Machine Learning Methods for Data Quality Aspects in Edge Computing Platforms Metadata

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Title

  • Main Title Machine Learning Methods for Data Quality Aspects in Edge Computing Platforms

Creator

  • Author: Mitra, Alakananda
    Creator Type: Personal

Contributor

  • Chair: Mohanty, Saraju P.
    Contributor Type: Personal
  • Other: Kougianos, Elias
    Contributor Type: Personal
  • Committee Member: Zhao, Hui
    Contributor Type: Personal
  • Committee Member: Tunc, Cihan
    Contributor Type: Personal

Publisher

  • Name: University of North Texas
    Place of Publication: Denton, Texas

Date

  • Creation: 2022-12

Language

  • English

Description

  • Content Description: In this research, three aspects of data quality with regard to artifical intelligence (AI) have been investigated: detection of misleading fake data, especially deepfakes, data scarcity, and data insufficiency, especially how much training data is required for an AI application. Different application domains where the selected aspects pose issues have been chosen. To address the issues of data privacy, security, and regulation, these solutions are targeted for edge devices. In Chapter 3, two solutions have been proposed that aim to preempt such misleading deepfake videos and images on social media. These solutions are deployable at edge devices. In Chapter 4, a deepfake resilient digital ID system has been described. Another data quality aspect, data scarcity, has been addressed in Chapter 5. One of such agricultural problems is estimating crop damage due to natural disasters. Data insufficiency is another aspect of data quality. The amount of data required to achieve acceptable accuracy in a machine learning (ML) model has been studied in Chapter 6. As the data scarcity problem is studied in the agriculture domain, a similar scenario—plant disease detection and damage estimation—has been chosen for this verification. This research aims to provide ML or deep learning (DL)-based methods to solve several data quality-related issues in different application domains and achieve high accuracy. We hope that this work will contribute to research on the application of machine learning techniques in domains where data quality is a barrier to success.

Subject

  • Keyword: Machine Learning
  • Keyword: Deep Learning
  • Keyword: Data Quality
  • Keyword: Edge Computing
  • Keyword: Deepfake
  • Keyword: Digital ID
  • Keyword: Smart City
  • Keyword: Smart Agriculture
  • Keyword: Agricultural Cyber Physical System
  • Keyword: Crop Damage Estimation
  • Keyword: Plant Disease Detection and Damage Estimation.
  • Keyword: Computer Science
  • Keyword: Artificial Intelligence

Collection

  • Name: UNT Theses and Dissertations
    Code: UNTETD

Institution

  • Name: UNT Libraries
    Code: UNT

Rights

  • Rights Access: public
  • Rights Holder: Mitra, Alakananda
  • Rights License: copyright
  • Rights Statement: Copyright is held by the author, unless otherwise noted. All rights Reserved.

Resource Type

  • Thesis or Dissertation

Format

  • Text

Identifier

  • Accession or Local Control No: submission_3200
  • Archival Resource Key: ark:/67531/metadc2048683

Degree

  • Degree Name: Doctor of Philosophy
  • Degree Level: Doctoral
  • Academic Department: Department of Computer Science and Engineering
  • College: College of Engineering
  • Degree Discipline: Computer Science and Engineering
  • Degree Publication Type: disse
  • Degree Grantor: University of North Texas

Note

  • Embargo Note: The work will be published after approval.
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