Digital Commons Network^™

Application Of Pattern Recognition And Adaptive Dsp Methods For Spatio-Temporal Analysis Of Satellite Based Hydrological Datasets, Anish Chand Turlapaty

Theses and Dissertations

Data assimilation of satellite-based observations of hydrological variables with full numerical physics models can be used to downscale these observations from coarse to high resolution to improve microwave sensor-based soil moisture observations. Moreover, assimilation can also be used to predict related hydrological variables, e.g., precipitation products can be assimilated in a land information system to estimate soil moisture. High quality spatio-temporal observations of these processes are vital for a successful assimilation which in turn needs a detailed analysis and improvement. In this research, pattern recognition and adaptive signal processing methods are developed for the spatio-temporal analysis and enhancement of soil …

Dealing With Activities With Diffuse Boundaries, Juan Ye, Lorcan Coyle, Susan Mckeever, Simon Dobson

Conference papers

Activity recognition has mainly focused to date on identifying repetitious and/or clearly delineated events. Our experience, drawing on many years’ research in smart and sensorised systems, leads us to observe that many (if not most) interesting activities fall into a different category: sporadically occurring and poorly differentiated from other concurrent activities. This implies that decision-making remains uncertain across the entire system, and suggests that progress would be greatly supported by standard evaluation methodologies and data sets.

A Comparative Study On Text Categorization, Aditya Chainulu Karamcheti

UNLV Theses, Dissertations, Professional Papers, and Capstones

Automated text categorization is a supervised learning task, defined as assigning category labels to new documents based on likelihood suggested by a training set of labeled documents. Two examples of methodology for text categorizations are Naive Bayes and K-Nearest Neighbor.

In this thesis, we implement two categorization engines based on Naive Bayes and K-Nearest Neighbor methodology. We then compare the effectiveness of these two engines by calculating standard precision and recall for a collection of documents. We will further report on time efficiency of these two engines.

Segmentation And Fracture Detection In X-Ray Images For Traumatic Pelvic Injury, Rebecca Smith

Theses and Dissertations

Due to the risk of complications such as hemorrhage, severe pelvic trauma is associated with a high mortality rate. Prompt medical treatment is therefore vital. However, the complexity of the injuries can make successful diagnosis and treatment challenging. By generating predictions and recommendations based on patient data, computer-aided decision support systems have the potential to assist physicians in improving outcomes. However, no current system considers features automatically extracted from medical images. This dissertation describes a system to extract diagnostic features from pelvic X-ray images that can be used as input to the prediction process; specifically, the presence of fracture and …

Semi-Supervised Self-Learning On Imbalanced Data Sets, John Nicholas Korecki

USF Tampa Graduate Theses and Dissertations

Semi-supervised self-learning algorithms have been shown to improve classifier accuracy under a variety of conditions. In this thesis, semi-supervised self-learning using ensembles of random forests and fuzzy c-means clustering similarity was applied to three data sets to show where improvement is possible over random forests alone. Two of the data sets are emulations of large simulations in which the data may be distributed. Additionally, the ratio of majority to minority class examples in the training set was altered to examine the effect of training set bias on performance when applying the semi-supervised algorithm.

Developing Cyberspace Data Understanding Using Crisp-Dm For Host-Based Ids Feature Mining, Joseph R. Erskine, Gilbert L. Peterson, Barry E. Mullins, Michael R. Grimaila

Faculty Publications

Current intrusion detection systems (IDS) generate a large number of specific alerts, but typically do not provide actionable information. Compounding this problem is the fact that many alerts are false positive alerts. This paper applies the Cross Industry Standard Process for Data Mining (CRISP-DM) to develop an understanding of a host environment under attack. Data is generated by launching scans and exploits at a machine outfitted with a set of host-based forensic data collectors. Through knowledge discovery, features are selected to project human understanding of the attack process into the IDS model. By discovering relationships between the data collected and …

Permutation-Based Pathway Testing Using The Super Learner Algorithm, Paul Chaffee, Alan E. Hubbard, Mark L. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Many diseases and other important phenotypic outcomes are the result of a combination of factors. For example, expression levels of genes have been used as input to various statistical methods for predicting phenotypic outcomes. One particular popular variety is the so-called gene set enrichment analysis (GSEA). This paper discusses an augmentation to an existing strategy to estimate the significance of an associations between a disease outcome and a predetermined combination of biological factors, based on a specific data adaptive regression method (the "Super Learner," van der Laan et al., 2007). The procedure uses an aggressive search procedure, potentially resulting in …

Extensions Of Nearest Shrunken Centroid Method For Classification, Tomohiko Funai

Theses and Dissertations

Stylometry assumes that the essence of the individual style of an author can be captured using a number of quantitative criteria, such as the relative frequencies of noncontextual words (e.g., or, the, and, etc.). Several statistical methodologies have been developed for authorship analysis. Jockers et al. (2009) utilize Nearest Shrunken Centroid (NSC) classification, a promising classification methodology in DNA microarray analysis for authorship analysis of the Book of Mormon. Schaalje et al. (2010) develop an extended NSC classification to remedy the problem of a missing author. Dabney (2005) and Koppel et al. (2009) suggest other modifications of NSC. This paper …

A Bayesian Decision Theoretical Approach To Supervised Learning, Selective Sampling, And Empirical Function Optimization, James Lamond Carroll

Theses and Dissertations

Many have used the principles of statistics and Bayesian decision theory to model specific learning problems. It is less common to see models of the processes of learning in general. One exception is the model of the supervised learning process known as the "Extended Bayesian Formalism" or EBF. This model is descriptive, in that it can describe and compare learning algorithms. Thus the EBF is capable of modeling both effective and ineffective learning algorithms. We extend the EBF to model un-supervised learning, semi-supervised learning, supervised learning, and empirical function optimization. We also generalize the utility model of the EBF to …

A Robust Gene Selection Method For Microarray-Based Cancer Classification., Xiaosheng Wang, Osamu Gotoh

Journal Articles: Genetics, Cell Biology & Anatomy

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification …

Quantification Of Artistic Style Through Sparse Coding Analysis In The Drawings Of Pieter Bruegel The Elder, James M. Hughes, Daniel J. Graham, Daniel N. Rockmore

Dartmouth Scholarship

Recently, statistical techniques have been used to assist art historians in the analysis of works of art. We present a novel technique for the quantification of artistic style that utilizes a sparse coding model. Originally developed in vision research, sparse coding models can be trained to represent any image space by maximizing the kurtosis of a representation of an arbitrarily selected image from that space. We apply such an analysis to successfully distinguish a set of authentic drawings by Pieter Bruegel the Elder from another set of well-known Bruegel imitations. We show that our approach, which involves a direct comparison …

Prediction Of Brain Tumor Progression Using A Machine Learning Technique, Yuzhong Shen, Debrup Banerjee, Jiang Li, Adam Chandler, Yufei Shen, Frederic D. Mckenzie, Jihong Wang, Nico Karssemeijer (Ed.), Ronald M. Summers (Ed.)

Electrical & Computer Engineering Faculty Publications

A machine learning technique is presented for assessing brain tumor progression by exploring six patients' complete MRI records scanned during their visits in the past two years. There are ten MRI series, including diffusion tensor image (DTI), for each visit. After registering all series to the corresponding DTI scan at the first visit, annotated normal and tumor regions were overlaid. Intensity value of each pixel inside the annotated regions were then extracted across all of the ten MRI series to compose a 10 dimensional vector. Each feature vector falls into one of three categories:normal, tumor, and normal but progressed to …

A Boosting Framework For Visuality-Preserving Distance Metric Learning And Its Application To Medical Image Retrieval, Yang Liu, Rong Jin, Lily Mummert, Rahul Sukthankar, Adam Goode, Bin Zheng, Steven C. H. Hoi, Mahadev Satyanarayanan

Research Collection School Of Computing and Information Systems

Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, "similarity" can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one …

Vowel Recognition From Continuous Articulatory Movements For Speaker-Dependent Applications, Jun Wang, Jordan R. Green, Ashok Samal, Tom D. Carrell

Department of Special Education and Communication Disorders: Faculty Publications

A novel approach was developed to recognize vowels from continuous tongue and lip movements. Vowels were classified based on movement patterns (rather than on derived articulatory features, e.g., lip opening) using a machine learning approach. Recognition accuracy on a single-speaker dataset was 94.02% with a very short latency. Recognition accuracy was better for high vowels than for low vowels. This finding parallels previous empirical findings on tongue movements during vowels. The recognition algorithm was then used to drive an articulation-to-acoustics synthesizer. The synthesizer recognizes vowels from continuous input stream of tongue and lip movements and plays the corresponding sound samples …

Noninvasive Estimation Of Pulmonary Artery Pressure Using Heart Sound Analysis, Aaron W. Dennis

Theses and Dissertations

Right-heart catheterization is the most accurate method for estimating pulmonary artery pressure (PAP). Because it is an invasive procedure it is expensive, exposes patients to the risk of infection, and is not suited for long-term monitoring situations. Medical researchers have shown that PAP influences the characteristics of heart sounds. This suggests that heart sound analysis is a potential noninvasive solution to the PAP estimation problem. This thesis describes the development of a prototype system, called PAPEr, which estimates PAP noninvasively using heart sound analysis. PAPEr uses patient data with machine learning algorithms to build models of how PAP affects heart …

Creating Insanity In Learning Systems: Addressing Ambiguity Effects Of Predicting Non-Linear Continuous Valued Functions With Reconstructabilty Analysis From Large Categorically Valued Input Data Sets, William D. Eisenhauer

Systems Science Friday Noon Seminar Series

Being told to give two different, and potentially counter, responses to the same stimulus can set up a double bind in humans, leading to a type of insanity. So what how do you deal with it when it comes up quite frequently in modeling through simplification and removal of predictive variables?

In his current dissertation research Ike Eisenhauer is using reconstructability analysis to implement K-System, U-System, and B-System approaches to predict a continuously valued function through discrete categorically valued input variables [e.g. textual data]. One of the key issues is how to address the inability of K-Systems and U-Systems to …

A Neural Network Approach To Border Gateway Protocol Peer Failure Detection And Prediction, Cory B. White

Master's Theses

The size and speed of computer networks continue to expand at a rapid pace, as do the corresponding errors, failures, and faults inherent within such extensive networks. This thesis introduces a novel approach to interface Border Gateway Protocol (BGP) computer networks with neural networks to learn the precursor connectivity patterns that emerge prior to a node failure. Details of the design and construction of a framework that utilizes neural networks to learn and monitor BGP connection states as a means of detecting and predicting BGP peer node failure are presented. Moreover, this framework is used to monitor a BGP network …

Learning Condition-Specific Networks, Sushmita Roy

Computer Science ETDs

Condition-specific cellular networks are networks of genes and proteins that describe functional interactions among genes occurring under different environmental conditions. These networks provide a systems-level view of how the parts-list (genes and proteins) interact within the cell as it functions under changing environmental conditions and can provide insight into mechanisms of stress response, cellular differentiation and disease susceptibility. The principle challenge, however, is that cellular networks remain unknown for most conditions and must be inferred from activity levels of genes (mRNA levels) under different conditions. This dissertation aims to develop computational approaches for inferring, analyzing and validating cellular networks of …

Automatic Red Tide Detection Using Modis Satellite Images, Wijian Cheng

USF Tampa Graduate Theses and Dissertations

Red tides pose a significant economic and environmental threat in the Gulf of Mexico. Detecting red tide is important for understanding this phenomenon. In this thesis, machine learning approaches based on Random Forests, Support Vector Machines and K-Nearest Neighbors have been evaluated for red tide detection from MODIS satellite images. Detection results using machine learning algorithms were compared to ship collected ground truth red tide data. This work has three major contributions. First, machine learning approaches outperformed two of the latest thresholding red tide detection algorithms based on bio-optical characterization by more than 10% in terms of F measure and …

Predictive Decoding Of Neural Data, Yaroslav O. Halchenko

Dissertations

In the last five decades the number of techniques available for non-invasive functional imaging has increased dramatically. Researchers today can choose from a variety of imaging modalities that include EEG, MEG, PET, SPECT, MRI, and fMRI.

This doctoral dissertation offers a methodology for the reliable analysis of neural data at different levels of investigation. By using statistical learning algorithms the proposed approach allows single-trial analysis of various neural data by decoding them into variables of interest. Unbiased testing of the decoder on new samples of the data provides a generalization assessment of decoding performance reliability. Through consecutive analysis of the …

Application Of Machine Learning Techniques For Real-Time Classification Of Sensor Array Data, Sichu Li

LSU New Orleans Theses and Dissertations

There is a significant need to identify approaches for classifying chemical sensor array data with high success rates that would enhance sensor detection capabilities. The present study attempts to fill this need by investigating six machine learning methods to classify a dataset collected using a chemical sensor array: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Classification and Regression Trees (CART), Random Forest (RF), Naïve Bayes Classifier (NB), and Principal Component Regression (PCR). A total of 10 predictors that are associated with the response from 10 sensor channels are used to train and test the classifiers. A training dataset of 4 …

Prediction Of Protein Function And Functional Sites From Protein Sequences, Jing Hu

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

High-throughput genomics projects have resulted in a rapid accumulation of protein sequences. Therefore, computational methods that can predict protein functions and functional sites efficiently and accurately are in high demand. In addition, prediction methods utilizing only sequence information are of particular interest because for most proteins, 3-dimensional structures are not available. However, there are several key challenges in developing methods for predicting protein function and functional sites. These challenges include the following: the construction of representative datasets to train and evaluate the method, the collection of features related to the protein functions, the selection of the most useful features, and …

A Strategy Oriented, Machine Learning Approach To Automatic Quality Assessment Of Wikipedia Articles, Gabriel De La Calzada

Master's Theses

This work discusses an approach to modeling and measuring information quality of Wikipedia articles. The approach is based on the idea that the quality of Wikipedia articles with distinctly different profiles needs to be measured using different information quality models. To implement this approach, a software framework written in the Java language was developed to collect and analyze information of Wikipedia articles.

We report on our initial study, which involved two categories of Wikipedia articles: ”stabilized” (those, whose content has not undergone major changes for a significant period of time) and ”controversial” (articles that have undergone vandalism, revert wars, or …

Quantum Inductive Learning And Quantum Logic Synthesis, Martin Lukac

Dissertations and Theses

Since Quantum Computer is almost realizable on large scale and Quantum Technology is one of the main solutions to the Moore Limit, Quantum Logic Synthesis (QLS) has become a required theory and tool for designing Quantum Logic Circuits. However, despite its growth, there is no any unified aproach to QLS as Quantum Computing is still being discovered and novel applications are being identified.

The intent of this study is to experimentally explore principles of Quantum Logic Synthesis and its applications to Inductive Machine Learning. Based on algorithmic approach, I first design a Genetic Algorithm for Quantum Logic Synthesis that is …

Bootstrapping Events And Relations From Text, Ting Liu

Legacy Theses & Dissertations (2009 - 2024)

Information Extraction (IE) is a technique for automatically extracting structured data from text documents. One of the key analytical tasks is extraction of important and relevant information from textual sources. While information is plentiful and readily available, from the Internet, news services, media, etc., extracting the critical nuggets that matter to business or to national security is a cognitively demanding and time consuming task. Intelligence and business analysts spend many hours poring over endless streams of text documents pulling out reference to entities of interest (people, locations, organizations) as well as their relationships as reported in text. Such extracted "information …

Machine Learned Melody Matching Using Strictly Relative Musical Abstractions, Michael Joseph Kolta

Legacy Theses & Dissertations (2009 - 2024)

We implement and evaluate a machine learning approach to improve systems for searching a database of music via melodic sample. We explore symbolic and aural input queries and test our prototypes with extensive user surveys. Our main contribution is to combine the following four elements. First is to create a unique musical abstraction that accounts for both pitch and rhythm in a relative manner. Second, our system allows for approximate matching of imperfect queries via the utilization of the Smith-Waterman algorithm that was originally designed for approximate matching of molecular subsequences, such as DNA samples. Third is to design our …

Using Student Mood And Task Performance To Train Classifier Algorithms To Select Effective Coaching Strategies Within Intelligent Tutoring Systems (Its), Robert Sottilare

Electronic Theses and Dissertations

The ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System's (ITS) coaching strategy based on the student's mood. As a step toward this goal, this study evaluated the relationships between each student's mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student's performance. Outcomes included methods to increase the perception of the intelligent tutor to allow it to adapt coaching strategies (methods of instruction) to the student's affective needs to mitigate barriers to performance (e.g. negative affect) during the one-to-one tutoring process. The study evaluated …

Concept Learning By Example Decomposition, Sameer Joshi

Electronic Theses and Dissertations

For efficient understanding and prediction in natural systems, even in artificially closed ones, we usually need to consider a number of factors that may combine in simple or complex ways. Additionally, many modern scientific disciplines face increasingly large datasets from which to extract knowledge (for example, genomics). Thus to learn all but the most trivial regularities in the natural world, we rely on different ways of simplifying the learning problem. One simplifying technique that is highly pervasive in nature is to break down a large learning problem into smaller ones; to learn the smaller, more manageable problems; and then to …

Analyses Of Crash Occurence And Injury Severities On Multi Lane Highways Using Machine Learning Algorithms, Abhishek Das

Electronic Theses and Dissertations

Reduction of crash occurrence on the various roadway locations (mid-block segments; signalized intersections; un-signalized intersections) and the mitigation of injury severity in the event of a crash are the major concerns of transportation safety engineers. Multi lane arterial roadways (excluding freeways and expressways) account for forty-three percent of fatal crashes in the state of Florida. Significant contributing causes fall under the broad categories of aggressive driver behavior; adverse weather and environmental conditions; and roadway geometric and traffic factors. The objective of this research was the implementation of innovative, state-of-the-art analytical methods to identify the contributing factors for crashes and injury …

Real-Time Automatic Price Prediction For Ebay Online Trading, Ilya Igorevitch Raykhel

Theses and Dissertations

While Machine Learning is one of the most popular research areas in Computer Science, there are still only a few deployed applications intended for use by the general public. We have developed an exemplary application that can be directly applied to eBay trading. Our system predicts how much an item would sell for on eBay based on that item's attributes. We ran our experiments on the eBay laptop category, with prior trades used as training data. The system implements a feature-weighted k-Nearest Neighbor algorithm, using genetic algorithms to determine feature weights. Our results demonstrate an average prediction error of 16%; …