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Author

A. Al-Shalabi

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Open access Sep 2026

NFGA-LINEAR: An Explainable Neuro-Fuzzy Genetic Framework for Forecasting Anomaly-Affected Data-Scarce Time Series

NFGA-LINEAR is developed, an interpretable Takagi–Sugeno–Kang neurofuzzy framework in which a genetic algorithm jointly determines the number of rules and optimizes Gaussian antecedents and rule-specific linear consequents and confirms that neither its forecasting nor detection performance is uniformly superior across...

Muneef Abdulkareem Farea Ahmed, Ahmed Al-Shalabi · 0 citations
Review Open access Aug 2026

Stability-Aware and Feasibility-Sensitive Aggregation in Federated Reinforcement Learning for Edge-IoT Systems: A Review

Examination of aggregation stability and feasibility-sensitive aggregation in FRL for Edge-IoT systems identifies a need for aggregation mechanisms that jointly account for update stability, update reliability, resource availability, and constraint feasibility.

M. А. Aslan, Ahmed Al-Shalabi, Ahmed S. Alhegami · 0 citations

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