Author

Sabita Maharjan

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Open access Oct 2026

Hierarchical Graph Neural Network for Probabilistic Power Flow With Grid Topology Contingencies

Data-driven surrogates reduce the nonlinear solves required for probabilistic power flow (PPF), but models tied to a fixed bus--branch graph often generalize poorly when outages change electrical coupling. This paper proposes a topology-decomposition-driven hierarchical bidirectional graph neural network (TDD--HBGNN) f...

Jin-Bao Wang, Jun Liu, Shi-Liang Zhang et al. · IEEE Transactions on Industr... · 0 citations

#artificial intelligence Preprint Oct 2026

MCIR: A Feature Dependence-Aware Explainability Method with Reliability Guarantees

Modern machine-learning models often contain strongly dependent or redundant features, making feature attribution difficult because shared predictive information can be distributed across correlated predictors. Existing methods such as SHAP, LIME, HSIC, MI/CMI, and SAGE may therefore produce unstable rankings under mul...

Poushali Sengupta, S. Maharjan, Frank Eliassen et al. · 0 citations

Jul 2026

Token Communications (TokCom): A Unified AI-Native Communication Framework

As artificial intelligence (AI) evolves from static perception to generative reasoning and autonomous agency, the fundamental principles of wireless communications are undergoing a paradigm shift. The classical Shannon paradigm, centered on reliable bit-level reconstruction for users, is increasingly misaligned with an...

Ya-Ru Fu, Liang Ji, S. Maharjan et al. · arXiv.org · 1 citation