Computer Sciences Commons^™

Assessment And Prediction Of Cardiovascular Status During Cardiac Arrest Through Machine Learning And Dynamical Time-Series Analysis, Sharad Shandilya

Theses and Dissertations

In this work, new methods of feature extraction, feature selection, stochastic data characterization/modeling, variance reduction and measures for parametric discrimination are proposed. These methods have implications for data mining, machine learning, and information theory. A novel decision-support system is developed in order to guide intervention during cardiac arrest. The models are built upon knowledge extracted with signal-processing, non-linear dynamic and machine-learning methods. The proposed ECG characterization, combined with information extracted from PetCO2 signals, shows viability for decision-support in clinical settings. The approach, which focuses on integration of multiple features through machine learning techniques, suits well to inclusion of multiple physiologic …

Exploiting Domain Structure In Multiagent Decision-Theoretic Planning And Reasoning, Akshat Kumar

Open Access Dissertations

This thesis focuses on decision-theoretic reasoning and planning problems that arise when a group of collaborative agents are tasked to achieve a goal that requires collective effort. The main contribution of this thesis is the development of effective, scalable and quality-bounded computational approaches for multiagent planning and coordination under uncertainty. This is achieved by a synthesis of techniques from multiple areas of artificial intelligence, machine learning and operations research. Empirically, each algorithmic contribution has been tested rigorously on common benchmark problems and, in many cases, real-world applications from machine learning and operations research literature.

The first part of the thesis …

Automating Large-Scale Simulation Calibration To Real-World Sensor Data, Richard Everett Edwards

Doctoral Dissertations

Many key decisions and design policies are made using sophisticated computer simulations. However, these sophisticated computer simulations have several major problems. The two main issues are 1) gaps between the simulation model and the actual structure, and 2) limitations of the modeling engine's capabilities. This dissertation's goal is to address these simulation deficiencies by presenting a general automated process for tuning simulation inputs such that simulation output matches real world measured data. The automated process involves the following key components -- 1) Identify a model that accurately estimates the real world simulation calibration target from measured sensor data; 2) Identify …

Knowledge Extraction In Video Through The Interaction Analysis Of Activities, Omar Ulises Florez

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

A video is a growing stream of unstructured data that significantly increases the amount of information transmitted and stored on the Internet. For example, every minute YouTube users upload 72 GB of information. Some of the best applications for video analysis include the monitoring of activities in defense and security scenarios such as the autonomous planes that collect video and images at reduced risk and the surveillance cameras in public places like traffic lights, airports, and schools.

Some of the challenges in the analysis of video correspond to implement complex operations such as searching of activities, understanding of scenes, and …

A Hierarchical Multi-Output Nearest Neighbor Model For Multi-Output Dependence Learning, Richard Glenn Morris

Theses and Dissertations

Multi-Output Dependence (MOD) learning is a generalization of standard classification problems that allows for multiple outputs that are dependent on each other. A primary issue that arises in the context of MOD learning is that for any given input pattern there can be multiple correct output patterns. This changes the learning task from function approximation to relation approximation. Previous algorithms do not consider this problem, and thus cannot be readily applied to MOD problems. To perform MOD learning, we introduce the Hierarchical Multi-Output Nearest Neighbor model (HMONN) that employs a basic learning model for each output and a modified nearest …

Spoons: Netflix Outage Detection Using Microtext Classification, Eriq A. Augusitne

Master's Theses

Every week there are over a billion new posts to Twitter services and many of those messages contain feedback to companies about their services. One company that recognizes this unused source of information is Netflix. That is why Netflix initiated the development of a system that lets them respond to the millions of Twitter and Netflix users that are acting as sensors and reporting all types of user visible outages. This system enhances the feedback loop between Netflix and its customers by increasing the amount of customer feedback that Netflix receives and reducing the time it takes for Netflix to …

Learning With An Insufficient Supply Of Data Via Knowledge Transfer And Sharing, Samir Al-Stouhi

Wayne State University Dissertations

As machine learning methods extend to more complex and diverse set of problems, situations arise where the complexity and availability of data presents a situation where the information source is not "adequate" to generate a representative hypothesis. Learning from multiple sources of data is a promising research direction as researchers leverage ever more diverse sources of information. Since data is not readily available, knowledge has to be transferred from other sources and new methods (both supervised and un-supervised) have to be developed to selectively share and transfer knowledge. In this dissertation, we present both supervised and un-supervised techniques to tackle …

Energy Efficient Context-Aware Framework In Mobile Sensing, Ozgur Yurur

USF Tampa Graduate Theses and Dissertations

The ever-increasing technological advances in embedded systems engineering, together with the proliferation of small-size sensor design and deployment, have enabled mobile devices (e.g., smartphones) to recognize daily occurring human based actions, activities and interactions. Therefore, inferring a vast variety of mobile device user based activities from a very diverse context obtained by a series of sensory observations has drawn much interest in the research area of ubiquitous sensing. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users, and this allows network services to respond proactively and intelligently …

Automatic Classification Of Epilepsy Lesions, Junwei Sun

Electronic Thesis and Dissertation Repository

Epilepsy is a common and diverse set of chronic neurological disorders characterized by seizures. Epileptic seizures result from abnormal, excessive or hypersynchronous neuronal activity in the brain. Seizure types are organized firstly according to whether the source of the seizure within the brain is localized or distributed. In this work, our objective is to validate the use of MRI (Magnetic Resonance Imaging) for localizing seizure focus for improved surgical planning. We apply computer vision and machine learning techniques to tackle the problem of epilepsy lesion classification. First datasets of digitized histology images from brain cortexes of different patients are obtained …

A Physiological Signal Processing System For Optimal Engagement And Attention Detection., Ashwin Belle

Theses and Dissertations

In today’s high paced, hi-tech and high stress environment, with extended work hours, long to-do lists and neglected personal health, sleep deprivation has become common in modern culture. Coupled with these factors is the inherent repetitious and tedious nature of certain occupations and daily routines, which all add up to an undesirable fluctuation in individuals’ cognitive attention and capacity. Given certain critical professions, a momentary or prolonged lapse in attention level can be catastrophic and sometimes deadly. This research proposes to develop a real-time monitoring system which uses fundamental physiological signals such as the Electrocardiograph (ECG), to analyze and predict …

Resource-Bounded Information Acquisition And Learning, Pallika H. Kanani

Open Access Dissertations

In many scenarios it is desirable to augment existing data with information acquired from an external source. For example, information from the Web can be used to fill missing values in a database or to correct errors. In many machine learning and data mining scenarios, acquiring additional feature values can lead to improved data quality and accuracy. However, there is often a cost associated with such information acquisition, and we typically need to operate under limited resources. In this thesis, I explore different aspects of Resource-bounded Information Acquisition and Learning.

The process of acquiring information from an external source involves …

Contributions To K-Means Clustering And Regression Via Classification Algorithms, Raied Salman

Theses and Dissertations

The dissertation deals with clustering algorithms and transforming regression prob-lems into classification problems. The main contributions of the dissertation are twofold; first, to improve (speed up) the clustering algorithms and second, to develop a strict learn-ing environment for solving regression problems as classification tasks by using support vector machines (SVMs). An extension to the most popular unsupervised clustering meth-od, k-means algorithm, is proposed, dubbed k-means2 (k-means squared) algorithm, appli-cable to ultra large datasets. The main idea is based on using a small portion of the dataset in the first stage of the clustering. Thus, the centers of such a smaller …

Topic Regression, David Mimno

Open Access Dissertations

Text documents are generally accompanied by non-textual information, such as authors, dates, publication sources, and, increasingly, automatically recognized named entities. Work in text analysis has often involved predicting these non-text values based on text data for tasks such as document classification and author identification. This thesis considers the opposite problem: predicting the textual content of documents based on non-text data. In this work I study several regression-based methods for estimating the influence of specific metadata elements in determining the content of text documents. Such topic regression methods allow users of document collections to test hypotheses about the underlying environments that …

An Ssvep Brain-Computer Interface: A Machine Learning Approach, Fei Teng

Electronic Theses and Dissertations

A Brain-Computer Interface (BCI) provides a bidirectional communication path for a human to control an external device using brain signals. Among neurophysiological features in BCI systems, steady state visually evoked potentials (SSVEP), natural responses to visual stimulation at specific frequencies, has increasingly drawn attentions because of its high temporal resolution and minimal user training, which are two important parameters in evaluating a BCI system. The performance of a BCI can be improved by a properly selected neurophysiological signal, or by the introduction of machine learning techniques. With the help of machine learning methods, a BCI system can adapt to the …

Tagline: Information Extraction For Semi-Structured Text Elements In Medical Progress Notes, Dezon K. Finch

USF Tampa Graduate Theses and Dissertations

Text analysis has become an important research activity in the Department of Veterans Affairs (VA). Statistical text mining and natural language processing have been shown to be very effective for extracting useful information from medical documents. However, neither of these techniques is effective at extracting the information stored in semi-structure text elements. A prototype system (TagLine) was developed as a method for extracting information from the semi-structured portions of text using machine learning. Features for the learning machine were suggested by prior work, as well as by examining the text, and selecting those attributes that help distinguish the various classes …

Data-Intensive Computing For Bioinformatics Using Virtualization Technologies And Hpc Infrastructures, Pengfei Xuan

All Theses

The bioinformatics applications often involve many computational components and massive data sets, which are very difficult to be deployed on a single computing machine. In this thesis, we designed a data-intensive computing platform for bioinformatics applications using virtualization technologies and high performance computing (HPC) infrastructures with the concept of multi-tier architecture, which can seamlessly integrate the web user interface (presentation tier), scientific workflow (logic tier) and computing infrastructure (data/computing tier). We demonstrated our platform on two bioinformatics projects. First, we redesigned and deployed the cotton marker database (CMD) (http://www.cottonmarker.org), a centralized web portal in the cotton research community, using the …

Fast Parallel Machine Learning Algorithms For Large Datasets Using Graphic Processing Unit, Qi Li

Theses and Dissertations

This dissertation deals with developing parallel processing algorithms for Graphic Processing Unit (GPU) in order to solve machine learning problems for large datasets. In particular, it contributes to the development of fast GPU based algorithms for calculating distance (i.e. similarity, affinity, closeness) matrix. It also presents the algorithm and implementation of a fast parallel Support Vector Machine (SVM) using GPU. These application tools are developed using Compute Unified Device Architecture (CUDA), which is a popular software framework for General Purpose Computing using GPU (GPGPU). Distance calculation is the core part of all machine learning algorithms because the closer the query …

Query-Dependent Selection Of Retrieval Alternatives, Niranjan Balasubramanian

Open Access Dissertations

The main goal of this thesis is to investigate query-dependent selection of retrieval alternatives for Information Retrieval (IR) systems. Retrieval alternatives include choices in representing queries (query representations), and choices in methods used for scoring documents. For example, an IR system can represent a user query without any modification, automatically expand it to include more terms, or reduce it by dropping some terms. The main motivation for this work is that no single query representation or retrieval model performs the best for all queries. This suggests that selecting the best representation or retrieval model for each query can yield improved …

Increasing Scalability In Algorithms For Centralized And Decentralized Partially Observable Markov Decision Processes: Efficient Decision-Making And Coordination In Uncertain Environments, Christopher Amato

Open Access Dissertations

As agents are built for ever more complex environments, methods that consider the uncertainty in the system have strong advantages. This uncertainty is common in domains such as robot navigation, medical diagnosis and treatment, inventory management, sensor networks and e-commerce. When a single decision maker is present, the partially observable Markov decision process (POMDP) model is a popular and powerful choice. When choices are made in a decentralized manner by a set of decision makers, the problem can be modeled as a decentralized partially observable Markov decision process (DEC-POMDP). While POMDPs and DEC-POMDPs offer rich frameworks for sequential decision making …

Using Context To Enhance The Understanding Of Face Images, Vidit Jain

Open Access Dissertations

Faces are special objects of interest. Developing automated systems for detecting and recognizing faces is useful in a variety of application domains including providing aid to visually-impaired people and managing large-scale collections of images. Humans have a remarkable ability to detect and identify faces in an image, but related automated systems perform poorly in real-world scenarios, particularly on faces that are difficult to detect and recognize. Why are humans so good? There is general agreement in the cognitive science community that the human brain uses the context of the scene shown in an image to solve the difficult cases of …

Fundamental Work Toward An Image Processing-Empowered Dental Intelligent Educational System, Grace Olsen

Theses and Dissertations

Computer-aided education in dental schools is greatly needed in order to reduce the need for human instructors to provide guidance and feedback as students practice dental procedures. A portable computer-aided educational system with advanced digital image processing capabilities would be less expensive than current computer-aided dental educational systems and would also address some of their limitations. This dissertation outlines the development of novel components that would be part of such a system. This research includes the design of a novel image processing technique, the Directed Active Shape Model algorithm, which is used to locate the tooth and drilled preparation from …

The Development Of Hierarchical Knowledge In Robot Systems, Stephen W. Hart

Open Access Dissertations

This dissertation investigates two complementary ideas in the literature on machine learning and robotics--those of embodiment and intrinsic motivation--to address a unified framework for skill learning and knowledge acquisition. "Embodied" systems make use of structure derived directly from sensory and motor configurations for learning behavior. Intrinsically motivated systems learn by searching for native, hedonic value through interaction with the world. Psychological theories of intrinsic motivation suggest that there exist internal drives favoring open-ended cognitive development and exploration. I argue that intrinsically motivated, embodied systems can learn generalizable skills, acquire control knowledge, and form an epistemological understanding of the world …

Action-Based Representation Discovery In Markov Decision Processes, Sarah Osentoski

Open Access Dissertations

This dissertation investigates the problem of representation discovery in discrete Markov decision processes, namely how agents can simultaneously learn representation and optimal control. Previous work on function approximation techniques for MDPs largely employed hand-engineered basis functions. In this dissertation, we explore approaches to automatically construct these basis functions and demonstrate that automatically constructed basis functions significantly outperform more traditional, hand-engineered approaches. We specifically examine two problems: how to automatically build representations for action-value functions by explicitly incorporating actions into a representation, and how representations can be automatically constructed by exploiting a pre-specified task hierarchy. We first introduce a technique for …

Computational Intelligence Based Classifier Fusion Models For Biomedical Classification Applications, Xiujuan Chen

Computer Science Dissertations

The generalization abilities of machine learning algorithms often depend on the algorithms’ initialization, parameter settings, training sets, or feature selections. For instance, SVM classifier performance largely relies on whether the selected kernel functions are suitable for real application data. To enhance the performance of individual classifiers, this dissertation proposes classifier fusion models using computational intelligence knowledge to combine different classifiers. The first fusion model called T1FFSVM combines multiple SVM classifiers through constructing a fuzzy logic system. T1FFSVM can be improved by tuning the fuzzy membership functions of linguistic variables using genetic algorithms. The improved model is called GFFSVM. To better …

Granular Support Vector Machines Based On Granular Computing, Soft Computing And Statistical Learning, Yuchun Tang

Computer Science Dissertations

With emergence of biomedical informatics, Web intelligence, and E-business, new challenges are coming for knowledge discovery and data mining modeling problems. In this dissertation work, a framework named Granular Support Vector Machines (GSVM) is proposed to systematically and formally combine statistical learning theory, granular computing theory and soft computing theory to address challenging predictive data modeling problems effectively and/or efficiently, with specific focus on binary classification problems. In general, GSVM works in 3 steps. Step 1 is granulation to build a sequence of information granules from the original dataset or from the original feature space. Step 2 is modeling Support …

Multizoom Activity Recognition Using Machine Learning, Raymond Smith

Electronic Theses and Dissertations

In this thesis we present a system for detection of events in video. First a multiview approach to automatically detect and track heads and hands in a scene is described. Then, by making use of epipolar, spatial, trajectory, and appearance constraints, objects are labeled consistently across cameras (zooms). Finally, we demonstrate a new machine learning paradigm, TemporalBoost, that can recognize events in video. One aspect of any machine learning algorithm is in the feature set used. The approach taken here is to build a large set of activity features, though TemporalBoost itself is able to work with any feature set …

Pattern Recognition Via Machine Learning With Genetic Decision-Programming, Carl C. Hoff

Browse all Theses and Dissertations

In the intersection of pattern recognition, machine learning, and evolutionary computation is a new search technique by which computers might program themselves. That technique is called genetic decision-programming. A computer can gain the ability to distinguish among the things that it needs to recognize by using genetic decision-programming for pattern discovery and concept learning. Those patterns and concepts can be easily encoded in the spines of a decision program (tree or diagram). A spine consists of two parts: (1) the test-outcome pairs along a path from the program's root to any of its leaves and (2) the conclusion in that …