A dynamic recurrent neural network is proposed to accurately predict workloads and integrates an auto-encoder to effectively extract representations from the original workload data with high dimensionality to enable adaptive and accurate predictions for highly variable workloads.
Okore Kalu, C. Okafor, P. Asuquo et al.· E3S Web of Conferences· 0 citations
Results indicate that decentralized, interoperable, and energy-aware intrusion detection is feasible for large-scale IoT deployments, particularly in resource-constrained IoT environments.
S. Bassey, Emmanuel Udoh, B. Stephen et al.· E3S Web of Conferences· 0 citations
This approach combines semantic understanding of clinical narratives with structural modeling of patient-disease-treatment relationships and successfully validates synthetic EHR data utility for privacy-preserving healthcare AI development while addressing critical requirements necessary for clinical decision support s...
U. Luke, P. Asuquo, Victor Anaga et al.· E3S Web of Conferences· 0 citations
Comparative evaluation against existing machine learning and deep learning approaches indicates that the proposed framework achieves competitive accuracy while maintaining deployment-oriented processing speeds, suggesting that the CNN-GRU model is well-suited for SDN security monitoring under controlled experimental co...
Victor Anaga, B. Stephen, E. Adediji et al.· E3S Web of Conferences· 0 citations
The results show a success in implementing a real time, scalable, privacy-preserving, and adaptive IDS in large-scale IoT deployments through intelligent workload distribution between edge and cloud layers.
Chidera Winifred John, Eduediuyai Ekerete Dan, P. Asuquo et al.· E3S Web of Conferences· 0 citations
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