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- Machine learning (2)
- Neuroimaging (2)
- Bayesian model averaging (1)
- Biomarker (1)
- Deep learning (1)
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- Dengue (1)
- Food choice (1)
- Food safety (1)
- Functional connectivity (1)
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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
What Affects Parents’ Choice Of Milk? An Application Of Bayesian Model Averaging, Yingzhe Cheng
What Affects Parents’ Choice Of Milk? An Application Of Bayesian Model Averaging, Yingzhe Cheng
Mathematics & Statistics ETDs
This study identifies the factors that influence parents’ choice of milk for their children, using data from a unique survey administered in 2013 in Hunan province, China. In this survey, we identified two brands of milk, which differ in their prices and safety claims by the producer. Data were collected on parents’ choice of milk between the two brands, demographics, attitude towards food safety and behaviors related to food. Stepwise model selection and Bayesian model averaging (BMA) are used to search for influential factors. The two approaches consistently select the same factors suggested by an economic theoretical model, including price …
Mechanistic Plug-And-Play Models For Understanding The Impact Of Control And Climate On Seasonal Dengue Dynamics In Iquitos, Peru, Nathan Levick
Mechanistic Plug-And-Play Models For Understanding The Impact Of Control And Climate On Seasonal Dengue Dynamics In Iquitos, Peru, Nathan Levick
Mathematics & Statistics ETDs
Dengue virus is a mosquito-borne multi-serotype disease whose dynamics are not precisely understood despite half of the world’s human population being at risk of infection. A recent dataset of dengue case reports from an isolated Amazonian city— Iquitos, Peru—provides a unique opportunity to assess dengue dynamics in a simpli- fied setting. Ten years of clinical surveillance data reveal a specific pattern: two novel serotypes, in turn, invaded and exclusively dominated incidence over several seasonal cycles, despite limited intra-annual variation in climate conditions. Together with mechanistic mathematical models, these data can provide an improved understand- ing of the nonlinear interactions between …
Searching Neuroimaging Biomarkers In Mental Disorders With Graph And Multimodal Fusion Analysis Of Functional Connectivity, Hao He
Electrical and Computer Engineering ETDs
Mental disorders such as schizophrenia (SZ), bipolar (BD), and major depression disorders (MDD) can cause severe symptoms and life disruption. They share some symptoms, which can pose a major clinical challenge to their differentiation. Objective biomarkers based on neuroimaging may help to improve diagnostic accuracy and facilitate optimal treatment for patients. Over the last decades, non-invasive in-vivo neuroimaging techniques such as magnetic resonance imaging (MRI) have been increasingly applied to measure structure and function in human brains. With functional MRI (fMRI) or structural MRI (sMRI), studies have identified neurophysiological deficits in patients’ brain from different perspective. Functional connectivity (FC) analysis …
Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm
Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm
Computer Science ETDs
Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in …
Data Driven Sample Generator Model With Application To Classification, Alvaro Emilio Ulloa Cerna
Data Driven Sample Generator Model With Application To Classification, Alvaro Emilio Ulloa Cerna
Mathematics & Statistics ETDs
Despite the rapidly growing interest, progress in the study of relations between physiological abnormalities and mental disorders is hampered by complexity of the human brain and high costs of data collection. The complexity can be captured by machine learning approaches, but they still may require significant amounts of data. In this thesis, we seek to mitigate the latter challenge by developing a data driven sample generator model for the generation of synthetic realistic training data. Our method greatly improves generalization in classification of schizophrenia patients and healthy controls from their structural magnetic resonance images. A feed forward neural network trained …