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.
Results
Showing 1 to 20 of 49 for “"Data pre-processing"”.
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3D Morphable Models: Data Pre-Processing, Statistical Analysis and Fitting
This thesis presents research aimed at using a 3D linear statistical model (known as a 3D morphable model) of an object class (which could be faces, bodies, cars, etc) for robust shape recovery. Our aim is to use this recovered information for the purposes of potentially useful applications like …
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Using Big Data Analytics to Optimize Practical Large Databases
<p>Big data analytics is gaining popularity for enterprises in optimizing their business processes ranging from retailers, supply chains, to online shopping stores. Existing practical raw data are far from usable to achieve the goal. Therefore, a good data pre-processing approach is required and is …
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Bayesian networks for spatio-temporal integrated catchment assessment
… Networks with GIS was used to facilitate data pre-processing and spatial modelling. Dynamic Bayesian Networks were implemented in the software for time-series modelling.
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Understand omnichannel customer value and the human-machine user experience when using mobile application
… by text-mining methods, including textual data pre-processing, LDA topic modeling, sentimental analysis, word co-occurrence network. This is the first attempt to quantify fashion retailing data using text-mining methods to thoroughly investigate the user/consumer experience in omnichannel …
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Credit card fraud detection using machine learning with integration of contextual knowledge
… multi-perspective approach allows automated data pre-processing to model time correlations to complement and eventually replace transaction aggregation strategies to improve detection efficiency. Experiments carried out on a large set of credit card transaction data from the real world (46 …
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Generic Architecture for Predictive Computational Modelling with Application to Financial Data Analysis: Integration of Semantic Approach and Machine Learning
… PhD thesis introduces a Generic Architecture for Predictive Computational Modelling capable of automating analytical conclusions regarding quantitative data structured as a data frame. The model involves heterogeneous data mining based on a semantic approach, graph-based methods (ontology, …
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Towards understanding and simplifying human-in-the-loop machine learning
"Machine learning application developers and data scientists spend inordinate amount of time iterating on machine learning (ML) workflows, by modifying the data pre-processing, model training, and post-processing steps, via trial-and-error to achieve the desired model performance. As a result, …
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A Methodology for Improved, Data-Oriented, Air Quality Forecasting
… Quality (AQ) forecasting system with the aid of data-driven models presupposes the availability of historical data, which has to be appropriately pre-processed before it can be used. This pre-processing is required because environmental datasets often include measurement errors, noise, outliers …
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Bayesian-based Finite Element Model Updating, Damage Detection, and Uncertainty Quantification for Cable-stayed Bridges
… such as analyzing a huge quantity of measured data for system identification, dealing with uncertainty in measurements and analytical models of structures, performing a real-world application of Bayesian Finite Element (FE) model updating, and Bayesian-based damage detection. The proposed …
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Machine Learning for Radio Frequency Interference Flagging
… involves the identification of corrupted data within radio astronomy measurements. This work explores the application of supervised machine learning algorithms for RFI flagging, trained on real measurement data and simulated data with simulated RFI. The goal of this work is to investigate …
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Network Anomaly Detection with Incomplete Audit Data
… assume the availability of complete and clean data for the purpose of intrusion detection. We contend that this assumption is not valid. Factors like noise in the audit data, mobility of the nodes, and the large amount of data generated by the network make it difficult to build a normal traffic …
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Multimodality: Models, Algorithms, and Applications
… solutions that leverage diverse data sources and analytical methods is surging. We investigate combining operations research and artificial intelligence to address urgent sustainability and healthcare concerns by developing adaptable, universally applicable frameworks. This thesis …
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Mapping vegetation with remote sensing and GIS data using object-based analysis and machine learning algorithms
… include the diverse range of high quality data sources and image analysis techniques. Object-based image analysis (OBIA) and machine learning algorithms are recent advances, which this thesis evaluates. OBIA and machine learning algorithms are first tested using a combination of multiple …
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Semi-automatic matching of semi-structured data updates
Data matching, also referred to as data linkage or field matching, is a technique used to combine multiple data sources into one data set. Data matching is used for data integration in a number of sectors and industries; from politics and health care to scientific applications. The motivation for …
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Building and using robust representations in image classification
… is the ability to learn high-level feature representations of complex data. These learned representations obviate manual data pre-processing, and are versatile enough to generalize across tasks. However, they are not yet capable of fully capturing abstract, meaningful features of the data. For …
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High resolution neural frontal face synthesis from face encodings using adversarial loss
In this thesis, we present a novel neural network method to synthesize a person's face imagery with frontal face and neutral expression, given a single unconstrained face photograph. We achieve this by a data-driven approach to train neural networks with a large-scale in-the-wild dataset of face …
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Cellular Kaleidoscope: Unveiling Tissue Microenvironment in Health and Disease
… Changes in this composition and gene expression patterns within a tissue can indicate a disease manifestation. Single-cell RNA sequencing has revolutionised our understanding of gene expression at the single-cell level, elucidating tissue composition and cell type-specific expression …
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An improved directed random walk framework for cancer classification using gene expression data
… used by scientists to measure the gene expression level changes in gene expression data. From the perspective of computing, an algorithm is developed to ease the diagnosis process, but the feasibility is not reliable. Numerous cancer studies have combined different machine learning …
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Novel pharmacophore clustering methods for protein binding site comparison
… and have only been developed for small specific datasets or very targeted applications. None of these methods make use of the powerful representation afforded by 3D complex-based pharmacophores. A pharmacophore model provides a description of a binding site, consisting of a group of chemical …
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Design and modelling of clock and data recovery integrated circuit in 130 nm CMOS technology for 10 Gb/s serial data communications
… phase and frequency-locked loop based clock and data recovery (PFLL-CDR) integrated circuit, as well as the Verilog-A modeling of an asynchronous serial link based chip to chip communication system incorporating the proposed concept. The proposed design was implemented and fabricated using the …
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