Fadel Megahed

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Enders Endres Associate Professor

University of Miami

United States

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Fadel Megahed is the Enders Endres Associate Professor at Miami University in the United States. His research areas include statistical quality control, the application of generative AI in practice and education, and predictive modeling, as evidenced by his recent articles on AI chatbots, classifier performance, and human reliability modeling. He is actively engaged in exploring the intersection of statistical methodologies and emerging technologies to enhance research and industrial practices.

Recent Grants

Grant: Close

GOALI/Collaborative Research: Human Maintenance - A Prognostics Framework to Model Changes in Drivers' Safety Performance and Optimize Dispatching Policies

Open Date: 2016-10-01

Close Date: 2019-09-30

Grant: Close

Planning Grant: I/UCRC for Advanced Vehicle Manufacturing

Open Date: 2014-04-15

Close Date: 2015-03-31

Articles (18)

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UNITED STATES

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Ehsan Esfahani

Associate Professor

State University of New York at Buffalo

UNITED STATES

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Bianca Maria Colosimo

professor

Politecnico di Milano

ITALY

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Hamidreza Ahady Dolatsara

Clark University

UNITED STATES

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Inez Maria Zwetsloot

Academic, Associate Professor

University of Amsterdam

NETHERLANDS

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L. Allison Jones-Farmer

University of Miami

UNITED STATES

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Lora Cavuoto

University at Buffalo

UNITED STATES

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Fadel Megahed

Research Interests

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About

Recent Grants

Articles (18)

Statistical Process Monitoring from Industry 2.0 to Industry 4.0: Insights into Research and Practice

Editorial advice for selecting an open-source license for your next paper: Navigating copyrights for publicly facing AI chatbots

Introducing ChatSQC: Enhancing statistical quality control with augmented AI

Comparing classifier performance with baselines

Shortest Path Problems with a Crash Risk Objective

How generative AI models such as ChatGPT can be (mis)used in SPC practice, education, and research? An exploratory study

The relationship between ratings of perceived exertion (RPE) and relative strength for a fatiguing dynamic upper extremity task: A consideration of multiple cycles and conditions

Human reliability modeling in occupational environments toward a safe and productive operator 4.0

Explaining Predictive Model Performance: An Experimental Study of Data Preparation and Model Choice

Functional ANOVA for Upper Extremity Fatigue Analysis during Dynamic Order Picking

Predicting in-hospital mortality after transcatheter aortic valve replacement using administrative data and machine learning

Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approach

Hierarchical point process models for recurring safety critical events involving commercial truck drivers: A reliability framework for human performance modeling

Explaining the Varying Patterns of COVID-19 Deaths Across the United States: 2-Stage Time Series Clustering Framework

Modeling the differences in the time-series profiles of new COVID-19 daily confirmed cases in 3,108 contiguous U.S. counties: A retrospective analysis

Investigation of Heterogeneity Sources for Occupational Task Recognition via Transfer Learning

Personalized and Nonparametric Framework for Detecting Changes in Gait Cycles

Monitoring worker fatigue using wearable devices: A case study to detect changes in gait parameters

Collaborators (11)

Alexander Vinel

Wenbin Zhang

Ying-Ju Chen

Hong Xian

William H. Woodall

Ehsan Esfahani

Bianca Maria Colosimo

Hamidreza Ahady Dolatsara

Inez Maria Zwetsloot

L. Allison Jones-Farmer

Lora Cavuoto

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