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 145 for “"Classification model"”.
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A covariate-adjusted classification model for multiple biomarkers in disease screening and diagnosis
The classification methods based on a linear combination of multiple biomarkers have been widely used to improve the accuracy in disease screening and diagnosis. However, it is seldom to include covariates such as gender and age at diagnosis into these classification procedures. It is known that …
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A Sentiment Analysis of "Filipinx" on Twitter Using a Multinomial Naïve Bayes Classification Model
… “Filipinx”, and to train a Naïve Bayes model to classify tweets into three sentiments: positive, neutral, and negative. My methodology takes inspiration from that of four related studies that similarly conducted sentiment analysis on English/Filipino tweets involving various topics, and …
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Detection of Attribute Hierarchies and Classification Accuracy: the Value of the Hierarchical Diagnostic Classification Model in Formative Assessment Practices
… excelling or struggling in, cognitive diagnostic models are emerging as potentially effective and efficient tools. Despite the value of diagnostic models, there are concerns regarding the application of these models when as learning hierarchy is present or theorized; applying nonhierarchical …
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Classification of large pollen datasets using neural networks with application to mapping and modelling pollen data
… databases and their application to mapping and modelling past vegetation. Maps of past taxon distributions are generated and classification techniques are used to compile maps of past woodland types. These visualisations of pollen data have applications in forest ecology and in modelling the …
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Investigating audio classification to automate the trimming of recorded lectures
… study, we investigate the potential of audio classification to automate this step. A classification model was trained to detect 2 classes: speech and non-speech. Speech represents a single dominant voice, for example, the lecturer, and non-speech represents student chatter, silence and other …
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Contributions to evaluation of machine learning models. Applicability domain of classification models
… problems. The performance of machine learning models depends on algorithms and the data. Moreover, learning algorithms create a model of reality through learning and testing with data processes, and their performance shows an agreement degree of their assumed model with reality. ML algorithms …
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Contributions to evaluation of machine learning models. Applicability domain of classification models
… problems. The performance of machine learning models depends on algorithms and the data. Moreover, learning algorithms create a model of reality through learning and testing with data processes, and their performance shows an agreement degree of their assumed model with reality. ML algorithms …
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A study of FT-IR spectroscopy for the identification and classifcation of haematological malignancies
… component fed linear discriminant spectral models have been tested with leave one out cross validation procedures. A preliminary unfiltered classification model using 50 frozen and air-dried samples correctly classified 54% of 18556 spectra. The performance improved with the three cell line …
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In silico prediction of non-coding RNAs using supervised learning and feature ranking methods
… for development of a non-coding RNA (ncRNA) classification model based on features derived from folding the consensus sequence of multiple sequence alignments using different folding programs: RNAalifold, CentroidFold, and RSpredict. The method ranks these folding features according to a …
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Developing a data-driven model for dynamic reservoir operation using a combined hidden Markov-decision tree and classification tree algorithms
… extends the hidden-Markov-decision tree (HM-DT) model developed by Zhao and Cai (2020) and proposes a data-driven reservoir operation model (DROM). The HM-DT model is first applied to individual reservoirs to derive sets of representative operation modules. Then a module classification model …
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Determination of a robust metabolic barcoding model for chemotaxonomy in Aizoaceae species : expanding morphological and genetic understanding
… store of species-specific information to use in model optimisation across 5 Aizoaceae species (Galenia africana, Aridaria noctiora, Carpobrotus edulis, Ruschia robusta, and Tetragonia fruticosa) using two Crassulaceae species as CAM controls (Cotyledon orbiculata and Tylecodon wallichii ). …
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A context-aware approach for handling concept drift in classification
Adapting classification models to changes is one of the main challenges associated with learning from data in dynamic environments. In particular, the description of the target concept is not static and may change over time under the influence of varying environmental conditions (i.e. varying …
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A discriminant model for classifying contractor performance on public works projects
… (PSC). The main aim was to develop a contractor classification framework to assist construction clients' decisionmaking during tender evaluation. Investigating client selection preferences and behaviours are the main focus of this research. However, attention was also given to the contractors' …
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Determination of geotechnical properties of seafloor sediment using a free fall penetrometer
… the nature of the seafloor. A simple sediment classification model was proposed using data from field deployment tests as well as literature. This model, though applicable only to the probes used in this study, presents an approach that can expand the usage of free fall …
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An evaluation of financial performance of companies. The financial performance of companies is investigated using multiple discriminant analysis together with methods for the identification of potential high performance companies.
… whether on the basis of these characteristics a classification model can be constructed that includes, alongside resource utilisation measures, predictors related to other financial dimensions calculated from published information. The- research proceeds by examining the factors influencing …
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Identifying and Understanding Examinee Behaviors in Item Response Data that Compromise Psychometric Quality
… quality; we also validated a diagnostic classification model (DCM) aiming to diagnose the presence of misconceptions.
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Pediatric traumatic brain injury and educational identification: estimating injury severity using data from a TBI screening tool
… The purpose of this study was to develop a model for interpreting this portion of the BCS and for roughly estimating the severity of any of the student's past head-related injuries. An Injury-Severity Classification Model (ISCM) was developed and inter-rater reliability tested for its use. …
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A framework for understanding user interaction with content-based image retrieval: model, interface and users
… covers three key elements: an interaction model, an interactive interface and users. The three key elements combine to enable effective interaction to happen. Many studies have investigated different aspects of user interaction. However, there is lack of research in combining all three …
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Classifying Challenging Behaviors in Autism Spectrum Disorder with Neural Document Embeddings
… individuals over time and thus, the appropriate classification of a challenging behavior when considering purely qualitative factors can be unclear. In this thesis we seek to add quantitative depth to this otherwise qualitative task of challenging behavior classification. We do so through the …
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