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CONNECTING THE NOSE AND THE BRAIN: DEEP LEARNING FOR CHEMICAL GAS SENSING

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Date Issued:
2019
Summary:
The success of deep learning in applications including computer vision, natural language processing, and even the game of Go can only be a orded by powerful computational resources and vast data sets. Data sets coming from the medical application are often much smaller and harder to acquire. Here a novel data approach is explained and used to demonstrate how to use deep learning as a step in data discovery, classi cation, and ultimately support for further investigation. Data sets used to illustrate these successes come from common ion-separation techniques that allow for gas samples to be quantitatively analyzed. The success of this data approach allows for the deployment of deep learning to smaller data sets.
Title: CONNECTING THE NOSE AND THE BRAIN: DEEP LEARNING FOR CHEMICAL GAS SENSING.
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Name(s): Stark, Emily Nicole, author
Barenholtz, Elan, Thesis advisor
Florida Atlantic University, Degree grantor
Department of Psychology
Charles E. Schmidt College of Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2019
Date Issued: 2019
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 71 p.
Language(s): English
Summary: The success of deep learning in applications including computer vision, natural language processing, and even the game of Go can only be a orded by powerful computational resources and vast data sets. Data sets coming from the medical application are often much smaller and harder to acquire. Here a novel data approach is explained and used to demonstrate how to use deep learning as a step in data discovery, classi cation, and ultimately support for further investigation. Data sets used to illustrate these successes come from common ion-separation techniques that allow for gas samples to be quantitatively analyzed. The success of this data approach allows for the deployment of deep learning to smaller data sets.
Identifier: FA00013416 (IID)
Degree granted: Thesis (M.A.)--Florida Atlantic University, 2019.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Deep Learning
Data sets
Gases--Analysis
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00013416
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.