posted on 2023-05-01, 00:00authored byMaryam Pishgar
With the ongoing digitization of industries and the rising number of interconnected devices, an increasing amount of system recordings are created. Process mining describes a set of techniques that help in the automatic discovery of mathematical models from such system recordings. Such system recordings could be complex including many concurrencies, noisy and infrequent behaviors. Even though many process discovery algorithms and pre-processing steps
have been proposed to remove such behaviors, still none of them have been able to decrease the complexity of the process models and show the dynamic behavior of the system closely. The
analysis of the discovered models might reveal important knowledge about the system behavior that is otherwise impossible to obtain. Therefore, generating a pre-processing step to improve the quality of the such recording, hence improving the quality of the process model is essential. Moreover, process mining techniques mainly rely on discrete event system theories in constructing the corresponding mathematical models. Therefore, they perform poorly, when continuous measures such as time and probabilities of the system events need to be considered. On the other hand, such
measures can be effectively modeled and predicted through deep learning. Hence, a system that could leverage both process mining and deep learning techniques for the prediction would be
useful and effective. Additionally, The field of Artificial Intelligence (AI) is rapidly expanding, with many applications seen
routinely in health care, industry, education, and increasingly in workplaces. Although there is
growing evidence of applications of AI in workplaces across all industries to simplify and/or automate tasks there is a limited understanding of the role that AI contributes to address Occupational Safety and Health (OSH) concerns. Hence, a framework that reviews the application of AI in workplaces is essential to highlight the role that AI plays in anticipating and controlling exposure risks in a worker’s immediate environment.
History
Advisor
Darabi, Houshang
Chair
Darabi, Houshang
Department
Mechanical and Industrial Engineering
Degree Grantor
University of Illinois at Chicago
Degree Level
Doctoral
Degree name
PhD, Doctor of Philosophy
Committee Member
Conroy, Lorraine
Anahideh, Hadis
Buy, Ugo
Fuad Issa, Salah