Global ETD Search
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Showing 1 to 6 of 6 for “"Machine Learning visualization"”.
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Application of Deep Learning to Brain Connectivity Classification in Large MRI Datasets
The use of machine learning for whole-brain classification of magnetic resonance imaging (MRI) data is of clear interest, both for understanding phenotypic differences in brain structure and function and for diagnostic applications. Developments of deep learning models in the past decade have …
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Visualization for Solving Non-image Problems and Saliency Mapping
… discovery and data science. Integration of visualization, visual analytics, machine learning (ML), and data mining (DM) are the key aspects of data science research for high-dimensional data. This thesis is to explore the efficiency of a new algorithm to convert non-images data into raster …
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Understanding The Effects of Incorporating Scientific Knowledge on Neural Network Outputs and Loss Landscapes
While machine learning (ML) methods have achieved considerable success on several mainstream problems in vision and language modeling, they are still challenged by their lack of interpretable decision-making that is consistent with scientific knowledge, limiting their applicability for scientific …
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Malware Image Classification using Machine Learning with Local Binary Pattern
… Finally,</p> <p>Tensorflow, a library for machine learning, is applied to classify</p> <p>malware images with the LBP feature. Performance comparison</p> <p>results among different classifiers with different image descriptors</p> <p>such as GIST, a spatial envelope, and the LBP demonstrate …
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Visualization of Deep Convolutional Neural Networks
<p>Deep learning has achieved great accuracy in large scale image classification and scene recognition tasks, especially after the Convolutional Neural Network (CNN) model was introduced. Although a CNN often demonstrates very good classification results, it is usually unclear how or why a …
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LeeLee : an attention visualisation analysis while reading academically
… patterns during reading. The study used the "Visualization as Intermediate Representations (VLAIR) technique to interpret these data. Eyetracking data revealed specific patterns of fixations, interest, and cognitive effort, while EEG data provided insights into levels of cognitive engagement …