Author

Akramul Azim

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#machine learning Nov 2024

Uncertainty measurement for complex event prediction in safety-critical systems

This paper proposes an approach (ML_CP) incorporating ML and sensitivity analysis that verifies how the output varies according to each input parameter and measures the uncertainty associated with the predicted complex event.

Maria J. P. Peixoto, Akramul Azim · arXiv.org · 1 citation

Preprint Aug 2026

FedChronos: Federated Fine-Tuning of Time-Series Foundation Models for Privacy-Preserving Commodity Price Forecasting

FedChronos is introduced, a framework for federated parameter-efficient fine-tuning of an already pre-trained TSFM, a setting that existing federated time-series work has not addressed, since prior methods either pre-train from scratch or align prototypes rather than adapt a fixed backbone.

Amit Sharma, Nitin Auluck, Akramul Azim · 0 citations