Matthew O. Ayemowa

ORCID, self-asserted

ORCID 0009-0005-0590-6041

Publications

Citations

h-index

Computed over the works the graph holds, so it is a floor rather than a career figure.

i10-index

Publications

8 works in the graph, newest first.

Study of optimal nuclear models and parameters for the Th–U nuclear fuel cycle
2026Energy Storage and SavingJournalOpen accessCorresponding0 citations10.1016/j.enss.2025.09.001
Generative artificial intelligence model for knowledge transfer in data-sparse cross-domain recommender systems
2026Discover ComputingJournalOpen accessFirst author0 citations10.1007/s10791-026-10409-1
Big Data Governance Challenges Arising From Data Generated by Intelligent Systems Technologies: A Systematic Literature Review
2025IEEE AccessJournalOpen accessSenior author25 citations10.1109/access.2025.3528941
Mitigating domain adaptation and cold start challenges in cross-domain recommender systems using generative adversarial network model
2025Electronic Commerce Research and ApplicationsJournalFirst authorCorresponding2 citations10.1016/j.elerap.2025.101564
Addressing Cold Start Challenges in Recommender Systems Using Generative Adversarial Networks with Auxiliary Information
2025Lecture notes in electrical engineeringPublicationFirst author0 citations10.1007/978-981-96-6034-6_10
Impact, Benefits, and Pitfalls of Big Data Governance on Data-Driven Organizations: A Literature Review
2025Lecture notes in electrical engineeringPublicationSenior author0 citations10.1007/978-981-95-2577-5_31
A systematic review of the literature on deep learning approaches for cross-domain recommender systems
2024Decision Analytics JournalJournalOpen accessFirst authorCorresponding28 citations10.1016/j.dajour.2024.100518
Harnessing and Mitigating Big Data Governance Challenges Using Hybrid Approach: A Systematic Literature Review
2024IEEE AccessJournalOpen access4 citations10.1109/access.2024.3498947