Digital Commons Network^™

Respiratory Pattern Analysis For Covid-19 Digital Screening Using Ai Techniques, Annita Tahsin Priyoti

Electronic Thesis and Dissertation Repository

Corona Virus (COVID-19) is a highly contagious respiratory disease that the World Health Organization (WHO) has declared a worldwide epidemic. This virus has spread worldwide, affecting various countries until now, causing millions of deaths globally. To tackle this public health crisis, medical professionals and researchers are working relentlessly, applying different techniques and methods. In terms of diagnosis, respiratory sound has been recognized as an indicator of one’s health condition. Our work is based on cough sound analysis. This study has included an in-depth analysis of the diagnosis of COVID-19 based on human cough sound. Based on cough audio samples from …

Intra-Population Variation Of Hair And Fingernail Stable Hydrogen, Oxygen, Carbon And Nitrogen Isotopes In London, Ontario, Canada Residents During The Covid-19 Pandemic, Sawyer C E Rowe

Electronic Thesis and Dissertation Repository

Lockdowns and travel restrictions during the COVID-19 pandemic forced a significant fraction of London, Ontario, Canada residents to remain in one location for long enough to reach isotopic equilibrium with their primary drinking water source(s). This situation created ideal natural conditions for measuring the isotopic fractionation between the stable hydrogen and oxygen isotopes of drinking water and hair or nail tissues, and for determining the magnitude of intra-population variation in tissue δ²H and δ¹⁸O. Hair and nail of participants who reported exclusively drinking London municipal tap water spanned much larger δ²H and δ^{18 …}

A Deep Topical N-Gram Model And Topic Discovery On Covid-19 News And Research Manuscripts, Yuan Du

Electronic Thesis and Dissertation Repository

Topic modeling with the latent semantic analysis (LSA), the latent Dirichlet allocation (LDA) and the biterm topic model (BTM) has been successfully implemented and used in many areas, including movie reviews, recommender systems, and text summarization, etc. However, these models may become computationally intensive if tested on a humongous corpus. Considering the wide acceptance of machine learning based on deep neural networks, this research proposes two deep neural network (NN) variants, 2-layer NN and 3-layer NN of the LDA modeling techniques. The primary goal is to deal with problems with a large corpus using manageable computational resources.

This thesis analyze …

Call To Action: Sars-Cov-2 And Cerebrovascular Disorders (Cascade), Shahram Abootalebi, Benjamin M. Aertker, Mohammad Sobhan Andalibi, Negar Asdaghi, Ozlem Aykac, M. Reza Azarpazhooh, M. Cecilia Bahit, Kristian Barlinn, Hamidon Basri, Reza Bavarsad Shahripour, Anna Bersano, Jose Biller, Afshin Borhani-Haghighi, Robert D. Brown, Bruce Cv Campbell, Salvador Cruz-Flores, Deidre Anne De Silva, Mario Di Napoli, Afshin A. Divani, Randall C. Edgell, Johanna T. Fifi, Abdoreza Ghoreishi, Teruyuki Hirano, Keun Sik Hong, Chung Y. Hsu, Josephine F. Huang, Manabu Inoue, Amanda L. Jagolino, Moira Kapral, Hoo Fan Kee, Zafer Keser, Rakesh Khatri

Epidemiology and Biostatistics Publications

Background and purpose: The novel severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), now named coronavirus disease 2019 (COVID-19), may change the risk of stroke through an enhanced systemic inflammatory response, hypercoagulable state, and endothelial damage in the cerebrovascular system. Moreover, due to the current pandemic, some countries have prioritized health resources towards COVID-19 management, making it more challenging to appropriately care for other potentially disabling and fatal diseases such as stroke. The aim of this study is to identify and describe changes in stroke epidemiological trends before, during, and after the COVID-19 pandemic. Methods: This is an international, multicenter, hospital-based …

Machine Learning With Digital Signal Processing For Rapid And Accurate Alignment-Free Genome Analysis: From Methodological Design To A Covid-19 Case Study, Gurjit Singh Randhawa

Electronic Thesis and Dissertation Repository

In the field of bioinformatics, taxonomic classification is the scientific practice of identifying, naming, and grouping of organisms based on their similarities and differences. The problem of taxonomic classification is of immense importance considering that nearly 86% of existing species on Earth and 91% of marine species remain unclassified. Due to the magnitude of the datasets, the need exists for an approach and software tool that is scalable enough to handle large datasets and can be used for rapid sequence comparison and analysis. We propose ML-DSP, a stand-alone alignment-free software tool that uses Machine Learning and Digital Signal Processing to …

Excess Mortality From Covid-19: A Commentary On The Italian Experience, Paolo Pasquariello, Saverio Stranges

Epidemiology and Biostatistics Publications

No abstract provided.