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Full-Text Articles in Artificial Intelligence and Robotics
Machine Learning Of Lifestyle Data For Diabetes, Yan Luo
Machine Learning Of Lifestyle Data For Diabetes, Yan Luo
Electronic Thesis and Dissertation Repository
Self-Monitoring of Blood Glucose (SMBG) for Type-2 Diabetes (T2D) remains highly challenging for both patients and doctors due to the complexities of diabetic lifestyle data logging and insufficient short-term and personalized recommendations/advice. The recent mobile diabetes management systems have been proved clinically effective to facilitate self-management. However, most such systems have poor usability and are limited in data analytic functionalities. These two challenges are connected and affected by each other. The ease of data recording brings better data for applicable data analytic algorithms. On the other hand, the irrelevant or inaccurate data input will certainly commit errors and noises. The …
Deep Learning Via Stacked Sparse Autoencoders For Automated Voxel-Wise Brain Parcellation Based On Functional Connectivity, Céline Gravelines
Deep Learning Via Stacked Sparse Autoencoders For Automated Voxel-Wise Brain Parcellation Based On Functional Connectivity, Céline Gravelines
Electronic Thesis and Dissertation Repository
Functional brain parcellation – the delineation of brain regions based on functional connectivity – is an active research area lacking an ideal subject-specific solution independent of anatomical composition, manual feature engineering, or heavily labelled examples. Deep learning is a cutting-edge area of machine learning on the forefront of current artificial intelligence developments. Specifically, autoencoders are artificial neural networks which can be stacked to form hierarchical sparse deep models from which high-level features are compressed, organized, and extracted, without labelled training data, allowing for unsupervised learning. This thesis presents a novel application of stacked sparse autoencoders to the problem of parcellating …