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AN ADAPTIVE DEEP LEARNING FRAMEWORK TO ENHANCE THE PERFORMANCE OF MONITORING SYSTEMS FOR BIOMEDICAL APPLICATIONS

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Date Issued:
2024
Abstract/Description:
Deep learning strategies combined with wearable sensors have advanced the capabilities of monitoring systems in biomedical applications, offering precise and efficient solutions for diagnosing and managing diseases. However, applying these systems faces several challenges. One of the challenges is the diminishing performance when these systems encounter new data with more complex patterns than those seen before. Another challenge is the limited availability of labeled data, on which deep learning-based systems depend highly. Additionally, obtaining high-quality labeled data to train deep learning models is often expensive, requiring significant time and resources. Another significant challenge is ensuring the practicality, accessibility, and convenience of the monitoring systems. This dissertation proposes an innovative deep learning framework to overcome these challenges and improve system generalization performance in classification and regression tasks, specifically monitoring patients with neurological disorders like Parkinson’s.
Title: AN ADAPTIVE DEEP LEARNING FRAMEWORK TO ENHANCE THE PERFORMANCE OF MONITORING SYSTEMS FOR BIOMEDICAL APPLICATIONS.
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Name(s): Shuqair, Mustafa , author
Ghoraani, Behnaz , Thesis advisor
Florida Atlantic University, Degree grantor
Department of Computer and Electrical Engineering and Computer Science
College of Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2024
Date Issued: 2024
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 177 p.
Language(s): English
Abstract/Description: Deep learning strategies combined with wearable sensors have advanced the capabilities of monitoring systems in biomedical applications, offering precise and efficient solutions for diagnosing and managing diseases. However, applying these systems faces several challenges. One of the challenges is the diminishing performance when these systems encounter new data with more complex patterns than those seen before. Another challenge is the limited availability of labeled data, on which deep learning-based systems depend highly. Additionally, obtaining high-quality labeled data to train deep learning models is often expensive, requiring significant time and resources. Another significant challenge is ensuring the practicality, accessibility, and convenience of the monitoring systems. This dissertation proposes an innovative deep learning framework to overcome these challenges and improve system generalization performance in classification and regression tasks, specifically monitoring patients with neurological disorders like Parkinson’s.
Identifier: FA00014542 (IID)
Degree granted: Dissertation (PhD)--Florida Atlantic University, 2024.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Deep learning (Machine learning)
Monitoring systems
Biomedical engineering
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00014542
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