Digital Commons Network^™

Analyzing Repetitive Sequences With Structured Dynamic Bayesian Networks, Thomas. L. Lake

Masters Theses

Time series often feature structure that is known a priori and easily described using natural language terms such as repetitive, symmetric, seasonal, and self-similar. However, the typical conjugate priors used in Bayesian analysis do not capture such complex phenomena well. As a result of this mismatch, known structure is modeled poorly or completely ignored. Focusing on time series with repetitive structure, this thesis proposes to overcome this problem by reducing rather than in- creasing the capacity of a well know time series model, the Hidden Markov Model. Through a careful choice in the way model capacity is reduced the model …

Semi-Supervised Pattern Recognition And Machine Learning For Eye-Tracking, Thomas B. Kinsman

Theses

The first step in monitoring an observer’s eye gaze is identifying and locating the image of their pupils in video recordings of their eyes. Current systems work under a range of conditions, but fail in bright sunlight and rapidly varying illumination. A computer vision system was developed to assist with the recognition of the pupil in every frame of a video, in spite of the presence of strong first-surface reflections off of the cornea. A modified Hough Circle detector was developed that incorporates knowledge that the pupil is darker than the surrounding iris of the eye, and is able to …

Design Of Neuromemristive Systems For Visual Information Processing, Cory E. Merkel

Theses

Neuromemristive systems (NMSs) are brain-inspired, adaptive computer architectures based on emerging resistive memory technology (memristors). NMSs adopt a mixed-signal design approach with closely-coupled memory and processing, resulting in high area and energy efficiencies. Previous work suggests that NMSs could even supplant conventional architectures in niche application domains such as visual information processing. However, given the infancy of the field, there are still several obstacles impeding the transition of these systems from theory to practice. This dissertation advances the state of NMS research by addressing open design problems spanning circuit, architecture, and system levels. Novel synapse, neuron, and plasticity circuits are …

Distributed Approach For Peptide Identification, Naga V K Abhinav Vedanbhatla

Masters Theses & Specialist Projects

A crucial step in protein identification is peptide identification. The Peptide Spectrum Match (PSM) information set is enormous. Hence, it is a time-consuming procedure to work on a single machine. PSMs are situated by a cross connection, a factual score, or a probability that the match between the trial and speculative is right and original. This procedure takes quite a while to execute. So, there is demand for enhancement of the performance to handle extensive peptide information sets. Development of appropriate distributed frameworks are expected to lessen the processing time.

The designed framework uses a peptide handling algorithm named C-Ranker, …

A Hybrid Approach To General Information Extraction, Marie Belen Grap

Master's Theses

Information Extraction (IE) is the process of analyzing documents and identifying desired pieces of information within them. Many IE systems have been developed over the last couple of decades, but there is still room for improvement as IE remains an open problem for researchers. This work discusses the development of a hybrid IE system that attempts to combine the strengths of rule-based and statistical IE systems while avoiding their unique pitfalls in order to achieve high performance for any type of information on any type of document. Test results show that this system operates competitively in cases where target information …

A New Scada Dataset For Intrusion Detection System Research, Ian P. Turnipseed

Theses and Dissertations

Supervisory Control and Data Acquisition (SCADA) systems monitor and control industrial control systems in many industrials and economic sectors which are considered critical infrastructure. In the past, most SCADA systems were isolated from all other networks, but recently connections to corporate enterprise networks and the Internet have increased. Security concerns have risen from this new found connectivity. This thesis makes one primary contribution to researchers and industry. Two datasets have been introduced to support intrusion detection system research for SCADA systems. The datasets include network traffic captured on a gas pipeline SCADA system in Mississippi State University’s SCADA lab. IDS …

An Empirical Analysis Of Predictive Machine Learning Algorithms On High-Dimensional Microarray Cancer Data, Jo A. Bill

Theses

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space p is much larger than the number of observations n. Seven gene-expression microarray cancer datasets, where the ratio κ = n/p is less than one, were chosen for evaluation. The statistical and computational challenges inherent with this type of high-dimensional low sample size (HDLSS) data were explored. The capability and performance of a diverse set of machine learning algorithms is presented and compared. The sparsity and collinearity of the data being employed, in conjunction with the complexity of the algorithms studied, …

Lidar And Machine Learning Estimation Of Hardwood Forest Biomass In Mountainous And Bottomland Environments, Bowei Xue

Graduate Theses and Dissertations

Light detection and ranging (lidar) has been applied in various forest applications, such as to retrieve forest structural information, to build statistical models for identification of tree species, and to monitor forest growth. However, despite significant progress in these areas, the choice of regression approach and parameter tuning remains an ongoing critical question. This study focused on choosing the right spatial generalization level to transform lidar point clouds to 2D images which can be further processed by mature image processing and pattern recognition approaches. It also compared the prediction ability of popular machine learning algorithms applied to aboveground forest biomass …

Detecting, Modeling, And Predicting User Temporal Intention, Hany M. Salaheldeen

Computer Science Theses & Dissertations

The content of social media has grown exponentially in the recent years and its role has evolved from narrating life events to actually shaping them. Unfortunately, content posted and shared in social networks is vulnerable and prone to loss or change, rendering the context associated with it (a tweet, post, status, or others) meaningless. There is an inherent value in maintaining the consistency of such social records as in some cases they take over the task of being the first draft of history as collections of these social posts narrate the pulse of the street during historic events, protest, riots, …

Hybrid Statistical Data Mining Framework For Multi-Commodity Fixed Charge Network Flow Problem, Anurag A. Ladage

Theses

This paper presents a new approach to analyze the network structure in multi-commodity fixed charge network flow problems (MCFCNF). This methodology uses historical data produced from repeatedly solving the traditional MCFCNF mathematical model as input for the machine-learning framework. Further, we reshape the problem as a binary classification problem and employ machine-learning algorithms to predict network structure. This predicted network structure is further used as an initial solution for our mathematical model. The quality of the initial solution generated is judged on the basis of predictive accuracy, feasibility and reduction in solving time.

Homogeneous Spiking Neuromorphic System For Real-World Pattern Recognition, Xinyu Wu, Vishal Saxena, Kehan Zhu

Electrical and Computer Engineering Faculty Publications and Presentations

A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density. Previous hybrid analog CMOS-memristor approaches required extensive CMOS circuitry for training, and thus eliminated most of the density advantages gained by the adoption of memristor synapses. Further, they used different waveforms for pre and post-synaptic spikes that added undesirable circuit overhead. Here we describe a hardware architecture that can feature a large number of memristor synapses to learn real-world patterns. We present a versatile CMOS neuron that combines integrate-and-fire behavior, drives …

Molecular And Computational Methods For Cellular State Control, Drew Groves Michael

Arts & Sciences Graduate Student Theses and Dissertations

The control of cellular state has many promising applications, including stem cell biology andregenerative medicine, biofuel production, and gene therapy. This dissertation demonstrates acomprehensive approach to cellular state control at the transcriptional level. We introduce anovel algorithm, NetSurgeon, which utilizes genome-wide gene regulatory networks to identifyinterventions that will force a cell toward a desired expression state. Following extensive insilico validation, we applied NetSurgeon to S. cerevisiae biofuel production, generatinginterventions designed to promote a fermentative state during xylose catabolism. Our selectedinterventions successfully promoted a fermentative transcriptional state and generated strainswith higher xylose import rates, improved xylose integration and increased ethanol productionrates. …

Eliciting Knowledge Bases With Defeasible Reasoning: A Comparative Analysis With Machine Learning, Peter Keogh

Dissertations

This thesis compares the ability of an implementation of Defeasible Reasoning (via Argumentation Theory) to model a construct (mental workload) with Machine Learning. In order to perform this comparison a defeasible reasoning system was designed and implemented in software. This software was used to elicit a knowledge base from an expert in an experiment which was then compared with machine learning. The central findings of this thesis were that the knowledge based approach was better at predicting an objective performance measure, time, than machine learning. However, machine learning was better equiped to identify another object measure task membership. The knowledge …

Earthen Levee Slide Detection Via Automated Analysis Of Synthetic Aperture Radar Imagery, Lalitha Dabbiru

Theses and Dissertations

The main focus of this research is to detect vulnerabilities on the Mississippi river levees using remotely sensed Synthetic Aperture Radar (SAR) imagery. Unstable slope conditions can lead to slump slides, which weaken the levees and increase the likelihood of failure during floods. On-site inspection of levees is expensive and time-consuming, so there is a need to develop efficient automated techniques based on remote sensing technologies to identify levees that are more vulnerable to failure under flood loading. Synthetic Aperture Radar technology, due to its high spatial resolution and potential soil penetration capability, is a good choice to identify problem …

Predicting Protein Contact Map By Bagging Decision Trees, Chuqiao Ren

Honors Theses

Proteins' function and structure are intrinsically related. In order to understand proteins' functionality, it is essential for medical and biological researchers to deter- mine proteins' three-dimensional structure. The traditional method using NMR spectroscopy or X-ray crystallography are inefficient compared to computational methods. Fortunately, substantial progress has been made in the prediction of protein structure in bioinformatics. Despite these achievements, the computational complexity of protein folding remains a challenge. Instead, many methods aim to predict a protein contact map from protein sequence using machine learning algorithms. In this thesis, we introduce a novel ensemble method for protein contact map prediction based …

Using Machine Learning To Classify Potential Hazard Of Environmental Chemicals In Rodents, Jie Liu

Theses and Dissertations

Over 10,000 chemicals are currently in commercial use, and hundreds of more chemicals are introduced each year. And humans are exposed to over 6,000 environmental chemicals. But only a small fraction of these chemicals have been adequately assessed for potential hazard, leading to the need to prioritize the chemicals for targeted toxicity testing. It is time-consuming and resource intensive to determine a single chemical’s potential for toxicity using animal testing. This approach is impractical for evaluating tens of thousands of chemicals. Hence, there is an urgent need to find alternatives to animal testing and a number of international efforts are …

Feature Identification And Reduction For Improved Generalization Accuracy In Secondary-Structure Prediction Using Temporal Context Inputs In Machine-Learning Models, Matthew Benjamin Seeley

Theses and Dissertations

A protein's properties are influenced by both its amino-acid sequence and its three-dimensional conformation. Ascertaining a protein's sequence is relatively easy using modern techniques, but determining its conformation requires much more expensive and time-consuming techniques. Consequently, it would be useful to identify a method that can accurately predict a protein's secondary-structure conformation using only the protein's sequence data. This problem is not trivial, however, because identical amino-acid subsequences in different contexts sometimes have disparate secondary structures, while highly dissimilar amino-acid subsequences sometimes have identical secondary structures. We propose (1) to develop a set of metrics that facilitates better comparisons between …

Automatic Synthesis Of Instruction Set Semantics And Its Applications, Niranjan Hasabnis

Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions)

Binary analysis, translation and instrumentation tools play an important role in software security. To support binaries for different processors, it is necessary to incorporate the semantics of every processor's instruction set into the tool. Unfortunately, the complexity of modern instruction sets makes the common approach of manual semantics modeling cumbersome and error-prone. Furthermore, it limits the number of processors as well as the fraction of the instruction set that is supported. In this dissertation, we propose novel architecture-neutral techniques for automatically synthesizing the semantics of instruction sets. Our approach relies on the observation that modern compilers such as GCC and …

Using Instance-Level Meta-Information To Facilitate A More Principled Approach To Machine Learning, Michael Reed Smith

Theses and Dissertations

As the capability for capturing and storing data increases and becomes more ubiquitous, an increasing number of organizations are looking to use machine learning techniques as a means of understanding and leveraging their data. However, the success of applying machine learning techniques depends on which learning algorithm is selected, the hyperparameters that are provided to the selected learning algorithm, and the data that is supplied to the learning algorithm. Even among machine learning experts, selecting an appropriate learning algorithm, setting its associated hyperparameters, and preprocessing the data can be a challenging task and is generally left to the expertise of …

Leveraging Contextual Relationships Between Objects For Localization, Clinton Leif Olson

Dissertations and Theses

Object localization is currently an active area of research in computer vision. The object localization task is to identify all locations of an object class within an image by drawing a bounding box around objects that are instances of that class. Object locations are typically found by computing a classification score over a small window at multiple locations in the image, based on some chosen criteria, and choosing the highest scoring windows as the object bounding-boxes. Localization methods vary widely, but there is a growing trend towards methods that are able to make localization more accurate and efficient through the …

Vision Based Multiple Target Tracking Using Recursive Ransac, Kyle Ingersoll

Theses and Dissertations

In this thesis, the Recursive-Random Sample Consensus (R-RANSAC) multiple target tracking (MTT) algorithm is further developed and applied to video taken from static platforms. Development of R-RANSAC is primarily focused in three areas: data association, the ability to track maneuvering objects, and track management. The probabilistic data association (PDA) filter performs very well in the R-RANSAC framework and adds minimal computation cost over less sophisticated methods. The interacting multiple models (IMM) filter as well as higher-order linear models are incorporated into R-RANSAC to improve tracking of highly maneuverable targets. An effective track labeling system, a more intuitive track merging criteria, …

Deformation Twin Nucleation And Growth Characterization In Magnesium Alloys Using Novel Ebsd Pattern Analysis And Machine Learning Tools, Travis Michael Rampton

Theses and Dissertations

Deformation twinning in Magnesium alloys both facilitates slip and forms sites for failure. Currently, basic studies of twinning in Mg are facilitated by electron backscatter diffraction (EBSD) which is able to extract a myriad of information relating to crystalline microstructures. Although much information is available via EBSD, various problems relating to deformation twinning have not been solved. This dissertation provides new insights into deformation twinning in Mg alloys, with particular focus on AZ31. These insights were gained through the development of new EBSD and related machine learning tools that extract more information beyond what is currently accessed.The first tool relating …

Cancer Risk Prediction With Next Generation Sequencing Data Using Machine Learning, Nihir Patel

Theses

The use of computational biology for next generation sequencing (NGS) analysis is rapidly increasing in genomics research. However, the effectiveness of NGS data to predict disease abundance is yet unclear. This research investigates the problem in the whole exome NGS data of the chronic lymphocytic leukemia (CLL) available at dbGaP. Initially, raw reads from samples are aligned to the human reference genome using burrows wheeler aligner. From the samples, structural variants, namely, Single Nucleotide Polymorphism (SNP) and Insertion Deletion (INDEL) are identified and are filtered using SAMtools as well as with Genome Analyzer Tool Kit (GATK). Subsequently, the variants are …

Electroencephalogram Based Causality Graph Analysis In Behavior Tasks Of Parkinson’S Disease Patients, Abdulaziz Saleh Almalaq

Electronic Theses and Dissertations

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 …

A Middleware Framework For Application-Aware And User-Specific Energy Optimization In Smart Mobile Devices, Sudeep Pasricha, Brad K. Donohoo, Chris Ohlsen

United States Air Force: Publications

munication, and social interaction. In addition to the demand for an acceptable level of performance and a comprehensive set of features, users often desire extended battery lifetime. In fact, limited battery lifetime is one of the biggest obstacles facing the current utility and future growth of increasingly sophisticated ‘‘smart’’ mobile devices. This paper proposes a novel application-aware and user-interaction aware energy optimization middleware framework (AURA) for pervasive mobile devices. AURA optimizes CPU and screen backlight energy consumption while maintaining a minimum acceptable level of performance. The proposed framework employs a novel Bayesian application classifier and management strategies based on Markov …

Cluster-Based Boosting, L. Dee Miller, Leen-Kiat Soh

School of Computing: Faculty Publications

Boosting is an iterative process that improves the predictive accuracy for supervised (machine) learning algorithms. Boosting operates by learning multiple functions with subsequent functions focusing on incorrect instances where the previous functions predicted the wrong label. Despite considerable success, boosting still has difficulty on data sets with certain types of problematic training data (e.g., label noise) and when complex functions overfit the training data. We propose a novel cluster-based boosting (CBB) approach to address limitations in boosting for supervised learning systems. Our CBB approach partitions the training data into clusters containing highly similar member data and integrates these clusters directly …

A Case Study Tested Framework For Multivariate Analyses Of Microbiomes: Software For Microbial Community Comparisons, Eric M. Spaulding

Graduate Student Theses, Dissertations, & Professional Papers

The study of microbiomes is important because our understanding of microbial communities is providing insight into human health and many other areas of interest. Researchers often use genomic data to study microbial organisms, demonstrating differences from one organism to the next. Metagenomic data is utilized to study communities of microbial organisms. The research described herein involved the development of a collection of computational methods.

This suite of computational methods and tools (written in the R and Perl languages) has become a framework used for metagenomic data analysis and result visualization. Multivariate analyses such as Linear Discriminate Analysis (LDA) are used …

Visual Saliency Estimation : A Pre-Attentive Cognitive And Context-Aware Approach, Amanda Shannon Danko

Legacy Theses & Dissertations (2009 - 2024)

At each glance, biological vision systems organize a tremendous amount of input and

Characterization Of Prose By Rhetorical Structure For Machine Learning Classification, James Java

CCAC Theses and Dissertations

Measures of classical rhetorical structure in text can improve accuracy in certain types of stylistic classification tasks such as authorship attribution. This research augments the relatively scarce work in the automated identification of rhetorical figures and uses the resulting statistics to characterize an author's rhetorical style. These characterizations of style can then become part of the feature set of various classification models.

Our Rhetorica software identifies 14 classical rhetorical figures in free English text, with generally good precision and recall, and provides summary measures to use in descriptive or classification tasks. Classification models trained on Rhetorica's rhetorical measures paired with …

Towards Improving Human-Robot Interaction For Social Robots, Saad Khan

Electronic Theses and Dissertations

Autonomous robots interacting with humans in a social setting must consider the social-cultural environment when pursuing their objectives. Thus the social robot must perceive and understand the social cultural environment in order to be able to explain and predict the actions of its human interaction partners. This dissertation contributes to the emerging field of human-robot interaction for social robots in the following ways: 1. We used the social calculus technique based on culture sanctioned social metrics (CSSMs) to quantify, analyze and predict the behavior of the robot, human soldiers and the public perception in the Market Patrol peacekeeping scenario. 2. …