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 70 for “"Machine learning classification"”.
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Machine Learning Classification of Gas Chromatography Data
… constituent components can be determined. Machine Learning (ML) is a field consisting of techniques by which machines can independently analyze data to derive their own procedures for processing it. Additionally, there are techniques for enhancing the performance of ML algorithms. Feature …
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Machine learning classification techniques for non-intrusive load monitoring
… identify a unique signature which is used by a machine learning classifier to automate the load identification process. In this thesis, existing machine learning classification techniques are reviewed within the context of the non-intrusive load monitoring application. A non-intrusive load …
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A Machine Learning Classification Framework for Early Prediction of Alzheimer’s Disease
… and investigate the disease further using machine learning models. In this study, we used machine learning models and conducted two classification experiments for early prediction of Alzheimer’s disease, and one ranking experiment to rank its risk factors by importance. Besides these …
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Enhancing Machine Learning Classification for Electrical Time Series with Additional Domain Applications
Recent advances in machine learning have significant, far-reaching potential in electrical time series applications. However, many methods cannot currently be implemented in real world applications due to multiple challenges. This thesis explores solutions to many of these challenges in an effort …
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Machine Learning Classification of Traumatic Brain Injury Patients Versus Healthy Controls Using Arterial Spin Labeled Perfusion MRI
… of the current study is to examine the use of machine learning, specifically a Support Vector Machine (SVM) classifier, in discriminating between healthy controls (n=35) and TBI patients (n=42) using ASL-generated CBF data 3 months post-injury. Identification of the regions of interest (ROIs) …
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Optimum parameter machine learning classification and prediction of Internet of Things (IoT) malwares using static malware analysis techniques
Application of machine learning in the field of malware analysis is not a new concept, there have been lots of researches done on the classification of malware in android and windows environments. However, when it comes to malware analysis in the internet of things (IoT), it still requires work to …
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Percussion Based Detection Method for Localization of Pipe Inspection Gauge using Advanced Machine Learning Classification and Clustering Techniques.
… focused on using percussion based detection and machine learning methods to localize missing PIGs. From a simple strike on a pipe system, this thesis compared advanced supervised and unsupervised machine learning techniques (Support Vector Machine, Convolutional Neural Network + Long-Short Term …
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An analysis of the performance and interpretability of machine learning classification algorithms to predict long-term share returns on the JSE
… analysis and formulating investment strategy. Machine learning is a promising approach for improving the accuracy of these predictions. However, the outputs of machine learning models are not transparent or interpretable, which limits their usability for real-world decision making. There is a …
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Classification trees outperform logistic regression predictions of attrition in the U.S. Marine Corps
The present study compared the performance of machine learning classification models against logistic regression in the context of predicting training attrition from the Delayed Enlistment Program in the United States Marine Corps (UMSC) with scores from the Tailored Adaptive Personality Assessment …
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Evaluating the potential of aerial remote sensing in flue-cured tobacco
… Secondly, develop hyperspectral indices and/or machine learning classification models capable of detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. In 2017, UAV-acquired ENDVI surveys demonstrated the ability to consistently separate between flue-cured tobacco …
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An agent-based simulation study of the social contagion of divorce
… their simulation outcomes analyzed. Then, using machine learning classification models to predict the probability that each couple has of facing relationship instability, multiple modifications are introduced in the SIRa model. Having more realistic and heterogeneous agents in the agent-based …
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A Machine Learning Approach to Predicting the Employability of a Graduate
… The aim for the dissertation is to build a machine learning classification model that can predict a students likelihood to become employed, based on their student data (for example, their GPA, degree/s held etc). The resulting model should be a feature that these institutions should use in …
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Cybersecurity Risk Assessment Using Graph Theoretical Anomaly Detection and Machine Learning
… <p>The purpose of this study is to use machine learning classification algorithms augmented by a new feature set extracted with graph theoretical information representing human to human and human to machine interactions in the quantification of cyber risk due to insider threats. Included …
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Machine Learning Algorithms for Improved Glaucoma Diagnosis
… of advanced statistical techniques based on machine learning for automated classification of tests from visual field examinations and retinal nerve fibre measurements to detect glaucoma. Diagnostic performance of the applied machine learning classification algorithms was shown to depend …
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Sensor-based Online Process Monitoring in Advanced Manufacturing
… to a desktop model fused deposition modeling machine, to collect data during the manufacturing process. A design of experiments plan is conducted to provide insight into the process, particularly the occurrence of process failure. Subsequently, machine learning classification techniques are …
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A CREDIT ANALYSIS OF THE UNBANKED AND UNDERBANKED: AN ARGUMENT FOR ALTERNATIVE DATA
… test the goodness of fit metric for some machine learning classification models to ascertain whether the alternative data truly helps in the credit building process.</p> <p>In Chapter 3, I discuss the economic significance of incorporating alternative data in the credit modeling process. …
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Integrated Machine Learning and Bioinformatics Approaches for Prediction of Cancer-Driving Gene Mutations
… leads to abnormal tumor proliferation. Proper classification of cancer-linked driver mutations will considerably help our understanding of the molecular dynamics of cancer. In this study, we compared several cancer-specific predictive models for prediction of driver mutations in cancer-linked …
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Making computer vision Methods accessible for cell classification
… higher level understanding. Recent advances in machine learning such as deep learning based architectures have greatly expanded their potential. However, biologists often lack the training or means to use new software or algorithms, leading to slower or less complete results. This thesis focuses …
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A machine-learning approach to aerosol classification for single-particle mass spectrometry
… on a single particle basis. In this study, machine learning classification algorithms are created using a dataset of SPMS spectra to automatically differentiate particles on the basis of chemistry and size. While clustering methods have been used to group aerosols into broad categories based …
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