This study introduces an alignment-free computational framework for identifying microbial bioremediation potential, establishing a standardized, multi-domain dataset encompassing Bacteria, Protists, Archaea, and Fungi, and integrating metadata on polymer interactions to fill existing data gaps.
U. Luke, Emem Okon Abang, Etimbuk Daniel Akpan et al.· E3S Web of Conferences· 0 citations
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 work proposes an intelligent, lightweight Tiny LSTM–GRU hybrid IDS on the edge to monitor device-generated behavioral patterns in real time, with minimal computational and energy overhead, and proposes an adaptive FedProx-based weighted federated learning framework.
Emmanuel Udok, B. Stephen, U. Luke 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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
