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Full-Text Articles in Physical Sciences and Mathematics
Learning Mortality Risk For Covid-19 Using Machine Learning And Statistical Methods, Shaoshi Zhang
Learning Mortality Risk For Covid-19 Using Machine Learning And Statistical Methods, Shaoshi Zhang
Electronic Thesis and Dissertation Repository
This research investigates the mortality risk of COVID-19 patients across different variant waves, using the data from Centers for Disease Control and Prevention (CDC) websites. By analyzing the available data, including patient medical records, vaccination rates, and hospital capacities, we aim to discern patterns and factors associated with COVID-19-related deaths.
To explore features linked to COVID-19 mortality, we employ different techniques such as Filter, Wrapper, and Embedded methods for feature selection. Furthermore, we apply various machine learning methods, including support vector machines, decision trees, random forests, logistic regression, K-nearest neighbours, na¨ıve Bayes methods, and artificial neural networks, to uncover underlying …
Data-Driven Exploration Of Coarse-Grained Equations: Harnessing Machine Learning, Elham Kianiharchegani
Data-Driven Exploration Of Coarse-Grained Equations: Harnessing Machine Learning, Elham Kianiharchegani
Electronic Thesis and Dissertation Repository
In scientific research, understanding and modeling physical systems often involves working with complex equations called Partial Differential Equations (PDEs). These equations are essential for describing the relationships between variables and their derivatives, allowing us to analyze a wide range of phenomena, from fluid dynamics to quantum mechanics. Traditionally, the discovery of PDEs relied on mathematical derivations and expert knowledge. However, the advent of data-driven approaches and machine learning (ML) techniques has transformed this process. By harnessing ML techniques and data analysis methods, data-driven approaches have revolutionized the task of uncovering complex equations that describe physical systems. The primary goal in …
Data-Driven Predictive Maintenance: Hvac Health Prognostics Using Power Consumption And Weather Data, Ruiqi Tian
Data-Driven Predictive Maintenance: Hvac Health Prognostics Using Power Consumption And Weather Data, Ruiqi Tian
Electronic Thesis and Dissertation Repository
Data-driven predictive maintenance for heat, ventilation, and air conditioning (HVAC) systems has gained much popularity over recent years due to the increasing availability of integrated internet of things (IoT) sensors capable of reporting HVAC internal operational data. Most existing predictive maintenance methods are designed to analyse these internal operational data for maintenance decision making. However, these methods are not applicable to HVAC systems that are not equipped with internal IoT sensors. Consequently, we propose an AutoEncoder and Artificial Neural Network based HVAC Health Prognostics framework (AE-ANN-HP) that classifies the health condition of HVAC systems using only daily power consumption and …
A Computational Framework For Identifying Relevant Cell Types And Specific Regulatory Mechanisms In Schizophrenia Using Data Integration Methods, Kayvan Shabani
A Computational Framework For Identifying Relevant Cell Types And Specific Regulatory Mechanisms In Schizophrenia Using Data Integration Methods, Kayvan Shabani
Electronic Thesis and Dissertation Repository
Combining multiple data types can help researchers gain deeper insight into the subject of the study compared to analyzing only one dataset in many cases. Biological researchers can also benefit from these methods of integration. For instance, GWAS data that gives information about variations in the DNA cannot provide us with much information about the specific biological components that are significant in the trait of interest. However, when combined with sequencing data such as chromatin accessibility data or gene expression data, they can help us find the significant biological elements in the trait of interest. In this study, I perform …