Doctoral Dissertations

Keywords and Phrases

Biomedical Data Analysis; Clustering; Demand-Side Management; Machine Learning; Smart Grid

Abstract

"The rapid progress and development in machine-learning algorithms becomes a key factor in determining the future of humanity. These algorithms and techniques were utilized to solve a wide spectrum of problems extended from data mining and knowledge discovery to unsupervised learning and optimization. This dissertation consists of two study areas. The first area investigates the use of reinforcement learning and adaptive critic design algorithms in the field of power grid control. The second area in this dissertation, consisting of three papers, focuses on developing and applying clustering algorithms on biomedical data. The first paper presents a novel modelling approach for demand side management of electric water heaters using Q-learning and action-dependent heuristic dynamic programming. The implemented approaches provide an efficient load management mechanism that reduces the overall power cost and smooths grid load profile. The second paper implements an ensemble statistical and subspace-clustering model for analyzing the heterogeneous data of the autism spectrum disorder. The paper implements a novel k-dimensional algorithm that shows efficiency in handling heterogeneous dataset. The third paper provides a unified learning model for clustering neuroimaging data to identify the potential risk factors for suboptimal brain aging. In the last paper, clustering and clustering validation indices are utilized to identify the groups of compounds that are responsible for plant uptake and contaminant transportation from roots to plants edible parts"--Abstract, page iv.

Advisor(s)

Wunsch, Donald C.

Committee Member(s)

Beetner, Daryl G.
Choi, Minsu
Ferguson, Ian T.
Gosavi, Abhijit

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Computer Engineering

Sponsor(s)

U.S. National Science Foundation
Higher Committee for Education Development in Iraq
Missouri University of Science and Technology Intelligent Systems Center
Mary K. Finley Missouri Endowment

Comments

This work was supported by National Science Foundation under Award Number 1606036.

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2018

Journal article titles appearing in thesis/dissertation

  • Demand-side management of domestic electric water heaters using approximate dynamic programming
  • Ensemble statistical and subspace clustering model for analysis of autism spectrum disorder phenotypes
  • Neuroimaging biomarkers of cognitive decline in healthy older adults via unified learning
  • A deeper look at plant uptake of environmental contaminants and associated human health risks using intelligent approaches

Pagination

xiii, 149 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2018 Khalid Khairullah Mezied Al-Jabery, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11368

Electronic OCLC #

1051223701

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