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Articles 91 - 111 of 111
Full-Text Articles in Data Science
K-Nearest Neighbors Density-Based Clustering, Avory C. Bryant
K-Nearest Neighbors Density-Based Clustering, Avory C. Bryant
Theses and Dissertations
Traditional density-based clustering approaches rely on a distance-based parameter to define data connectivity and density. However, an appropriate value of this parameter can be difficult to determine as it is highly dependent on the underlying distribution of the data. In particular, distribution parameters affect the scale of inter-group distances (e.g., variance); this dependence leads to a well-known inability to simultaneously detect clusters at varying levels of density. In this work, connectivity and density are defined according to the rank-order induced by the distance metric (i.e., invariant to the expected scale of the distances). Connectivity by k-nearest neighbors and density by …
Continual Learning For Multi-Label Drifting Data Streams Using Homogeneous Ensemble Of Self-Adjusting Nearest Neighbors, Gavin Alberghini
Continual Learning For Multi-Label Drifting Data Streams Using Homogeneous Ensemble Of Self-Adjusting Nearest Neighbors, Gavin Alberghini
Theses and Dissertations
Multi-label data streams are sequences of multi-label instances arriving over time to a multi-label classifier. The properties of the data stream may continuously change due to concept drift. Therefore, algorithms must adapt constantly to the new data distributions. In this paper we propose a novel ensemble method for multi-label drifting streams named Homogeneous Ensemble of Self-Adjusting Nearest Neighbors (HESAkNN). It leverages a self-adjusting kNN as a base classifier with the advantages of ensembles to adapt to concept drift in the multi-label environment. To promote diverse knowledge within the ensemble, each base classifier is given a unique subset of features and …
Lifespan Analysis Of Earth Satellites, Venkata Jaipal Reddy Batthula
Lifespan Analysis Of Earth Satellites, Venkata Jaipal Reddy Batthula
Theses and Dissertations
Different countries have their own satellites for their various needs like communication, weather forecast, and security. The first satellite was launched in 1957 into space. Thousands of satellite lifetimes have already ended but they are still in orbit. The present world has more advanced technology when compared with previous technology. So, the technology for satellites is improving compared with the past. We need to understand trends in improvements to satellites related to lifespans better, using a new dataset that has not been available before, as well as datasets that we have worked with before, and that is the purpose of …
Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé
Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé
Theses and Dissertations
A holistic approach to the algorithm selection problem is presented. The “algorithm selection framework" uses a combination of user input and meta-data to streamline the algorithm selection for any data analysis task. The framework removes the conjecture of the common trial and error strategy and generates a preference ranked list of recommended analysis techniques. The framework is performed on nine analysis problems. Each of the recommended analysis techniques are implemented on the corresponding data sets. Algorithm performance is assessed using the primary metric of recall and the secondary metric of run time. In six of the problems, the recall of …
An Analysis Of Learning Curve Theory & Diminishing Rates Of Learning, Dakotah W. Hogan
An Analysis Of Learning Curve Theory & Diminishing Rates Of Learning, Dakotah W. Hogan
Theses and Dissertations
Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced; however, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, Boones Learning Curve (2018), was recently developed to model this phenomenon. This research confirmed that Boones Learning Curve is more accurate in modeling observed learning curves using production data of 169 Department of Defense end-items. However, further empirical analysis revealed deficiencies …
Using A Data Science Driven Approach To Analyzing Chemistry Hazard Code Data, Devlon Bomer
Using A Data Science Driven Approach To Analyzing Chemistry Hazard Code Data, Devlon Bomer
Theses and Dissertations
There exists a mostly unexplored ‘big data’ dataset comprising of chemical hazard and safety codes. The purpose of this research is to apply a data science methodology to the problem of exploring this data. The data was run through extract-transform-load protocols, and then through data mining algorithms. The results are described and discussed. More work could be done on the subject, but this paper starts the process.
Invariance And Invertibility In Deep Neural Networks, Han Zhang
Invariance And Invertibility In Deep Neural Networks, Han Zhang
Theses and Dissertations
Machine learning is concerned with computer systems that learn from data instead of being explicitly programmed to solve a particular task. One of the main approaches behind recent advances in machine learning involves neural networks with a large number of layers, often referred to as deep learning. In this dissertation, we study how to equip deep neural networks with two useful properties: invariance and invertibility. The first part of our work is focused on constructing neural networks that are invariant to certain transformations in the input, that is, some outputs of the network stay the same even if the input …
Earth Satellite Data Analysis, Venkat Kodali
Earth Satellite Data Analysis, Venkat Kodali
Theses and Dissertations
The number of satellites has been steadily increasing since the first satellite launched to orbit in 1957. Satellites are launched for various purpose and users of the satellites are now spread across the world. The dependency on satellites has increased greatly over time and they now have a major impact on everyday life. The project I will discuss is focused on data analysis of earth satellites. The datasets used for analysis are from several sources, the data was cleaned and used to find some meaningful insights. The objective is develop data visualization charts and modeling techniques using R, identify trends …
Effects Of Data Replication On Data Exfiltration In Mobile Ad Hoc Networks Utilizing Reactive Protocols, Corey T. Willinger
Effects Of Data Replication On Data Exfiltration In Mobile Ad Hoc Networks Utilizing Reactive Protocols, Corey T. Willinger
Theses and Dissertations
A swarm of autonomous UAVs can provide a significant amount of ISR data where current UAV assets may not be feasible or practical. As such, the availability of the data the resides in the swarm is a topic that will benefit from further investigation. This thesis examines the impact of le replication and swarm characteristics such as node mobility, swarm size, and churn rate on data availability utilizing reactive protocols. This document examines the most prominent factors affecting the networking of nodes in a MANET. Factors include network routing protocols and peer-to-peer le protocols. It compares and contrasts several open …
Detecting Student Dropouts Using Fuzzy Inferencing, Shahriar Husainy
Detecting Student Dropouts Using Fuzzy Inferencing, Shahriar Husainy
Theses and Dissertations
Fuzzy logic provides a methodology for reasoning using imprecise rules and assertions. Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can be made, or patterns discerned. This study concerns the development of a Fuzzy Inference System (FIS) for identifying likely student dropouts at Columbus State University (CSU). The fuzzy inference based model uses a hybrid knowledge extraction process to predict how likely each freshman student will be to drop their program of study at the end of their first semester. …
Modeling Cyber Situational Awareness Through Data Fusion, Evan L. Raulerson
Modeling Cyber Situational Awareness Through Data Fusion, Evan L. Raulerson
Theses and Dissertations
Cyber attacks are compromising networks faster than administrators can respond. Network defenders are unable to become oriented with these attacks, determine the potential impacts, and assess the damages in a timely manner. Since the observations of network sensors are normally disjointed, analysis of the data is overwhelming and time is not spent efficiently. Automation in defending cyber networks requires a level of reasoning for adequate response. Current automated systems are mostly limited to scripted responses. Better defense tools are required. This research develops a framework that aggregates data from heterogeneous network sensors. The collected data is correlated into a single …
The Affect Of Varying Arousal Methods Upon Vigilance And Error Detection In An Automated Command And Control Environment, Brent T. Langhals
The Affect Of Varying Arousal Methods Upon Vigilance And Error Detection In An Automated Command And Control Environment, Brent T. Langhals
Theses and Dissertations
This study focused on improving vigilance performance through developing methods to arouse subjects to the possibility of errors in a data manipulation information warfare attack. The study suggests that by continuously applying arousal stimuli, subjects would retain initially high vigilance levels thereby avoiding the vigilance decrement phenomenon and improving error detection. The research focused on which methods were the most effective as well the impact of age upon the arousability of the subjects. Further the implications of vigilance and vigilance decrement for correct detections as well as productivity were explored. The study used a simulation experiment to provide a vigilance …
Extracting Mission Semantics From Unmanned Aerial Vehicle Telemetry And Flight Plans, Walter T. Berridge
Extracting Mission Semantics From Unmanned Aerial Vehicle Telemetry And Flight Plans, Walter T. Berridge
Theses and Dissertations
With the acceptance of Unmanned Aerial Vehicles (UAVs) as a primary platform within the Department of Defense (DOD) for gathering intelligence data, the amount of video information being recorded, analyzed, and archived continues to grow. Mechanisms for quickly locating and retrieving video segments of interest amongst the many hours of recorded video are required to accommodate the rapid turnaround expected in today's wartime planning environments. This research demonstrates that text-based data accompanying UAV video yields sufficient information to identify and create data items that can be indexed to provide for rapid identification and retrieval of video segments of interest. Four …
Evolving Compact Decision Rule Sets, Robert E. Marmelstein
Evolving Compact Decision Rule Sets, Robert E. Marmelstein
Theses and Dissertations
While data mining technology holds the promise of automatically extracting useful patterns (such as decision rules) from data, this potential has yet to be realized. One of the major technical impediments is that the current generation of data mining tools produce decision rule sets that are very accurate, but extremely complex and difficult to interpret. As a result, there is a clear need for methods that yield decision rule sets that are both accurate and compact. The development of the Genetic Rule and Classifier Construction Environment (GRaCCE) is proposed as an alternative to existing decision rule induction (DRI) algorithms. GRaCCE …
An Examination Of Multi-Tier Designs For Legacy Data Access, Michael L. Acker
An Examination Of Multi-Tier Designs For Legacy Data Access, Michael L. Acker
Theses and Dissertations
This work examines the application of Java and the Common Object Request Broker Architecture (CORBA) to support access to remote databases via the Internet. The research applies these software technologies to assist an Air Force distance learning provider in improving the capabilities of its World Wide Web-based correspondence system. An analysis of the distance learning provider's operation revealed a strong dependency on a non-collocated legacy relational database. This dependency limits the distance learning provider's future web-based capabilities. A recommendation to improve operation by data replication is proposed, and the implementation details are provided for two alternative test systems that support …
A Comparison Of Loose And Tight Gps/Ins Integration Using Real Ins And Gps Data, Warren H. Nuibe
A Comparison Of Loose And Tight Gps/Ins Integration Using Real Ins And Gps Data, Warren H. Nuibe
Theses and Dissertations
An extended Kalman filter (EKE) is used to combine the information obtained from a Global Positioning System (GPS) receiver and an Inertial Navigation System (INS) to provide a navigation solution. This research compares the results of a tightly-coupled GPS/INS integrated system with a loosely-coupled integrated system, using real world data. A fair comparison is accomplished by using the same sets of data, and keeping the integration structures as close as possible. Both integrations are feedforward and have the same error states in the navigation Kalman filters. Differences between the two, such as navigation solutions and tuning values, are shown in …
A Neural Network Approach To The Prediction And Confidence Assignation Of Nonlinear Time Series Classifications, Erin S. Heim
A Neural Network Approach To The Prediction And Confidence Assignation Of Nonlinear Time Series Classifications, Erin S. Heim
Theses and Dissertations
This thesis uses multiple layer perceptrons (MLP) neural networks and Kohonen clustering networks to predict and assign confidence to nonlinear time series classifications. The nonlinear time series used for analysis is the Standard and Poor's 100 (S&P 100) index. The target prediction is classification of the daily index change. Financial indicators were evaluated to determine the most useful combination of features for input into the networks. After evaluation it was determined that net changes in the index over time and three short-term indicators result in better accuracy. A back-propagation trained MLP neural network was then trained with these features to …
Acquiring Consistent Knowledge For Bayesian Forests, Darwyn O. Banks
Acquiring Consistent Knowledge For Bayesian Forests, Darwyn O. Banks
Theses and Dissertations
This thesis develops a methodology and a tool for knowledge acquisition with the new probabilistic knowledge representation-the Bayesian Forest. It establishes the structure of the Knowledge Acquisition and Maintenance module of the Probabilities. Expert Systems, Knowledge and Inference (PESKI) architecture. The tool, MACK, is designed to be used directly by the domain expert(s) rather than by knowledge engineer(s), and thus supports automated knowledge acquisition. This research determines and implements the constraints necessary to ensure the consistency of Bayesian Forest knowledge bases as data is both acquired and subsequently maintained. The impact to the PESKI architecture of time-dependent information and default …
The Application Of A Readiness-Based Sparing Model To Foreign Military Sales, Karen M. Klinger
The Application Of A Readiness-Based Sparing Model To Foreign Military Sales, Karen M. Klinger
Theses and Dissertations
Current Foreign Military Sales FMS models provide stock levels that result in a very low system availability or a funding requirement that exceeds the overall budget. The purpose of this research was to determine if an inventory model exists that can be used in FMS reparable sparing to provide a more efficient and economical inventory purchase. The Aircraft Sustainability Model ASM is such a model, providing the most aircraft availability possible from a given inventory investment by computing the optimal number of spare parts to buy for each item. FMS data was obtained from two sources - the International Data …
Data Reduction With Least Squares Differential Correction Using Equinoctial Elements, Michael S. Wasson
Data Reduction With Least Squares Differential Correction Using Equinoctial Elements, Michael S. Wasson
Theses and Dissertations
This study investigates earth satellite orbit estimation on a track of range, azimuth, and elevation data from a single tracking station. The estimation routine is a least squares batch filter based solely on two-body orbital motion. Using equinoctial elements for the reference orbit avoids the numerical difficulties of the classical elements at eccentricities near zero and inclinations near zero or 90 degrees. Orbits for Mir, DMSP, Explorer, Cosmos, and GPS are investigated. The goal of this study is to reduce orbit information from observations (range, azimuth, and elevation) to an element set and a covariance matrix without considering perturbation effects. …
The Application Of Kriging For Controlled Minimization Of Large Data Sets, Christopher Brodkin
The Application Of Kriging For Controlled Minimization Of Large Data Sets, Christopher Brodkin
Theses and Dissertations
Frequently, the quantity of data available is much greater than that which can be manipulated in an efficient and timely manner. This can cause several problems. The first, and probably most critical, problem is the excessive on-line storage needs of these huge data sets. Secondly, in the computer animation field, huge data sets may require excessive computational time for generation of each frame of a computer animation. Thirdly, computer screens have a limited resolution and need too much computational time removing excessive detail from images generated with a higher resolution than can be displayed. Lastly, too much time is required …