Digital Commons Network^™

Predicting Changes To Source Code, Justin James Roll

Master's Theses

Organizations typically use issue tracking systems (ITS) such as Jira to plan software releases and assign requirements to developers. Organizations typically also use source control management (SCM) repositories such as Git to track historical changes to a code-base. These ITS and SCM repositories contain valuable data that remains largely untapped. As developers churn through an organization, it becomes expensive for developers to spend time determining which software artifact must be modified to implement a requirement. In this work we created, developed, tested and evaluated a tool called Class Change Predictor, otherwise known as CCP, for predicting which class will implement …

Cross-Subject Continuous Analytic Workload Profiling Using Stochastic Discrete Event Simulation, Joseph J. Giametta

Theses and Dissertations

Operator functional state (OFS) in remotely piloted aircraft (RPA) simulations is modeled using electroencephalograph (EEG) physiological data and continuous analytic workload profiles (CAWPs). A framework is proposed that provides solutions to the limitations that stem from lengthy training data collection and labeling techniques associated with generating CAWPs for multiple operators/trials. The framework focuses on the creation of scalable machine learning models using two generalization methods: 1) the stochastic generation of CAWPs and 2) the use of cross-subject physiological training data to calibrate machine learning models. Cross-subject workload models are used to infer OFS on new subjects, reducing the need to …

Methods To Address Extreme Class Imbalance In Machine Learning Based Network Intrusion Detection Systems, Russell W. Walter

Theses and Dissertations

Despite the considerable academic interest in using machine learning methods to detect cyber attacks and malicious network traffic, there is little evidence that modern organizations employ such systems. Due to the targeted nature of attacks and cybercriminals’ constantly changing behavior, valid observations of attack traffic suitable for training a classifier are extremely rare. Rare positive cases combined with the fact that the overwhelming majority of network traffic is benign create an extreme class imbalance problem. Using publically available datasets, this research examines the class imbalance problem by using small samples of the attack observations to create multiple training sets that …

A Novel Stacking Method For Multi-Label Classification, Abdulaziz Alali

Open Access Dissertations

Classical machine learning algorithms were tailored to automatically classify examples that belong to mutually exclusive classes; each example may belong to one class out of a finite set of classes. In realistic applications, however, examples often belong to more than one class at the same time. For example, a text document that belongs to Geography may also be labeled as Geology. Perhaps due to the popularity of its applications, targeting this category of problems has garnered great research interest over the past decade. A widely popular approach, called Binary Relevance (BR), is to induce a separate classifier for each class; …

Method And System For Automatic Selection Of One Or More Image Processing Algorithm, Tanushyam Chattopadhyay, Ramu Vempada Reddy, Utpal Garain

Patents

Disclosed is a method and system for automatic algorithm selection for image processing. The invention discloses the method and system for automatically selecting the correct algorithm(s) for a varying requirement of the image for processing. The selection of algorithm is completely automatic and guided by a plurality of machine learning approaches. The system here is configured to pre-process plurality of images for creating a training data. Next, the test image is extracted, pre-processed and matched for assessing the best possible match of algorithm for processing.

An Artificial Neural Networks Based Temperature Prediction Framework For Network-On-Chip Based Multicore Platform, Sandeep Aswath Narayana

Theses

Continuous improvement in silicon process technologies has made possible the integration of hundreds of cores on a single chip. However, power and heat have become dominant constraints in designing these massive multicore chips causing issues with reliability, timing variations and reduced lifetime of the chips. Dynamic Thermal Management (DTM) is a solution to avoid high temperatures on the die. Typical DTM schemes only address core level thermal issues. However, the Network-on-chip (NoC) paradigm, which has emerged as an enabling methodology for integrating hundreds to thousands of cores on the same die can contribute significantly to the thermal issues. Moreover, the …

Topic Modeling Based Cross-Source Consumer Opinions Discovery & Integration, Fan Liu

Theses and Dissertations

Online reviews can easily be accessed from e-commerce websites, dedicated web portals, blogs, discussion groups, forums, and other social media. It provides affluent information about products or services for consumers, merchants, and business analysts. It brings convenience, and also confusion. People feel overwhelmed by the information they receive from online reviews. "Is this product real good, or just so so? What are the opinions of the other consumers who had experience on the item? Why the reviews from different sites read different?" These questions are frequently asked by the review readers. This dissertation answers the above questions by first investigating …

Introducing An Automated Subject Classifier, Pru Mitchell, Tine Grimston, Robert Parkes

Information Management

The library community understands the value of controlled vocabularies in enhancing resource discovery. There is however ongoing tension between that value and the cost of maintaining and applying specialist vocabularies. This paper presents the outcomes of a 2014-15 trial of automated subject indexing at the Australian Council for Educational Research. The integration of a machine learning classification tool has resulted in streamlined workflows and increased use of machine-readable data. Insights were gained into the decisions human indexers make in using a controlled vocabulary, and into the importance of quality abstracts and metadata.

Cross-Platform Normalization Of Microarray And Rna-Seq Data For Machine Learning Applications, Jeffrey A. Thompson, Jie Tan, Casey S. Greene

Dartmouth Scholarship

Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training datasets can be created and validation can be performed on newly generated data. We developed Training Distribution Matching (TDM), which transforms RNA-seq data for use with models constructed from legacy platforms. We evaluated TDM, as well as quantile normalization, nonparanormal transformation, and a simple log …

Machine Learning In Manufacturing: Advantages, Challenges, And Applications, Thorsten Wuest, Daniel Weimer, Christopher Irgens, Klaus-Dieter Thoben

Faculty & Staff Scholarship

The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering …

A Near-To-Far Learning Framework For Terrain Characterization Using An Aerial/Ground-Vehicle Team, Ashkan Hajjam

Electronic Theses and Dissertations

In this thesis, a novel framework for adaptive terrain characterization of untraversed far terrain in a natural outdoor setting is presented. The system learns the association between visual appearance of different terrain and the proprioceptive characteristics of that terrain in a self-supervised framework. The proprioceptive characteristics of the terrain are acquired by inertial sensors recording measurements of one second traversals that are mapped into the frequency domain and later through a clustering technique classified into discrete proprioceptive classes. Later, these labels are used as training inputs to the adaptive visual classifier. The visual classifier uses images captured by an aerial …

Power Grid Management In Response To Extreme Events, Rozhin Eskandarpour

Electronic Theses and Dissertations

Power system management in response to extreme events is one the most important operational aspects of power systems. In this thesis, a novel Event-driven Security Constrained Unit Commitment (E-SCUC) model and a statistical method, based on regression and data mining to estimate the system components outages, are proposed. The proposed models help consider the simultaneous outage of several system components represented by an N-1-m reliability criterion and accordingly determine the proper system response. In addition, an optimal microgrid placement model with the objective of minimizing the cost of unserved energy to enhance power system resilience is proposed.

The …

Soil Salinity Study In Northern Great Plains Sodium Affected Soil, Tulsi P. Kharel

Electronic Theses and Dissertations

Climate and land-use changes when combined with the marine sediments that underlay portions of the Northern Great Plains have increased the salinization and sodification risks. The objectives of this dissertation were to compare three chemical amendments (calcium chloride, sulfuric acid and gypsum) remediation strategies on water permeability and sodium (Na) transport in undisturbed soil columns and to develop a remote sensing technique to characterize salinization in South Dakota soils. Fortyeight undisturbed soil columns (30 cm x 15 cm) collected from White Lake, Redfield, and Pierpont were used to assess the chemical remediation strategies. In this study the experimental design was …

Mathematical Foundations Of Sentiment Classification : A Probabilistic Approach, Syed Shahzad Raza

Legacy Theses & Dissertations (2009 - 2024)

This thesis is an introduction to the mathematical formalization of sentiment classification. It presents two popular probabilistic machine learning models to classify tweets downloaded from Twitter during the US Election Period, 2016. The thesis analyses accuracy of the two classification algorithms used. Namely, Multinomial Naïve Bayes and Bernoulli Naïve Bayes algorithms. Supervised learning approaches implemented in this thesis use approximately 600 manually labeled tweets containing information regarding the US presidential candidates. It is shown with 80% accuracy that majority of twitter users spoke in favor of Donald Trump before and after the presidential election through their tweets. We also discuss …

Using Tourmaline As An Indicator Of Provenance: Development And Application Of A Statistical Approach Using Random Forests, Erin Lael Walden

LSU Master's Theses

Tourmaline is a petrologic indicator mineral that is the major repository of boron in the earth’s crust. It forms readily when boron is present, accommodating multiple cations and anions with multiple possible substitutions for each site in the crystal structure. It is stable over a wide variety of pressures and temperatures, from near-surface P/T conditions to greater than 950 C and 7 GPa. It records information about conditions of formation, as well as pressure and temperature. Due to its resistance to chemical or physical weathering, and the negligible diffusion of elements in the crystal lattice, information about provenance is preserved. …

Characterization Of Peripheral Lung Lesions By Statistical Image Processing Of Endobronchial Ultrasound Images, Aaron T. Madaris

Browse all Theses and Dissertations

This thesis introduces the concept of implementing greyscale analysis, also known as intensity analysis, on endobronchial ultrasound (EBUS) images for the purposes of diagnosing peripheral lung tumors. The statistical methodology of using greyscale and histogram analysis allows the characterization of lung tissue in EBUS images. Regions of interest (ROI) will be analyzed in MATLAB and a feature vector will be created. A feature vector of first-order, second-order and histogram greyscale analysis will be created and used for the classification of malignant vs benign peripheral lung tumors. The tools that were implemented were MedCalc for the initial statistical analysis of receiver …

Developing A Risk Model To Target High-Risk Preventive Interventions For Sexual Assault Victimization Among Female U.S. Army Soldiers, Amy E. Street, Anthony J. Rosellini, Robert J. Ursano, Steven G. Heeringa, Eric D. Hill, John Monahan, James A. Naifeh, Maria V. Petukhova, Ben Y. Reis, Nancy A. Sampson, Paul D. Biese, Murray B. Stein, Alan M. Zaslavsky, Ronald C. Kessler

Faculty Publications

Sexual violence victimization is a significant problem among female U.S. military personnel. Preventive interventions for high-risk individuals might reduce prevalence but would require accurate targeting. We attempted to develop a targeting model for female Regular U.S. Army soldiers based on theoretically guided predictors abstracted from administrative data records. As administrative reports of sexual assault victimization are known to be incomplete, parallel machine learning models were developed to predict administratively recorded (in the population) and self-reported (in a representative survey) victimization. Capture–recapture methods were used to combine predictions across models. Key predictors included low status, crime involvement, and treated mental disorders. …

Eeg Interictal Spike Detection Using Artificial Neural Networks, Howard J. Carey Iii

Theses and Dissertations

Epilepsy is a neurological disease causing seizures in its victims and affects approximately 50 million people worldwide. Successful treatment is dependent upon correct identification of the origin of the seizures within the brain. To achieve this, electroencephalograms (EEGs) are used to measure a patient’s brainwaves. This EEG data must be manually analyzed to identify interictal spikes that emanate from the afflicted region of the brain. This process can take a neurologist more than a week and a half per patient. This thesis presents a method to extract and process the interictal spikes in a patient, and use them to reduce …

Vehicle Engine Classification Using Of Laser Vibrometry Feature Extraction, Chi Him Liu

Dissertations and Theses

Used as a non-invasive and remote sensor, the laser Doppler vibrometer (LDV) has been used in many different applications, such as inspection of aircrafts, bridge and structure and remote voice acquisition. However, using LDV as a vehicle surveillance device has not been feasible due to the lack of systematic investigations on its behavioral properties. In this thesis, the LDV data from different vehicles are examined and features are extracted. A tone-pitch indexing (TPI) scheme is developed to classify different vehicles by exploiting the engine’s periodic vibrations that are transferred throughout the vehicle’s body. Using the TPI with a two-layer feed-forward …

Direct L2 Support Vector Machine, Ljiljana Zigic

Theses and Dissertations

This dissertation introduces a novel model for solving the L2 support vector machine dubbed Direct L2 Support Vector Machine (DL2 SVM). DL2 SVM represents a new classification model that transforms the SVM's underlying quadratic programming problem into a system of linear equations with nonnegativity constraints. The devised system of linear equations has a symmetric positive definite matrix and a solution vector has to be nonnegative.

Furthermore, this dissertation introduces a novel algorithm dubbed Non-Negative Iterative Single Data Algorithm (NN ISDA) which solves the underlying DL2 SVM's constrained system of equations. This solver shows significant speedup compared to several other state-of-the-art …

Machine Learning Of Composite Concepts And The Alleviation Of The Content Completeness Problem In Text Mention Normalization, John David Osborne

All ETDs from UAB

A critical area in the field of information extraction is the assignment of ontological categories (meaning) to fragments of natural language. These fragments are termed ``utterances'' when referring to a continuous fragment or ``mentions'' when the fragment may be discontinuous. In text, most approaches to ontological assignment use named entity recognition (NER) to first discover mention boundaries. This can then be followed by a concept recognition or normalization process to assign a more precise conceptual category to the NER discovered mention. Typically this semantic assignment is represented by only a single identifier from an ontology. If the provided semantic categories …

Determining Unique Agents By Evaluating Web Form Interaction, Ben Cooley

College of Graduate Studies: Theses & Dissertations

Because of the inherent risks in today’s online activities, it becomes imperative to identify a malicious user masquerading as someone else. Incorporating biometric analysis enhances the confidence of authenticating valid users over the Internet while providing additional layers of security with no hindrance to the end user. Through the analysis of traffic patterns and HTTP Header analysis, the detection and early refusal of robot agents plays a great role in reducing fraudulent login attempts.

Harnessing The Power Of Text Mining For The Detection Of Abusive Content In Social Media, Hao Chen, Susan Mckeever, Sarah Jane Delany

Conference papers

Abstract The issues of cyberbullying and online harassment have gained considerable coverage in the last number of years. Social media providers need to be able to detect abusive content both accurately and efficiently in order to protect their users. Our aim is to investigate the application of core text mining techniques for the automatic detection of abusive content across a range of social media sources include blogs, forums, media-sharing, Q&A and chat - using datasets from Twitter, YouTube, MySpace, Kongregate, Formspring and Slashdot. Using supervised machine learning, we compare alternative text representations and dimension reduction approaches, including feature selection and …

Mining Human Activity Using Dimensionality Reduction And Pattern Recognition, Ismail El Moudden, Mounir Ouzir, Badreddine Benyacoub, Souad El Bernoussi

Research and Infrastructure Service Enterprise (RISE) Faculty Publications

Human activity recognition (HAR) is an emerging research topic in pattern recognition, especially in computer vision. The main objective of human activity recognition is to automatically detect and analyze human activities from the information acquired from different sensors. Human activity prediction using big data remains a challengingly open problem. Several approaches have recently been developed in order to find practical ways to solve high dimensionality of data problems. The aim of this study is to attempt, using data mining techniques, to deal with HAR modeling involving a significant number of variables in order to identify relevant parameters from data and …

Classification Of Breast Cancer Patients Using Somatic Mutation Profiles And Machine Learning Approaches, Suleyman Vural

Theses & Dissertations

The high degree of heterogeneity observed in breast cancers makes it very difficult to classify cancer patients into distinct clinical subgroups and consequently limits the ability to devise effective therapeutic strategies. In this study, we explore the use of gene mutation profiles to classify, characterize and predict the subgroups of breast cancers. We analyzed the whole exome sequencing data from 358 ethnically similar breast cancer patients in The Cancer Genome Atlas (TCGA) project. Identified somatic and non-synonymous single nucleotide variants were assigned a quantitative score (C-score) that represents the extent of negative impact on the function of the gene. Using …

Using Twitter To Analyze Stock Market And Assist Stock And Options Trading, Yuexin Mao

Doctoral Dissertations

Twitter has rapidly gained popularity since its creation in March 2006. Stock is a popular topic in Twitter. Many traders, investors, financial analysts and news agencies post tweets about various stocks on a daily basis. These tweets reflect their collective wisdom, and may provide important insights on the stock market. In this dissertation work, we investigate using the tweets concerning Standard & Poor 500 (S&P 500) stocks to analyze the stock markets and assist stock and option trading.

The first part of the dissertation focuses on understanding the correlation between Twitter data and stock trading volume, and predicting stock trading …

Pathological Brain Detection By A Novel Image Feature—Fractional Fourier Entropy, Shuihua Wang, Yudong Zhang, Xiaojun Yang, Ping Sun, Zhengchao Dong, Aijun Lu, Ti-Fei Yuan

Publications and Research

Aim: To detect pathological brain conditions early is a core procedure for patients so as to have enough time for treatment. Traditional manual detection is either cumbersome, or expensive, or time-consuming. We aim to offer a system that can automatically identify pathological brain images in this paper.Method: We propose a novel image feature, viz., Fractional Fourier Entropy (FRFE), which is based on the combination of Fractional Fourier Transform(FRFT) and Shannon entropy. Afterwards, the Welch’s t-test (WTT) and Mahalanobis distance (MD) were harnessed to select distinguishing features. Finally, we introduced an advanced classifier: twin support vector machine (TSVM). Results: A 10 …

The Performance Of Random Prototypes In Hierarchical Models Of Vision, Kendall Lee Stewart

Dissertations and Theses

I investigate properties of HMAX, a computational model of hierarchical processing in the primate visual cortex. High-level cortical neurons have been shown to respond highly to particular natural shapes, such as faces. HMAX models this property with a dictionary of natural shapes, called prototypes, that respond to the presence of those shapes. The resulting set of similarity measurements is an effective descriptor for classifying images. Curiously, prior work has shown that replacing the dictionary of natural shapes with entirely random prototypes has little impact on classification performance. This work explores that phenomenon by studying the performance of random prototypes on …

Automatic License Plate Recognition Using Deep Learning Techniques, Naga Surya Sandeep Angara

Electrical Engineering Theses

Automatic License Plate Recognition (ALPR) systems capture a vehicles license plate and recognize the license number and other required information from the captured image. ALPR systems have number of significant applications: law enforcement, public safety agencies, toll gate systems, etc. The goal of these systems is to recognize the characters and state on the license plate with high accuracy. ALPR has been implemented using various techniques. Traditional recognition methods use handcrafted features for obtaining features from the image. Unlike conventional methods, deep learning techniques automatically select features and are one of the game changing technologies in the field of computer …

Evaluating The Intrinsic Similarity Between Neural Networks, Stephen Charles Ashmore

Graduate Theses and Dissertations

We present Forward Bipartite Alignment (FBA), a method that aligns the topological structures of two neural networks. Neural networks are considered to be a black box, because neural networks contain complex model surface determined by their weights that combine attributes non-linearly. Two networks that make similar predictions on training data may still generalize differently. FBA enables a diversity of applications, including visualization and canonicalization of neural networks, ensembles, and cross-over between unrelated neural networks in evolutionary optimization. We describe the FBA algorithm, and describe implementations for three applications: genetic algorithms, visualization, and ensembles. We demonstrate FBA's usefulness by comparing a …