Adeyemi Samuel Ayorinde
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Program Manager
Also affiliated: Federal University of Technology (2023)
Staff Researcher
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Adeyemi Samuel Ayorinde's research focuses on the application of artificial intelligence and the Internet of Things (AI-IoT) to address complex challenges. His work investigates the integration of these technologies for sustainable farming practices, aiming to mitigate ecological damage and reduce financial resource leakage. Additionally, Ayorinde is exploring the development of explainable deep learning models to detect sophisticated cyber-enabled financial fraud within multi-layered FinTech infrastructures. His scholarly output includes three publications, with a recent focus on AI applications in finance and agriculture. Ayorinde holds a position as Program Manager at the University of Arkansas at Little Rock.
Metrics
- h-index: 1
- Publications: 3
- Citations: 10
Selected Publications
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Data extraction dataset for: Machine Learning Applications in Environmental Contamination Assessment — A Systematic Review (PROSPERO CRD420261351495) (2026)
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Data extraction dataset for: Machine Learning Applications in Environmental Contamination Assessment — A Systematic Review (PROSPERO CRD420261351495) (2026)
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Sustainable Farming Through AI-IoT Synergy: Mitigating Ecological Damage and Financial Resource Leakage (2025)
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Explainable Deep Learning Models for Detecting Sophisticated Cyber-Enabled Financial Fraud Across Multi-Layered FinTech Infrastructure (2025)
Collaboration Network
Top Collaborators
- Data extraction dataset for: Machine Learning Applications in Environmental Contamination Assessment — A Systematic Review (PROSPERO CRD420261351495)
- Data extraction dataset for: Machine Learning Applications in Environmental Contamination Assessment — A Systematic Review (PROSPERO CRD420261351495)
- Data extraction dataset for: Machine Learning Applications in Environmental Contamination Assessment — A Systematic Review (PROSPERO CRD420261351495)
- Data extraction dataset for: Machine Learning Applications in Environmental Contamination Assessment — A Systematic Review (PROSPERO CRD420261351495)
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