Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 54 for “"Data Preparation"”.
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Bottom-Up Standardization For Data Preparation
Data preparation is an essential step in every data-related effort, from scientific projects in academia to data-driven decision-making in industry. Typically, data preparation is not the novel or interesting piece of a project — it transforms raw data into a format that enables further innovative …
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Data preparation and visualization for the SWAN refraction model
… that otherwise involves primarily numerical data in a static, non-interactive format. Tools will be developed that enable users to prepare numerical data required for the SWAN refraction model and to visualize the results in an interactivie three-dimensional graphical context. SWAN (acronym …
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Cost-effective data structural preparation
People structure and represent their data in many different ways. One factor to consider in choosing between different representations is how the structure will affect the effectiveness of algorithms that run over the data. In fact, before sophisticated analytics can be performed, one must usually …
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Discovery of multi-omics biomarkers for toxicity using meta-analysis
… 1. Cell lines 11 2. Reagents 11 B. METHODS 11 1. Data preparation & class assignment 11 1-1. Data collection and in-depth curation 11 1-2. Preprocessing 14 1-3. Database construction 14 1-4. Determinant of three toxicity levels for each organ using a threshold 16 1-5. Investment of the …
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Sequential Approaches to Three-Way Data Analytics
Data analytics is a process to discover useful information in data, draw valuable conclusions, and help users make wise decisions. In most occasions, the conclusions can be formally represented as decision rules in which the left-hand-sides describe conditions and the right-hand-sides give …
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Assisting data exploration via in-situ adaptive visualizations
Visual analytics has been widely used by data scientists to shed light on complex problems. Despite the prevalence of many visual analytics tools that empower human decision making with data-driven insights, challenges still exist that hinder users from genuinely capitalizing on insights from …
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Application of Natural Language Processing to Unstructured Data: A Case Study of Climate Change
… areas of interest. The resurgence of big data and machine learning has brought a high hope that designers can learn from past successes and failures. However, when the available data is in a mixture of textual, numerical or graphical form, then the currently popular deep learning tools …
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Bus passenger Origin-Destination Matrix estimation using Automated Data Collection systems
Automatic Data Collection (ADC systems can enhance the ability of transit agencies to obtain useful planning information that was previously too expensive to obtain. This thesis documents the development of an algorithm to estimate a Bus Passenger Trip Origin-Destination Matrix (OD Matrix) based on …
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ModelPred: A Framework for Predicting Trained Model from Training Data
… to understand the impact of changes in training data on a trained model. This is critical for building trust in various stages of a machine learning pipeline: from cleaning poor-quality samples and tracking important ones to be collected during data preparation, to calibrating uncertainty of …
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Democratizing data science through interactive curation of ML pipelines
… are key to extract actionable insights out of data, yet such skills rarely coexist together. In Machine Learning, high-quality results are only attainable via mindful data preprocessing, hyperparameter tuning and model selection. Domain experts are often overwhelmed by such complexity, de-facto …
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Integrating Machine Learning Techniques with Measurement-While-Drilling Data for Subsurface Characterization in Open-Pit Mines
… (MWD) systems generate continuous drilling data that reflect subsurface conditions in real time. With the increasing availability of this data, there is a growing opportunity to use data-driven methods to support geological interpretation and geotechnical risk assessment in mining. However, …
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Digital Humanities: a bridge between computer vision and study of art
… learning in painting research in four parts: “Data Preparation,” “Model Training,” “Evaluation and Optimization,” and “Analysis and Interpretation,” each part including an introduction to basic knowledge, the application of technology (experiments), and reflections on deep learning. Chapter …
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Predictive Risk Modelling of Hospital Emergency Readmission, and Temporal Comorbidity Index Modelling Using Machine Learning Methods
… risk problems, using healthcare administrative data. The aim is to introduce generic and robust solution approaches that can be applied to different healthcare settings. Existing solution methods and techniques of predictive risk modelling of hospital emergency readmission and comorbidity risk …
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Scalable Data Transformations for Low-Latency Large-Scale Data Analysis
… moving elements strongly dependent on the input data size from the interactive phase of the workflow to the data preparation phase. This reduces the overall computational complexity of the interactive phase, enabling reduced interaction latency. Two related groups of approaches are explored: …
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A Novel Report Generation Approach For Medical Applications: The Sisds Methodology And Its Applications
In medicine, reliable data are available only in a few areas and necessary information on prognostic implications is generally missing. In spite of the fact that a great amount of money has been invested to ease the process, an effective solution has yet to be found. Unfortunately, existing data …
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Algorithmic Approaches for Context-Informed Reaction Prediction
… tools, including knowledge representation, data preparation, and model development. The first chapter discusses the representation of molecules in detail, laying the foundation for all the following chapters. The discussion of data representation continues with a showcase of how the Unified …
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An Integrated Framework for Automated Data Collection and Processing for Discrete Event Simulation Models
… in different disciplines. DES models require data in order to determine the different parameters that drive the simulations. The literature about DES input data management indicates that the preparation of necessary input data is often a highly manual process, which causes inefficiencies, …
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Semiometrics: producing a compositional view of influence
… of research, combining citation and metadata analysis to allow richer calculations to be performed over large-scale document networks. As a result, more qualitative influence ratings can be determined and a broader outlook on scientific disciplines can be produced. These ratings are best …
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Prediction of Large for Gestational Age Infants in Ethnically Diverse Datasets Using Machine Learning Techniques. Development of 3rd Trimester Machine Learning Prediction Models and Identification of Important Features Using Dimensionality Reduction Techniques
… LGA prediction models for ethnically diverse datasets and provide a benchmark for future LGA prediction work. Methods: Two retrospective datasets were used: Born In Bradford (BiB) and NHS, each including a large percentage of women of South Asian ethnicity. After appropriate data preparation, …
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A general approach to develop and assess models estimating coal energy content
… literature models be applicable to a new coal dataset and which one would be best? A preliminary evaluation testing the application of existing literature models on new coal data showed significant discrepancies in the results. This evaluation demonstrated that the literature models perform …
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