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 97 for “"Unsupervised Machine Learning"”.
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Unsupervised machine learning applied to radio data
… advanced data analysis algorithms in the form of unsupervised learning techniques, and the unprecedented volumes and complexities of data from the next generation of surveys. For several years, computers have been governed by Moore’s law, which posited that computing power would double every two …
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Application of Unsupervised Machine Learning for Event Classification
We study quark and gluon jets separately using public collider data from the CMS experiment. Our analysis is based on an Open Data dataset of proton-proton collisions collected at the Large Hadron Collider in 2011. We define two non-overlapping data mixtures via a pseudorapidity cut—central jets …
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Reconstructing Textual File Fragments Using Unsupervised Machine Learning Techniques
… analysis motivated by a desire to demonstrate machine learning's applicability to Digital Forensics. Using a categorized corpus of Usenet, Bulletin Board Systems, and other assorted documents a series of experiments are conducted using machine learning techniques to train classifiers which are …
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Animal Internal Motion Analysis with Unsupervised Machine Learning Methods
… life processes. This report presents innovative unsupervised machine learning methods to explore the dynamics of cellular and tissue motion and their implications. We first address cellular motion, focusing on long-term movements. While our laboratory's previously established Minimum-Cost …
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Clustering Single-Cell Electropherograms by Genotype Through Unsupervised Machine Learning
… goal is to query whether it is possible to use unsupervised machine learning to accurately and efficiently gather single cell signals into groups by genotype. If possible, it would greatly reduce the computational complexity of the evaluation of evidence and improve its accuracy. The results in …
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Identifying Functional Profiles of Challenging Behaviors in Autism Spectrum Disorder with Unsupervised Machine Learning
<p>Machine learning and deep learning methods are becoming increasingly used in the understanding, identification, and improvement of the diagnosis and treatment of Autism Spectrum Disorder. People with ASD often exemplify challenging behaviors that can put their safety, education, and general …
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Perturbation Modeling for Molecular Design of Protein Tyrosine Kinase Inhibitors using Unsupervised Machine Learning
… molecule discovery. In specific, generative deep learning models have excelled as tools to aid in navigating the large space of known molecules and in the creation of new molecules. These models are fed various representations of molecules as inputs and learn to perform a variety of things, such …
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Novel topography-based classification for mountain basins: utility and limitations of unsupervised machine learning, A
Unsupervised machine learning algorithms are commonly used data analytic methods with applications spanning many disciplines. In the field of hydrology, K-means and similar clustering methods have been shown to be useful discerning differences in hydrologic signatures between catchments. …
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Leveraging unsupervised machine learning to map neural mechanisms of psychopathology: a review and user guide
… of research has emerged which seeks to utilize unsupervised machine learning to map the relationships between neural activity and psychopathology agnostic of existing diagnostic categories. The ability of machine learning algorithms to extract patterns from large amounts of data may hold promise …
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Exploring the Employment Landscape for Individuals with Autism Spectrum Disorders using Supervised and Unsupervised Machine Learning
<p>Autism Spectrum Disorders (ASD) are a class of neurodevelopmental disorders which usually present with difficulties in social interactions, verbal and nonverbal forms of communication, repetitive behaviors, and restricted interests. Employment rates of young adults with ASD is a national …
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Ambient seismic noise tomography of the southern United States and seismic inversion with dictionary learning using unsupervised machine learning.
… for thin-layered structures, we incorporate unsupervised machine learning. CNN and U-Net decompose seismic traces into dictionary and coefficients, reconstruct reflectivity, and convolve it with a wavelet. Lasso regularization aids training. We also use Variational Autoencoders (VAEs) for …
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Disaggregation and classification of residential water events from high-resolution smart water meter data using unsupervised machine learning methods
… times of water events. k-means clustering, an unsupervised machine learning method, then categorized these water events based on information collected from the appliance end-uses. The use of unsupervised learning substantially reduces the training data requirements and lowers the barrier of …
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Unsupervised Machine Learning Application for the Identification of Kimberlite Ore Facie using Convolutional Neural Networks and Deep Embedded Clustering
… phase of the modelling pipeline - utilizing an unsupervised clustering method known as Convolutional Deep Embedded Clustering with Data Augmentation (ConvDEC-DA). The clustering phase of this research provides a method to group feed material rocks into their respective types or facie using …
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Unsupervised machine learning and k-Means clustering as a way of discovering anomalous events In continuous seismic time series
Unsupervised k-Means clustering was implemented as a method for identifying anomalies in seismic time series. Sliding window approach was used for generating specific subsequences from the overall waveform. Dynamic Time Warping (DTW) was used as the method for comparing seismic subsequences. DTW …
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Multi-Modal Data Fusion, Image Segmentation, and Object Identification using Unsupervised Machine Learning: Conception, Validation, Applications, and a Basis for Multi-Modal Object Detection and Tracking
<p>Remote sensing and instrumentation is constantly improving and increasing in capability. Included within this, is the increase in amount of different instrument types, with various combinations of spatial and spectral resolutions, pointing angles, and various other instrument-specific qualities. …
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AI Assisted Cellular and 3D Spheroid Analyses for Combined Therapies Against Glioblastoma
… effects. This thesis investigated whether unsupervised machine learning can extract therapeutically relevant signatures from glioblastoma cells treated with combination therapies without prior training or assumptions. Three combination strategies were tested: radiation with vorinostat …
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Aluminium Alloy Design Using Machine Learning
… it challenging to achieve desired properties. Machine learning has emerged as a crucial tool in resolving this dilemma, enabling the precise balancing of these properties to design new alloys. The thesis combines unsupervised machine learning, multi-target regression, and genetic algorithms to …
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Aluminium Alloy Design Using Machine Learning
… it challenging to achieve desired properties. Machine learning has emerged as a crucial tool in resolving this dilemma, enabling the precise balancing of these properties to design new alloys. The thesis combines unsupervised machine learning, multi-target regression, and genetic algorithms to …
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Brief Study of Classification Algorithms in Machine Learning
… and implementation of three most commonly used Machine Learning algorithms: k-Nearest Neighbors (kNN), Decision Trees and Naïve Bayes. All these algorithms fall under the Classification algorithm category of Unsupervised Machine Learning. This paper is constructed structurally in explaining the …
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