Divine Iloh
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Independent Researcher
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Divine Iloh's research centers on the application of advanced computational techniques, particularly machine learning and artificial intelligence, to address complex problems across various domains. Recent work includes developing generative models for privacy-preserving synthetic student data, investigating deadline-aware prefetching for learners with intermittent connectivity, and designing adaptive cybersecurity architectures for digital product ecosystems using agentic AI. Iloh has also explored optimized deep learning frameworks for malware classification, integrating Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) approaches. Further research encompasses algorithmic trading and machine learning for market prediction and strategy development.
Iloh's scholarly output includes six publications with 16 citations and an h-index of 2. Key collaborations include work with Oluwatomiwa Ajiferuke at the University of Arkansas at Little Rock, with whom Iloh shares one publication. Iloh remains an active researcher.
Metrics
- h-index: 2
- Publications: 6
- Citations: 16
Positions
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Independent Researcher publications 2025–2026University of Arkansas at Little Rock Business Information Systems and Analytics ORCID
Selected Publications
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A Comprehensive Evaluation of Generative Models for Privacy-Preserving Synthetic Student Data (2026)
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Generative Private Synthetic Student Data for Learning Analytics: An Empirical Study (2025)
Collaboration Network
Top Collaborators
- Generative Private Synthetic Student Data for Learning Analytics: An Empirical Study
- Generative Private Synthetic Student Data for Learning Analytics: An Empirical Study
- Generative Private Synthetic Student Data for Learning Analytics: An Empirical Study
- A Comprehensive Evaluation of Generative Models for Privacy-Preserving Synthetic Student Data
- A Comprehensive Evaluation of Generative Models for Privacy-Preserving Synthetic Student Data
- A Comprehensive Evaluation of Generative Models for Privacy-Preserving Synthetic Student Data
- A Comprehensive Evaluation of Generative Models for Privacy-Preserving Synthetic Student Data
- A Comprehensive Evaluation of Generative Models for Privacy-Preserving Synthetic Student Data
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