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Full-Text Articles in Physical Sciences and Mathematics
The Application Of Synthetic Signals For Ecg Beat Classification, Elliot Morgan Brown
The Application Of Synthetic Signals For Ecg Beat Classification, Elliot Morgan Brown
Theses and Dissertations
A brief overview of electrocardiogram (ECG) properties and the characteristics of various cardiac conditions is given. Two different models are used to generate synthetic ECG signals. Domain knowledge is used to create synthetic examples of 16 different heart beat types with these models. Other techniques for synthesizing ECG signals are explored. Various machine learning models with different combinations of real and synthetic data are used to classify individual heart beats. The performance of the different methods and models are compared, and synthetic data is shown to be useful in beat classification.
From Optimization To Equilibration: Understanding An Emerging Paradigm In Artificial Intelligence And Machine Learning, Ian Gemp
Doctoral Dissertations
Many existing machine learning (ML) algorithms cannot be viewed as gradient descent on some single objective. The solution trajectories taken by these algorithms naturally exhibit rotation, sometimes forming cycles, a behavior that is not expected with (full-batch) gradient descent. However, these algorithms can be viewed more generally as solving for the equilibrium of a game with possibly multiple competing objectives. Moreover, some recent ML models, specifically generative adversarial networks (GANs) and its variants, are now explicitly formulated as equilibrium problems. Equilibrium problems present challenges beyond those encountered in optimization such as limit-cycles and chaotic attractors and are able to abstract …
Forecasting Crashes, Credit Card Default, And Imputation Analysis On Missing Values By The Use Of Neural Networks, Jazmin Quezada
Forecasting Crashes, Credit Card Default, And Imputation Analysis On Missing Values By The Use Of Neural Networks, Jazmin Quezada
Open Access Theses & Dissertations
A neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks,- also called Artificial Neural Networks - are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence, or AI. Recent studies shows that Artificial Neural Network has the highest coefficient of determination (i.e. measure to assess how well a model explains and predicts future outcomes.) in comparison to the K-nearest neighbor classifiers, logistic regression, discriminant analysis, naive Bayesian classifier, and classification trees. In this work, the theoretical description of the neural network methodology …
Credit Risk Analysis In Peer To Peer Lending Data Set: Lending Club, Mohammad Mubasil Bokhari
Credit Risk Analysis In Peer To Peer Lending Data Set: Lending Club, Mohammad Mubasil Bokhari
Senior Projects Spring 2019
This project studies the classification variable ‘default’ in Peer to Peer lending dataset known as Lending Club. The project improved on existing work in terms of accuracy, F-1 measure, precision, recall, and root mean squared error. We explored balancing techniques such as oversampling the minority class, undersampling the majority class, and random forests with balanced bootstraps. We also analyzed and proposed new features that improve the Learner performance.