{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/95128"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/95128","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Machine Learning based Predictive Modeling of Stochastic Systems","abstract":"Complex signals are ubiquitous in our daily lives, and interpreting and modeling them is vital for scientific advancement. Traditional methods for predictive modeling of complex signals include statistical signal processing and physics-based simulations. However, statistical signal processing methods often struggle to fully utilize complex and rich datasets, while physics-based simulations can be computationally demanding. As an alternative approach, machine learning (ML) offers a more effective method for the predictive modeling of complex signals. This research explores the applicability of ML-based predictive modeling to a biomedical and a mechanical system through two case studies. The first case study focuses on developing a machine learning-based model for early-stage glaucoma detection using electroretinogram signals, which has been a challenging problem in ophthalmology. By leveraging medically relevant information contained in ERG signals, the study aims to establish a novel and reliable predictive framework for the early detection of glaucoma using a machine-learning-based algorithm. The results demonstrate that machine-learning-based models, trained using advanced wavelet-based features, can effectively detect the early stage of glaucoma from ERG stochastic signals. The second case study centers on developing a machine learning-based model for stall delay correction in wind turbines. Existing stall delay correction models rely on 2D airfoil characteristics, which can lead to inaccuracies in predicting aerodynamic loads during design and, consequently, result in structural failure due to excessive load. To address this issue, the study proposes a novel stall delay correction model based on the soft computing technique of symbolic regression. The model offers high-level precise aerodynamic performance prediction through the blade element momentum process, making it a promising alternative for accurate and efficient stall delay correction in wind turbines.","abstract_html":"Complex signals are ubiquitous in our daily lives, and interpreting and modeling them is vital for scientific advancement. Traditional methods for predictive modeling of complex signals include statistical signal processing and physics-based simulations. However, statistical signal processing methods often struggle to fully utilize complex and rich datasets, while physics-based simulations can be computationally demanding. As an alternative approach, machine learning (ML) offers a more effective method for the predictive modeling of complex signals. This research explores the applicability of ML-based predictive modeling to a biomedical and a mechanical system through two case studies. The first case study focuses on developing a machine learning-based model for early-stage glaucoma detection using electroretinogram signals, which has been a challenging problem in ophthalmology. By leveraging medically relevant information contained in ERG signals, the study aims to establish a novel and reliable predictive framework for the early detection of glaucoma using a machine-learning-based algorithm. The results demonstrate that machine-learning-based models, trained using advanced wavelet-based features, can effectively detect the early stage of glaucoma from ERG stochastic signals. The second case study centers on developing a machine learning-based model for stall delay correction in wind turbines. Existing stall delay correction models rely on 2D airfoil characteristics, which can lead to inaccuracies in predicting aerodynamic loads during design and, consequently, result in structural failure due to excessive load. To address this issue, the study proposes a novel stall delay correction model based on the soft computing technique of symbolic regression. The model offers high-level precise aerodynamic performance prediction through the blade element momentum process, making it a promising alternative for accurate and efficient stall delay correction in wind turbines.","abstract_has_math":false,"creators":["Gajendran, Mohan Kumar"],"institution":"University of Missouri--Kansas City","degree_name":"Ph.D. (Doctor of Philosophy)","degree_level":"Doctoral","degree_discipline":"Mechanical Engineering (UMKC)","degree_department":null,"school":null,"contributors":[],"advisors":["Mehdizadeh, Amirfarhang"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-24T05:18:49Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10355/95128","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Mehdizadeh, Amirfarhang"]},{"key":"dc:creator","label":"Author","values":["Gajendran, Mohan Kumar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-05-23T16:20:41Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-05-23T16:20:41Z"]},{"key":"dc:date.issued","label":"Date","values":["2023"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering (UMKC)","Mathematics (UMKC)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D. (Doctor of Philosophy)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Kansas City"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/95128"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Title from PDF of title page, viewed June 14, 2023","Dissertation advisor: Amirfarhang Mehdizadeh","Vita","Includes bibliographical references (pages 89-121)","Dissertation (Ph.D.)--Department of Civil and Mechanical Engineering, Department of Mathematics and Statistics. University of Missouri--Kansas City, 2023"]},{"key":"dc:description.abstract","label":"Abstract","values":["Complex signals are ubiquitous in our daily lives, and interpreting and modeling them is vital for scientific advancement. Traditional methods for predictive modeling of complex signals include statistical signal processing and physics-based simulations. However, statistical signal processing methods often struggle to fully utilize complex and rich datasets, while physics-based simulations can be computationally demanding. As an alternative approach, machine learning (ML) offers a more effective method for the predictive modeling of complex signals. This research explores the applicability of ML-based predictive modeling to a biomedical and a mechanical system through two case studies. The first case study focuses on developing a machine learning-based model for early-stage glaucoma detection using electroretinogram signals, which has been a challenging problem in ophthalmology. By leveraging medically relevant information contained in ERG signals, the study aims to establish a novel and reliable predictive framework for the early detection of glaucoma using a machine-learning-based algorithm. The results demonstrate that machine-learning-based models, trained using advanced wavelet-based features, can effectively detect the early stage of glaucoma from ERG stochastic signals. The second case study centers on developing a machine learning-based model for stall delay correction in wind turbines. Existing stall delay correction models rely on 2D airfoil characteristics, which can lead to inaccuracies in predicting aerodynamic loads during design and, consequently, result in structural failure due to excessive load. To address this issue, the study proposes a novel stall delay correction model based on the soft computing technique of symbolic regression. The model offers high-level precise aerodynamic performance prediction through the blade element momentum process, making it a promising alternative for accurate and efficient stall delay correction in wind turbines."]},{"key":"dc:title","label":"Title","values":["Machine Learning based Predictive Modeling of Stochastic Systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Mehdizadeh, Amirfarhang"],"dc:creator":["Gajendran, Mohan Kumar"],"dc:date.accessioned":["2023-05-23T16:20:41Z"],"dc:date.available":["2023-05-23T16:20:41Z"],"dc:date.issued":["2023"],"dc:description":["Title from PDF of title page, viewed June 14, 2023","Dissertation advisor: Amirfarhang Mehdizadeh","Vita","Includes bibliographical references (pages 89-121)","Dissertation (Ph.D.)--Department of Civil and Mechanical Engineering, Department of Mathematics and Statistics. University of Missouri--Kansas City, 2023"],"dc:description.abstract":["Complex signals are ubiquitous in our daily lives, and interpreting and modeling them is vital for scientific advancement. Traditional methods for predictive modeling of complex signals include statistical signal processing and physics-based simulations. However, statistical signal processing methods often struggle to fully utilize complex and rich datasets, while physics-based simulations can be computationally demanding. As an alternative approach, machine learning (ML) offers a more effective method for the predictive modeling of complex signals. This research explores the applicability of ML-based predictive modeling to a biomedical and a mechanical system through two case studies. The first case study focuses on developing a machine learning-based model for early-stage glaucoma detection using electroretinogram signals, which has been a challenging problem in ophthalmology. By leveraging medically relevant information contained in ERG signals, the study aims to establish a novel and reliable predictive framework for the early detection of glaucoma using a machine-learning-based algorithm. The results demonstrate that machine-learning-based models, trained using advanced wavelet-based features, can effectively detect the early stage of glaucoma from ERG stochastic signals. The second case study centers on developing a machine learning-based model for stall delay correction in wind turbines. Existing stall delay correction models rely on 2D airfoil characteristics, which can lead to inaccuracies in predicting aerodynamic loads during design and, consequently, result in structural failure due to excessive load. To address this issue, the study proposes a novel stall delay correction model based on the soft computing technique of symbolic regression. The model offers high-level precise aerodynamic performance prediction through the blade element momentum process, making it a promising alternative for accurate and efficient stall delay correction in wind turbines."],"dc:identifier.uri":["https://hdl.handle.net/10355/95128"],"dc:title":["Machine Learning based Predictive Modeling of Stochastic Systems"],"thesis:degree_discipline":["Mechanical Engineering (UMKC)","Mathematics (UMKC)"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Ph.D. (Doctor of Philosophy)"],"thesis:institution_name":["University of Missouri--Kansas City"]},"updated_at":"2026-07-24T05:18:49Z"}