (MINT) framework that incorporates extensive knowledge about models and data, with several innovative components: 1) New principle-based ontology generation tools for modeling variables, used to describe models and data; 2) A novel workflow system that selects relevant models from a curated registry …

Evaluating Beach Water Quality And Dengue Fever Risk Factors By Satellite Remote Sensing And Artificial Neural Networks, Abdiel Elias Laureano-Rosario

USF Tampa Graduate Theses and Dissertations

Climatic variations, together with large-scale environmental forces and human development affect the quality of coastal recreational waters, creating potential risks to human health. These environmental forces, including increased temperature and precipitation, often promote specific vector-borne diseases in the Caribbean and Gulf of Mexico. Human activities affect water quality through discharges from urban areas, including nutrient and other pollutants derived from wastewater systems. Both water quality of recreational beaches and vector-borne diseases can be better managed by understanding their relationship with local environmental forces.

I evaluated how changes in vector-borne diseases and poor recreational water quality were related to specific environmental …

An Exploration Of Linear Classifiers For Unsupervised Spiking Neural Networks With Event-Driven Data, Wesley Chavez

Dissertations and Theses

Object recognition in video has seen giant strides in accuracy improvements in the last few years, a testament to the computational capacity of deep convolutional neural networks. However, this computational capacity of software-based neural networks coincides with high power consumption compared to that of some spiking neural networks (SNNs), up to 300,000 times more energy per synaptic event in IBM's TrueNorth chip, for example. SNNs are also well-suited to exploit the precise timing of event-driven image sensors, which transmit asynchronous "events" only when the luminance of a pixel changes above or below a threshold value. The combination of event-based imagers …

Software Requirements Classification Using Word Embeddings And Convolutional Neural Networks, Vivian Lin Fong

Master's Theses

Software requirements classification, the practice of categorizing requirements by their type or purpose, can improve organization and transparency in the requirements engineering process and thus promote requirement fulfillment and software project completion. Requirements classification automation is a prominent area of research as automation can alleviate the tediousness of manual labeling and loosen its necessity for domain-expertise.

This thesis explores the application of deep learning techniques on software requirements classification, specifically the use of word embeddings for document representation when training a convolutional neural network (CNN). As past research endeavors mainly utilize information retrieval and traditional machine learning techniques, we entertain …

Prediction Of Drugs To Be Prescribed To A Diabetic, Ojasva Jain

Manipal Journal of Science and Technology

The purpose of this research is to review the outcome measures used to evaluate a prevailing disease, in this case, diabetes and make recommendations for the incoming future patients.

Advanced Malware Detection For Android Platform, Ke Xu

Dissertations and Theses Collection (Open Access)

In the first quarter of 2018, 75.66% of smartphones sales were devices running An- droid. Due to its popularity, cyber-criminals have increasingly targeted this ecosys- tem. Malware running on Android severely violates end users security and privacy, allowing many attacks such as defeating two factor authentication of mobile bank- ing applications, capturing real-time voice calls and leaking sensitive information. In this dissertation, I describe the pieces of work that I have done to effectively de- tect malware on Android platform, i.e., ICC-based malware detection system (IC- CDetector), multi-layer malware detection system (DeepRefiner), and self-evolving and scalable malware detection system (DroidEvolver) …

N-Slope: A One-Class Classification Ensemble For Nuclear Forensics, Justin Kehl

Master's Theses

One-class classification is a specialized form of classification from the field of machine learning. Traditional classification attempts to assign unknowns to known classes, but cannot handle novel unknowns that do not belong to any of the known classes. One-class classification seeks to identify these outliers, while still correctly assigning unknowns to classes appropriately. One-class classification is applied here to the field of nuclear forensics, which is the study and analysis of nuclear material for the purpose of nuclear incident investigations. Nuclear forensics data poses an interesting challenge because false positive identification can prove costly and data is often small, high-dimensional, …

How Copyright Law Can Fix Artificial Intelligence's Implicit Bias Problem, Amanda Levendowski

Georgetown Law Faculty Publications and Other Works

As the use of artificial intelligence (AI) continues to spread, we have seen an increase in examples of AI systems reflecting or exacerbating societal bias, from racist facial recognition to sexist natural language processing. These biases threaten to overshadow AI’s technological gains and potential benefits. While legal and computer science scholars have analyzed many sources of bias, including the unexamined assumptions of its often homogenous creators, flawed algorithms, and incomplete datasets, the role of the law itself has been largely ignored. Yet just as code and culture play significant roles in how AI agents learn about and act in the …

Exact Recovery Of Prototypical Atoms Through Dictionary Initialization, Greg Zanotti, Enrico Au-Yeung

DePaul Discoveries

In dictionary learning, a matrix comprised of signals Y is factorized into the product of two matrices: a matrix of prototypical "atoms" D, and a sparse matrix containing coefficients for atoms in D, called X. Dictionary learning finds applications in signal processing, image recognition, and a number of other fields. Many algorithms for solving the dictionary learning problem follow the alternating minimization paradigm; that is, by alternating solving for D and X. In 2014, Agarwal et al. proposed a dictionary initialization procedure that is used before this alternating minimization process. We show that there is a …

Narrowing The Scope Of Failure Prediction Using Targeted Fault Load Injection, Paul L. Jordan, Gilbert L. Peterson, Alan C. Lin, Michael J. Mendenhall, Andrew J. Sellers

Faculty Publications

As society becomes more dependent upon computer systems to perform increasingly critical tasks, ensuring that those systems do not fail becomes increasingly important. Many organizations depend heavily on desktop computers for day-to-day operations. Unfortunately, the software that runs on these computers is written by humans and, as such, is still subject to human error and consequent failure. A natural solution is to use statistical machine learning to predict failure. However, since failure is still a relatively rare event, obtaining labelled training data to train these models is not a trivial task. This work presents new simulated fault-inducing loads that extend …

Theory, Operation, And Application Of Neural Networks, John Andersen

University Honors Theses

This paper examines the history and current state of machine learning. It examines neural networks, theory behind neural networks, how they are implemented, and how they are used. The systems and networks examined have up to three modes of learning. Theory behind machine learning is broken up into three approaches; rule-based, Bayesian, and neural networks. Operation of machine learning algorithms has been enabled by several prevalent libraries in the open source community, as well as various hardware technologies. Due to this surge in resources application developers have been able to apply machine learning in novel ways. An application of machine …

An Investigation Of The Cortical Learning Algorithm, Anthony C. Samaritano

Theses and Dissertations

Pattern recognition and machine learning fields have revolutionized countless industries and applications from biometric security to modern industrial assembly lines. The fields continue to accelerate as faster, more efficient processing hardware becomes commercially available. Despite the accelerated growth of the pattern recognition and machine learning fields, computers still are unable to learn, reason, and perform rudimentary tasks that humans and animals find routine. Animals are able to move fluidly, understand their environment, and maximize their chances of survival through adaptation - animals demonstrate intelligence. A primary argument in this thesis that we have not yet achieved a level of intelligence …

Design Of A Distributed Real-Time E-Health Cyber Ecosystem With Collective Actions: Diagnosis, Dynamic Queueing, And Decision Making, Yanlin Zhou

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

In this thesis, we develop a framework for E-health Cyber Ecosystems, and look into different involved actors. The three interested parties in the ecosystem including patients, doctors, and healthcare providers are discussed in 3 different phases. In Phase 1, machine-learning based modeling and simulation analysis is performed to remotely predict a patient's risk level of having heart diseases in real time. In Phase 2, an online dynamic queueing model is devised to pair doctors with patients having high risk levels (diagnosed in Phase 1) to confirm the risk, and provide help. In Phase 3, a decision making paradigm is proposed …

Machine Learning Applications In Graduation Prediction At The University Of Nevada, Las Vegas, Elliott Collin Ploutz

UNLV Theses, Dissertations, Professional Papers, and Capstones

Graduation rates of four-year institutions are an increasingly important metric to incoming students and for ranking universities. To increase completion rates, universities must analyze available student data to understand trends and factors leading to graduation. Using predictive modeling, incoming students can be assessed as to their likelihood of completing a degree. If students are predicted to be most likely to drop out, interventions can be enacted to increase retention and completion rates.

At the University of Nevada, Las Vegas (UNLV), four-year graduation rates are 15% and six-year graduation rates are 39%. To improve these rates, we have gathered seven years …

Discerning Drivers Of Cancer: Computational Approaches To Somatic Exome Sequencing Data, Runjun Kumar

Arts & Sciences Graduate Student Theses and Dissertations

Paired tumor-normal sequencing of thousands of patient’s exomes has revealed millions of somatic mutations, but functional characterization and clinical decision making are stymied because biologically neutral ‘passenger’ mutations greatly outnumber pathogenic ‘driver’ mutations. Since most mutations will return negative results if tested, conventional resource-intensive experiments are reserved for mutations which are observed in multiple patients or rarer mutations found in well-established cancer genes. Most mutations are therefore never tested, diminishing the potential to discover new mechanisms of cancer development and treatment opportunities. Computational methods that reliably prioritize mutations for testing would greatly increase the translation of sequencing results to clinical …

Malware Image Classification Using Machine Learning With Local Binary Pattern, Jhu-Sin Luo, Dan Lo

Master of Science in Computer Science Theses

Malware classification is a critical part in the cybersecurity.

Traditional methodologies for the malware classification

typically use static analysis and dynamic analysis to identify malware.

In this paper, a malware classification methodology based

on its binary image and extracting local binary pattern (LBP)

features are proposed. First, malware images are reorganized into

3 by 3 grids which is mainly used to extract LBP feature. Second,

the LBP is implemented on the malware images to extract features

in that it is useful in pattern or texture classification. Finally,

Tensorflow, a library for machine learning, is applied to classify

malware images with …

A Multiple Classifier System For Predicting Best-Selling Amazon Products, Michael Kranzlein

Master of Science in Computer Science Theses

In this work, I examine a dataset of Amazon product metadata and propose a heterogeneous multiple classifier system for the task of identifying best-selling products in multiple categories. This system of classifiers consumes the product description and the featured product image as input and feeds them through binary classifiers of the following types: Convolutional Neural Network, Na¨ıve Bayes, Random Forest, Ridge Regression, and Support Vector Machine. While each individual model is largely successful in identifying best-selling products from non best-selling products and from worst-selling products, the multiple classifier system is shown to be stronger than any individual model in the …

Experiences Building, Training, And Deploying A Chatbot In An Academic Library, David Meincke

Library Staff Publications

No abstract provided.

Cardiovascular Biometrics To Secure The Internet Of Things, Nima Karimianbahnemiri

Doctoral Dissertations

IoT wearables and mobile devices have the potential to dramatically lower healthcare costs through remote health monitoring but come with risks of fraud due to the lack of physical presence at the time of collection. Many of the problems are related to access control and can be at least partially alleviated by incorporating biometric authentication.

Biometrics have several benefits. First, they are more convenient than passwords, thereby preserving the spirit of IoT. Second, if appropriately selected, they could have low probability of circumvention. Third, the incorporation of biometrics into IoT brings concerns of cost and implementing a “user-friendly” design. While …

Fusion Of Evolution Constructed Features For Computer Vision, Stanton Robert Price

Theses and Dissertations

In this dissertation, image feature extraction quality is enhanced through the introduction of two feature learning techniques and, subsequently, feature-level fusion strategies are presented that improve classification performance. Two image/signal processing techniques are defined for pre-conditioning image data such that the discriminatory information is highlighted for improved feature extraction. The first approach, improved Evolution-COnstructed features, employs a modified genetic algorithm to learn a series of image transforms, specific to a given feature descriptor, for enhanced feature extraction. The second method, Genetic prOgramming Optimal Feature Descriptor (GOOFeD), is a genetic programming-based approach to learning the transformations of the data for feature …

Concatenated Decision Paths Classification For Time Series Shapelets - A New Approach For One Dimensional Data Classification And Its Application, Ivan Stefanov Mitzev

Theses and Dissertations

Time series are very common in presenting collected data such as economic indicators, natural phenomenon, control engineering data, among others. In the last decade, the interest in time series data mining increased as the amount of collected data increased dramatically. Standard approaches for time series classification are based on collecting distance measures, such as the Euclidian distance (ED) and dynamic time warping (DTW) along with 1-NN classifier for further classification. Recently, more advanced types of classification were found, introducing primitives (named time series shapelet) that consistently represent a certain class. The time series shapelet is a small sub-section of the …

Time Course Of Brain Network Reconfiguration Supporting Inhibitory Control, Tzvetan Popov, Britta U. Westner, Rebecca L. Silton, Sarah M. Sass, Jeffrey M. Speilberg, Brigitte Rockstroh, Wendy Heller, Gregory A. Miller

Psychology: Faculty Publications and Other Works

Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total N …

Face Detection And Recognition Using Moving Window Accumulator With Various Deep Learning Architecture, Anil Kumar Nayak

Computer Science and Engineering Theses - Archive

Recent advancement in the field of Computer Vision and Deep Learning is making object detection and recognition easier. Hence, growing research activities in the field of deep learning are enabling researchers to find new ideas in the area of face detection and recognition. Implementation of such systems has a number of challenges when it comes to the current approaches. In this paper, we have presented a system of Face Detection and Recognition with newly designed deep learning classification models like CNN, Inception and various state of art models like SVM and we also compared the result with FaceNet. Multiple approaches …

From Text Classification To Image Clustering, Problems Less Optimized, Amirhossein Herandi

Computer Science and Engineering Theses - Archive

Machine Learning is thriving. Every industry is using its techniques in some way to improve their efficiency and revenue. However, the focus on research is not divided equally between all of the different areas and problems that this field can tackle and analyze. Currently, Computer Vision is the one area that is being focused very extensively by researchers and companies alike, and as a result has seen an amazing boost in the recent years. This ranges from the well-known problems of classification that use discriminative models all the way to more novel problems that use generative models such as style …

A Two Stage Event Based Data Driven Controller For Improved Grasping Of An Artificial Hand, Christopher Abrego

Mechanical and Aerospace Engineering Dissertations - Archive

The human hand is one of the greatest (if not the greatest) tool known to mankind for grasping objects. So much so, that researchers have been investigating the development of artificial biomimetic hands in an effort to mimic their functionality and dexterity with the indent to apply the technology to various robotic platforms; ranging from end effectors for industrial pick-and-place robotics, to upper-limb prosthetics, to humanoids. There are certain features that make this endeavor challenging such as the mechanical design, actuation and sensorization, and functionality as well as the interaction from both the view point of interacting with an end …

On The Development And Integration Of Pneumatic Extrusion Module And A Methodology To Identify Process Parameters For Additive Manufacturing Using Machine Learning, Kashish Dhal

Mechanical and Aerospace Engineering Theses - Archive

Commonly used additive manufacturing platforms have a single extrusion module based on Fused Filament Fabrication (FFF) and their processing software generates G-Codes for this FFF module using defined process parameters. These platforms and software do not accommodate different processing modules such as viscous extruders or Direct Ink Writing (DIW). This research is focused on the development of a Pneumatic Extrusion Module (PEM) capable of dispensing viscous materials such as gels or slurries controlled through a digital pneumatic valve. A PEM is developed, integrated and its performance is evaluated on a multi-modality additive manufacturing platform in the MARS Lab. The operation …

Deep Learning For Recognition Of Objects, Activities, Faces, And Spatio-Temporal Patterns, Amir Ghaderi

Computer Science and Engineering Dissertations - Archive

A popular method in machine learning is Convolutional Neural Network (CNN). CNN had was of high interest to the research community in the 1990s, but after that its popularity receded compared to the Support Vector Machine Support Vector Machine (SVM)[1]. One of the reasons was the relatively lower computational demands of SVM. Training CNNs requires significantly more computational power, time, and data than training SVM. One of the important issues in showing the power of the CNN is the availability of the huge amount of data and introducing big datasets. With increased availability of powerful GPU processing, using several improvements …

A Framework For Cardio-Pulmonary Resuscitation (Cpr) Scene Retrieval From Medical Simulation Videos Based On Object And Activity Detection., Anju Panicker Madhusoodhanan Sathik

Electronic Theses and Dissertations

In this thesis, we propose a framework to detect and retrieve CPR activity scenes from medical simulation videos. Medical simulation is a modern training method for medical students, where an emergency patient condition is simulated on human-like mannequins and the students act upon. These simulation sessions are recorded by the physician, for later debriefing. With the increasing number of simulation videos, automatic detection and retrieval of specific scenes became necessary. The proposed framework for CPR scene retrieval, would eliminate the conventional approach of using shot detection and frame segmentation techniques. Firstly, our work explores the application of Histogram of Oriented …

Predicting Customer Complaints In Mobile Telecom Industry Using Machine Learning Algorithms, Chiyoung Choi

Open Access Theses

Mobile telecom industry competition has been fierce for decades, therefore increasing the importance of customer retention. Most mobile operators consider customer complaints as a key factor of customer retention. We implement machine learning algorithms to predict the customer complaints of a Korean mobile telecom company. We used four machine learning algorithms ANN (Artificial Neural Network), SVM (Support Vector Machine), KNN (K-Nearest Neighbors) and DT (Decision Tree). Our experiment utilized a database of 10,000 Korean mobile market subscribers and the variables of gender, age, device manufacturer, service quality, and complaint status. We found that ANN’s prediction performance outperformed other algorithms. We …

Horse Racing Prediction Using Graph-Based Features., Mehmet Akif Gulum

Electronic Theses and Dissertations

This thesis presents an applied horse racing prediction using graph based features on a set of horse races data. We used artificial neural network and logistic regression models to train then test to prediction without graph based features and with graph based features. This thesis can be explained in 4 main parts. Collect data from a horse racing website held from 2015 to 2017. Train data to using predictive models and make a prediction. Create a global directed graph of horses and extract graph-based features (Core Part) . Add graph based features to basic features and train to using same …