Muzakirruddin Ahmed Mohammed
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Research Assistant
Research Areas
Biography and Research Information
OverviewAI-generated summary
Muzakirruddin Ahmed Mohammed's research focuses on the application of artificial intelligence and machine learning techniques, particularly in the areas of entity resolution and natural language processing. He has investigated advanced frameworks for entity resolution, including multi-agent systems and the use of generative AI for tasks such as household movement discovery and industrial part specification extraction. His work also explores policy-aware generative AI for data access governance and sentiment-based recommendation systems for e-commerce. Mohammed has contributed to the understanding of machine learning applications, technologies, and challenges within manufacturing and Industry 4.0 contexts.
His recent publications in 2025 and 2026 highlight his continued engagement with sophisticated AI methodologies. Mohammed collaborates with researchers at the University of Arkansas at Little Rock, including Mert Can Çakmak and John R. Talburt. With an h-index of 4 and 45 total citations across 18 publications, his work contributes to the growing body of research in applied AI and machine learning.
Metrics
- h-index: 4
- Publications: 18
- Citations: 45
Positions
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Research Assistant publications 2026University of Arkansas at Little Rock Institution web page
Selected Publications
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A Coordinated Multi-agent Architecture for Automated Data Governance Using Large Language Models (2026)
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Multilingual Customer Record Linkage: A Novel Approach Using LLMs for Cross-Lingual Entity Resolution (2026)
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Case count metric for comparative analysis of entity resolution results (2026)
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Engineering Queryable Industrial Product Knowledge Bases from Technical Documents: A Survey of Data, Knowledge, and Retrieval Pipelines (2026)
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Positive Data Control: A Secure Architecture for LLM-Mediated Data Governance (2026)
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AI-Powered Multi-stakeholder Ecosystems for Global Development: A Design Research Study on the GSI D-Hub Proof-of-Concept Platform (2026)
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Evaluating Semantic and Syntactic Understanding in Large Language Models for Payroll Systems (2026)
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A System for Name and Address Parsing with Large Language Models (2026)
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Retrieval-Augmented Multi-LLM Ensemble for Industrial Part Specification Extraction (2025)
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Policy-Aware Generative AI for Safe, Auditable Data Access Governance (2025)
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Entity Resolution with Household Movement Discovery Using Google Generative AI (2025)
Collaboration Network
Top Collaborators
- Entity Resolution with Household Movement Discovery Using Google Generative AI
- Policy-Aware Generative AI for Safe, Auditable Data Access Governance
- Retrieval-Augmented Multi-LLM Ensemble for Industrial Part Specification Extraction
- AI-Powered Multi-stakeholder Ecosystems for Global Development: A Design Research Study on the GSI D-Hub Proof-of-Concept Platform
- Positive Data Control: A Secure Architecture for LLM-Mediated Data Governance
Showing 5 of 6 shared publications
- Policy-Aware Generative AI for Safe, Auditable Data Access Governance
- Case count metric for comparative analysis of entity resolution results
- AI-Powered Multi-stakeholder Ecosystems for Global Development: A Design Research Study on the GSI D-Hub Proof-of-Concept Platform
- Positive Data Control: A Secure Architecture for LLM-Mediated Data Governance
- Engineering Queryable Industrial Product Knowledge Bases from Technical Documents: A Survey of Data, Knowledge, and Retrieval Pipelines
- Policy-Aware Generative AI for Safe, Auditable Data Access Governance
- Positive Data Control: A Secure Architecture for LLM-Mediated Data Governance
- Multilingual Customer Record Linkage: A Novel Approach Using LLMs for Cross-Lingual Entity Resolution
- A Coordinated Multi-agent Architecture for Automated Data Governance Using Large Language Models
- Policy-Aware Generative AI for Safe, Auditable Data Access Governance
- Evaluating Semantic and Syntactic Understanding in Large Language Models for Payroll Systems
- Multilingual Customer Record Linkage: A Novel Approach Using LLMs for Cross-Lingual Entity Resolution
- A Coordinated Multi-agent Architecture for Automated Data Governance Using Large Language Models
- A System for Name and Address Parsing with Large Language Models
- AI-Powered Multi-stakeholder Ecosystems for Global Development: A Design Research Study on the GSI D-Hub Proof-of-Concept Platform
- A Coordinated Multi-agent Architecture for Automated Data Governance Using Large Language Models
- A System for Name and Address Parsing with Large Language Models
- Evaluating Semantic and Syntactic Understanding in Large Language Models for Payroll Systems
- A Coordinated Multi-agent Architecture for Automated Data Governance Using Large Language Models
- Entity Resolution with Household Movement Discovery Using Google Generative AI
- Case count metric for comparative analysis of entity resolution results
- A System for Name and Address Parsing with Large Language Models
- Evaluating Semantic and Syntactic Understanding in Large Language Models for Payroll Systems
- A System for Name and Address Parsing with Large Language Models
- Evaluating Semantic and Syntactic Understanding in Large Language Models for Payroll Systems
- Case count metric for comparative analysis of entity resolution results
- Multilingual Customer Record Linkage: A Novel Approach Using LLMs for Cross-Lingual Entity Resolution
- Retrieval-Augmented Multi-LLM Ensemble for Industrial Part Specification Extraction
- Retrieval-Augmented Multi-LLM Ensemble for Industrial Part Specification Extraction
- AI-Powered Multi-stakeholder Ecosystems for Global Development: A Design Research Study on the GSI D-Hub Proof-of-Concept Platform
- AI-Powered Multi-stakeholder Ecosystems for Global Development: A Design Research Study on the GSI D-Hub Proof-of-Concept Platform
- Case count metric for comparative analysis of entity resolution results
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