Divine Iloh Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
unknown
Research Areas
Biography and Research Information
OverviewAI-generated summary
Divine Iloh's research focuses on the application of artificial intelligence and machine learning techniques to address complex challenges. His work includes developing adaptive cybersecurity architectures for digital product ecosystems utilizing agentic AI and optimizing deep learning frameworks for malware classification through integrated LSTM and CNN approaches. Iloh has also investigated generative private synthetic student data for learning analytics and explored advanced techniques in algorithmic trading for market prediction and strategy development. He has published four articles, with a total of eight citations and an h-index of 2. Iloh collaborates with Oluwatomiwa Ajiferuke at the University of Arkansas at Little Rock, with whom he shares one publication.
Metrics
- h-index: 2
- Publications: 4
- Citations: 9
Selected Publications
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Generative Private Synthetic Student Data for Learning Analytics: An Empirical Study (2025)
Collaboration Network
Top Collaborators
- Algorithmic trading and machine learning: Advanced techniques for market prediction and strategy development
- Algorithmic trading and machine learning: Advanced techniques for market prediction and strategy development
- Algorithmic trading and machine learning: Advanced techniques for market prediction and strategy development
- Algorithmic trading and machine learning: Advanced techniques for market prediction and strategy development
- Algorithmic trading and machine learning: Advanced techniques for market prediction and strategy development
- Algorithmic trading and machine learning: Advanced techniques for market prediction and strategy development
- Algorithmic trading and machine learning: Advanced techniques for market prediction and strategy development
- An Optimized Deep Learning Framework for Malware Classification Using Integrated LSTM and CNN Approaches
- An Optimized Deep Learning Framework for Malware Classification Using Integrated LSTM and CNN Approaches
- An Optimized Deep Learning Framework for Malware Classification Using Integrated LSTM and CNN Approaches
- An Optimized Deep Learning Framework for Malware Classification Using Integrated LSTM and CNN Approaches
- An Optimized Deep Learning Framework for Malware Classification Using Integrated LSTM and CNN Approaches
- An Optimized Deep Learning Framework for Malware Classification Using Integrated LSTM and CNN Approaches
- Adaptive Cybersecurity Architecture for Digital Product Ecosystems Using Agentic AI
- Adaptive Cybersecurity Architecture for Digital Product Ecosystems Using Agentic AI
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