Abstract

A Fuzzy Inference System (FIS) is a Machine Learning (ML) model used for a multitude of decision-making problems with multiple possible optimization avenues. It operates from the premise of Fuzzy Logic, created by Lotfi Zadeh, to provide the capability of handling the uncertainty, or noise, of data. After a brief overview of the FIS, we will discuss one of the multiple paths forward for optimization and improvement. That is, we will discuss Genetic Algorithm (GA) integration into the FIS architecture. We will showcase three real-world use-cases of the FIS with corresponding published works. First, using an FIS for guaging the accuracy of an exercise in a mobile fitness safety and improvement application. Second, using an FIS to predict the pain severity of an individual with Complex Regional Pain Syndrome (CRPS) and assigning an exercise to assist in alleviating that pain. Lastly, using an FIS for predicting word difficulty in a word scramble game to assign to dementia caregivers for stress detection. The three topics are published in the 2021 IEEE 33rd international Conference on tools with Artificial Intelligence (ICTAI), the 2023 11th International Symposium on Digital Forensics and Security (ISDFS), and the 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), respectively.

About the Speaker

Dr. Garrett Goodman earned his Ph.D. in Computer Science and Engineering at Wright State University where he also earned his M.S. and B.S. in Computer Science as well. His research focuses on incorporating Machine Learning (ML) and Artificial Intelligence (AI) in healthcare and assistive technologies to improve the lives and wellbeing of people in need. The healthcare realms he focuses on are Dementia Caregiver stress assessment, mortality predictions, cardiovascular monitoring, and others that require assistance. He previously worked as a software development consultant at Illumination Works LLC before starting his graduate degrees and briefly worked with the Air Force Research Labs, Sensors Directorate, during his M.S. Though, during his Ph.D., he found a love for education while teaching the class "Introduction to Software Engineering" as an Adjunct Professor. Thus, his enthusiasm for teaching and working with students has settled him in academia in an education focused role as an Assistant Teaching Professor at Miami University of Ohio.