Ali A. Abushaiba
This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.
Researcher
Also affiliated: Southern Illinois University Carbondale (2014); University of Kansas (2016–2022)
Faculty Researcher
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Ali A. Abushaiba's research interests include machine learning applications for industrial automation, with a focus on digital twin and edge AI integration. He has investigated real-time control applications through the integration of microcontrollers with simulation software, and studied sensorless control methods for permanent magnet synchronous motors using reduced-order observers. His work also encompasses a comparative evaluation of DC-DC converter topologies, particularly for electric vehicle chargers, and surveys reinforcement learning-based control in DC-DC converters.
Abushaiba collaborates with Kamran Iqbal and Md Farhan Shahrior at the University of Arkansas at Little Rock, with whom he shares multiple publications. His scholarly contributions are reflected in an h-index of 6 and over 111 citations across 11 publications.
Metrics
- h-index: 6
- Publications: 11
- Citations: 119
Selected Publications
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Reinforcement Learning-based Control in DC-DC Converters: A Survey (2025)
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Comparative Evaluation of DC–DC Converter Topologies for Electric Vehicle Chargers (2025)
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Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration (2025)
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Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration (2025)
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Integration of C2000 Microcontrollers with MATLAB Simulink Embedded Coder: A Real-Time Control Application (2024)
Collaboration Network
Top Collaborators
- Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration
- Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration
- Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration
- Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration
- Comparative Evaluation of DC–DC Converter Topologies for Electric Vehicle Chargers
- Reinforcement Learning-based Control in DC-DC Converters: A Survey
- Integration of C2000 Microcontrollers with MATLAB Simulink Embedded Coder: A Real-Time Control Application
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