A lightweight ensemble machine learning framework integrating Light Gradient Boosting, Histogram-based Gradient Boosting, and Categorical Boosting to detect both sensor-based and broader cyber-attack types in UAV environments is proposed, introducing an interpretable, high-performance detection framework capable of ope...
C. Chidimma, P. Asuquo, Ihemereze Chijioke Nnanna et al.· E3S Web of Conferences· 0 citations
Heating, Ventilation, and Air Conditioning (HVAC) systems play a critical role in ensuring energy efficiency, occupant comfort, and sustainability in modern buildings. Their complex operational dynamics,however, make them prone to faults that, if undetected, can result in higher energy consumption, reduced comfort, and...
I. Samuel, B. Stephen, S. Ozuomba et al.· E3S Web of Conferences· 0 citations
Financial fraud detection in transaction networks is challenging due to evolving attack strategies, complex relational structures, and extreme class imbalance. We propose a hybrid deep learning model that fuses Graph Convolutional Neural Networks (GCNNs) with bidirectional LSTMs enhanced by temporal attention, enabling...
Ofonime Dominic Okon, Imo Enang, B. Stephen et al.· E3S Web of Conferences· 0 citations
The development of an ensemble machine learning pipeline for predicting resistance gene functions using the Comprehensive Antibiotic Resistance Database (CARD) demonstrates superior performance while maintaining computational efficiency, making it suitable for deployment in resourceconstrained environments.
Princewill Ahumaraeze, Ofonime Dominic Okon, P. Asuquo et al.· E3S Web of Conferences· 0 citations
A deep learning-based failure prediction model that integrates Convolutional Neural Networks and Bidirectional Long Short-Term Memory networks to identify job failures before they occur is presented, improving the performance of cloud computing applications by reducing job failures and optimising resource utilisation.
Wunukhen Shehu Awudu, P. Asuquo, B. Agbor et al.· E3S Web of Conferences· 0 citations
Experimental evaluation demonstrates up to 95% detection accuracy, a 50% reduction in response latency, and scalability to over 100,000 IoT devices without performance degradation, highlighting the suitability of SC-ARS for deployment in smart cities, industrial IoT, and decentralized critical infrastructures where tru...
S. Bassey, B. Stephen, Emediong Bassey Obot 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
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