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Dictionary-Based Data Generation For Fine-Tuning Bert For Adverbial Paraphrasing Tasks, Mark Anthony Carthon
Dictionary-Based Data Generation For Fine-Tuning Bert For Adverbial Paraphrasing Tasks, Mark Anthony Carthon
Theses and Dissertations
Recent advances in natural language processing technology have led to the emergence of
large and deep pre-trained neural networks. The use and focus of these networks are on transfer
learning. More specifically, retraining or fine-tuning such pre-trained networks to achieve state
of the art performance in a variety of challenging natural language processing/understanding
(NLP/NLU) tasks. In this thesis, we focus on identifying paraphrases at the sentence level using
the network Bidirectional Encoder Representations from Transformers (BERT). It is well
understood that in deep learning the volume and quality of training data is a determining factor
of performance. The objective of …
An Improved Method For Spectroscopic Quality Classification, Elizabeth G. Mayer
An Improved Method For Spectroscopic Quality Classification, Elizabeth G. Mayer
Mathematics & Statistics ETDs
Spectral quality classification is a vital step in data cleaning before the
analysis of magnetic resonance spectroscopy (MRS) data can be done. This
analysis compares five methods of quality classification; three of these are
legacy methods, Maudsley et al. (2006), Zhang et al. (2018), and
Bustillo et al. (2020), and two newly created methods that used a random forests
classifier (RFC) to inform their classifications. We found that the random forest
classifier was the most accurate at predicting spectra quality (balanced
accuracy for RF of 88% vs legacy of 70%, 72%, or 72%). A
Random-Forests-Informed Filtering method (RFIFM) for quality …
A Study Of The Efficacy Of Machine Learning For Diagnosing Obstructive Coronary Artery Disease In Non-Diabetic Patients, Demond Larae Handley
A Study Of The Efficacy Of Machine Learning For Diagnosing Obstructive Coronary Artery Disease In Non-Diabetic Patients, Demond Larae Handley
Theses and Dissertations
According to the Centers for Disease Control and Prevention, about 18.2 million adults age 20 and older have Coronary Artery Disease in the United States. Early diagnosis is therefore of crucial importance to help prevent debilitating consequences, and principally death for many patients. In this study we use data containing gene expression values from peripheral blood samples in 198 non-diabetic patients, with the goal of developing an age and sex gene expression model for diagnosis of Coronary Artery Disease. We employ machine learning methods to obtain a classification based on genetic information, age and sex. Our implementation uses feed forward …
Artificial Neural Network Models For Pattern Discovery From Ecg Time Series, Mehakpreet Kaur
Artificial Neural Network Models For Pattern Discovery From Ecg Time Series, Mehakpreet Kaur
Electronic Theses and Dissertations
Artificial Neural Network (ANN) models have recently become de facto models for deep learning with a wide range of applications spanning from scientific fields such as computer vision, physics, biology, medicine to social life (suggesting preferred movies, shopping lists, etc.). Due to advancements in computer technology and the increased practice of Artificial Intelligence (AI) in medicine and biological research, ANNs have been extensively applied not only to provide quick information about diseases, but also to make diagnostics accurate and cost-effective. We propose an ANN-based model to analyze a patient's electrocardiogram (ECG) data and produce accurate diagnostics regarding possible heart diseases …