Výsledky vyhledávání - "Shaer, Ibrahim"

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Thwarting Cybersecurity Attacks with Explainable Concept Drift

Cyber-security attacks pose a significant threat to the operation of autonomous systems. Particularly impacted are the Heating, Ventilation, and Air Conditioning (HVAC) systems in smart buildings, which depend on data gathered by sensors and Machine

Externí odkaz: http://arxiv.org/abs/2403.13023

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Efficient Transformer-based Hyper-parameter Optimization for Resource-constrained IoT Environments

Autor: Shaer, Ibrahim, Nikan, Soodeh, Shami, Abdallah

The hyper-parameter optimization (HPO) process is imperative for finding the best-performing Convolutional Neural Networks (CNNs). The automation process of HPO is characterized by its sizable computational footprint and its lack of transparency; bot

Externí odkaz: http://arxiv.org/abs/2403.12237

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CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT Environment

Autor: Shaer, Ibrahim, Shami, Abdallah

Publikováno v: IEEE Transactions on Network and Service Management, vol. 20, no. 2, pp. 1543-1557, June 2023

The Federated Learning (FL) paradigm faces several challenges that limit its application in real-world environments. These challenges include the local models' architecture heterogeneity and the unavailability of distributed Internet of Things (IoT)

Externí odkaz: http://arxiv.org/abs/2307.12149

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Sound Event Classification in an Industrial Environment: Pipe Leakage Detection Use Case

Autor: Shaer, Ibrahim, Shami, Abdallah

In this work, a multi-stage Machine Learning (ML) pipeline is proposed for pipe leakage detection in an industrial environment. As opposed to other industrial and urban environments, the environment under study includes many interfering background no

Externí odkaz: http://arxiv.org/abs/2205.02706

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Concept Drift Detection in Federated Networked Systems

Autor: Manias, Dimitrios Michael, Shaer, Ibrahim, Yang, Li, Shami, Abdallah

As next-generation networks materialize, increasing levels of intelligence are required. Federated Learning has been identified as a key enabling technology of intelligent and distributed networks; however, it is prone to concept drift as with any ma

Externí odkaz: http://arxiv.org/abs/2109.06088

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Multi-Component V2X Applications Placement in Edge Computing Environment

Autor: Shaer, Ibrahim, Haque, Anwar, Shami, Abdallah

Vehicle-to-everything (V2X) services are attracting a lot of attention in the research and industry communities due to their applicability in the landscape of connected and autonomous vehicles. Such applications have stringent performance requirement

Externí odkaz: http://arxiv.org/abs/2005.06956

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