{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-1867"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-1867","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Enhancing health and energy efficiency through data-driven urban initiatives: a smart city approach","abstract":"With the recent growing recognition, the \"smart city\" project aims to advance the quality of modern cities through technology and data science. In this dissertation, two fundamental smart city applications are explored: Smart Health and Smart Energy. The goal of the presented studies is to transform the future of healthcare and energy through data-driven solutions. For Smart Health, statistical analysis and machine learning algorithms are employed to improve patient management and their eventual outcomes. This is done by implementing a predictive analytics framework to identify various risk factors associated with respective medical conditions. The aim of the Smart Energy application is to analyze energy meter data to improve energy efficiency and manage power demand in both residential and industrial sectors. Various state-of-the-art machine learning algorithms are investigated by scrutinizing data obtained from multiple sources. The proposed method introduced in this dissertation emphasizes the effectiveness of data-driven approaches in urban development and planning. The unification of technology and infrastructure will improve individual quality of life and advance the community into a new era of smart society.","abstract_html":"With the recent growing recognition, the "smart city" project aims to advance the quality of modern cities through technology and data science. In this dissertation, two fundamental smart city applications are explored: Smart Health and Smart Energy. The goal of the presented studies is to transform the future of healthcare and energy through data-driven solutions. For Smart Health, statistical analysis and machine learning algorithms are employed to improve patient management and their eventual outcomes. This is done by implementing a predictive analytics framework to identify various risk factors associated with respective medical conditions. The aim of the Smart Energy application is to analyze energy meter data to improve energy efficiency and manage power demand in both residential and industrial sectors. Various state-of-the-art machine learning algorithms are investigated by scrutinizing data obtained from multiple sources. The proposed method introduced in this dissertation emphasizes the effectiveness of data-driven approaches in urban development and planning. The unification of technology and infrastructure will improve individual quality of life and advance the community into a new era of smart society.","abstract_has_math":false,"creators":["Cho, Jin"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Sartipi, Mina","Fell, Nancy; Wu, Dalei; Gao, Lani","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05-31T07:00:00Z","date_published":"2022-05-31T07:00:00Z","updated_at":"2026-07-24T05:47:06Z","subjects":["Big data","Machine learning","Smart cities"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/697","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sartipi, Mina","Fell, Nancy; Wu, Dalei; Gao, Lani","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Cho, Jin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-05-01T07:00:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-05-31T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Doctoral dissertations","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Big data","Machine learning","Smart cities"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/697"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Computational Science","Ph. D.; A dissertation submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Doctor of Philosophy."]},{"key":"dc:description.abstract","label":"Abstract","values":["With the recent growing recognition, the \"smart city\" project aims to advance the quality of modern cities through technology and data science. In this dissertation, two fundamental smart city applications are explored: Smart Health and Smart Energy. The goal of the presented studies is to transform the future of healthcare and energy through data-driven solutions. For Smart Health, statistical analysis and machine learning algorithms are employed to improve patient management and their eventual outcomes. This is done by implementing a predictive analytics framework to identify various risk factors associated with respective medical conditions. 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In this dissertation, two fundamental smart city applications are explored: Smart Health and Smart Energy. The goal of the presented studies is to transform the future of healthcare and energy through data-driven solutions. For Smart Health, statistical analysis and machine learning algorithms are employed to improve patient management and their eventual outcomes. This is done by implementing a predictive analytics framework to identify various risk factors associated with respective medical conditions. The aim of the Smart Energy application is to analyze energy meter data to improve energy efficiency and manage power demand in both residential and industrial sectors. Various state-of-the-art machine learning algorithms are investigated by scrutinizing data obtained from multiple sources. The proposed method introduced in this dissertation emphasizes the effectiveness of data-driven approaches in urban development and planning. 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