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Skip to Search Results- 1Adutwum, Lawrence A
- 1Alireza Kheradmand
- 1Armstrong, Michael D S
- 1Barker-Rothschild, Daniel
- 1Boers, Nicholas M.
- 1Cakiroglu, Celal
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Tools and Methodologies for The Rapid Determination and Transfer of Thermodynamic Parameters used in the Prediction of Gas Chromatographic and Two Dimensional Comprehensive Gas Chromatographic Retention Times
DownloadFall 2014
Three parameter thermodynamic predictive models have been shown previously to provide superior accuracy in the prediction of gas chromatographic retention times in comparison to other forms of modelling such as retention indices. However, these models suffer from the need for extensive...
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Fall 2019
The growth of an organization in the market relies on customer’s satisfaction towards its products and services. Due to the dynamic nature of the internet, and increasing blogs, forums, and customer feedback, it usually remains a key issue in any industry to identify and extract data attributes...
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Understanding the Chemistry of Conversion of Model Compounds and Biomass by Hydrous Pyrolysis Based on Spectroscopic Data Using Data Fusion, Data Mining, and Chemometrics
DownloadFall 2019
Encoding the presence of multispecies in a complex system, difficulty in characterizing the physical constituents of the products with analytical instruments along with developing the causality or modeling between these groups are some major challenges in a complex system such as biomass...
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Decomposition and Feature Selection of Comprehensive 2-DimensionalGas Chromatography - Time-of-Flight Mass Spectrometry(GC×GC-TOFMS) Data
DownloadFall 2021
Comprehensive Two-Dimensional Gas Chromatography - Time-of-Flight Mass Spectrometry (GC×GC-TOFMS) is an advanced instrumental technique that separates complex mixtures along two chromatographic dimensions, followed by multivariate detection that collects mass spectral information at a high...
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High-dimensional data mining: subspace clustering, outlier detection and applications to classification
DownloadSpring 2010
Data mining in high dimensionality almost inevitably faces the consequences of increasing sparsity and declining differentiation between points. This is problematic because we usually exploit these differences for approaches such as clustering and outlier detection. In addition, the exponentially...
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Developing and Evaluating Algorithms for Fixing Omission and Commission Errors in Structured Data
DownloadFall 2020
The use of machine learning is rapidly rising to deliver a variety of benefits in various domains. However, developing predictive systems often faces many challenges that can drastically delay model deployment. For instance, obtaining labeled training data is one of the most expensive bottlenecks...
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Application of chemometric and experimental tools for monitoring processes of industrial importance
DownloadFall 2019
The common theme of this work was to investigate the relationships between multi-dimensional experimental data and the physical and chemical properties of certain reacting systems that have industrial relevance. Three particular systems of increasing complexity in terms of composition of the...
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Fall 2021
Deep neural network (DNN) has been developed rapidly in years. While it shows promising results in various tasks of computer vision, DNN typically suffers from accuracy loss due to the domain shift from a source domain to a target domain. To mitigate the accuracy loss without the label from...
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Fall 2015
This study is about a series of operational acts of identification, such as interpretations, categorizations, representations, classifications, through which past materials have acquired their meaning and therefore identity. Furthermore, this meaning-making will be demonstrated always to be...