Fell, Nancy; Wu, Dalei; Gao, Lani
College of Engineering and Computer Science
University of Tennessee at Chattanooga
Place of Publication
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.
I would like to thank many people who have guided me to get to this point. First and foremost, I want to express my gratitude to my parents, Chong and Ok Cho, as well as my brothers, Jin Young Cho and Sam Nho. They gave me so much encouragement and energy for me to finish this long journey. This would have not been possible without the support from my family. I am extremely grateful to my supervisor, Dr. Mina Sartipi. Her experience and knowledge have encouraged me in all the time of my academic research and throughout my academic career. She has been my mentor and supervisor for the past 6 years. During these years, she has provided me with valuable research experience by demanding a high quality of work, supporting my attendance at various conferences, and providing constructive feedback. My academic and research career would have not been possible if it wasn't for her help. I would like to thank the previous members of SCAL. The previous members include: Dr. Zhen Hu, Brandon Allen, Brian Williams, and Austin Harris. Dr. Zhen Hu has been a huge part of my research life. Also, I would like to thank all the current members of CUIP. I would also like to thank the faculty in the Computer Science Department at the University of Tennessee at Chattanooga. They have taught me valuable lessons and pushed me to succeed and focus on my future. Finally, I would like to acknowledge my dissertation committee members: Dr. Nancy Fell, Dr. Dalei Wu, and Dr. Lani Gao for their support and guidance throughout my academic journey. Thank you all for your support and everything that you have done for me.
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.
Big data; Machine learning; Smart cities
xi, 105 leaves
Cho, Jin, "Enhancing health and energy efficiency through data-driven urban initiatives: a smart city approach" (2021). Masters Theses and Doctoral Dissertations.
Available for download on Tuesday, May 31, 2022