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machine-learning-applications

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mojjss / pmu-placement-gnn-first-phase-public

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Reproducible Optimal PMU Placement (OPP) framework using a Graph Neural Network trained on solutions generated by a greedy OPP method.

machine-learning optimization networkx greedy ieee research-project research-paper greedy-algorithms opp pmu phasor-measurement-unit pandapower graph-neural-networks gnn-model gnn-algorithm gnn-pytorch electrical-power-engineering machine-learning-applications smart-power-grids power-network-optimization
  • Updated Aug 6, 2026
  • Jupyter Notebook

aanyabhandari3 / cerebras_POC

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Real-Time Financial News Sentiment Analysis uses the LLaMA 3.1 model with Cerebras inference to instantly gauge market sentiment from live financial news. The system scrapes, analyzes, and visualizes news sentiment to help investors make faster, data-driven decisions.

natural-language-processing data-visualization real-time-systems financial-data-analysis machine-learning-applications ai-powered-decision-support
  • Updated Oct 14, 2024
  • Jupyter Notebook

mojjss / pmu-placement-gnn-second-phase-public

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Greedy-supervised graph neural networks for PMU placement in power networks, with coverage-aware decoding, IEEE test-system datasets, and research workflows.

machine-learning optimization networkx greedy ieee research-project research-paper greedy-algorithms opp pmu phasor-measurement-unit pandapower graph-neural-networks gnn-model gnn-algorithm gnn-pytorch electrical-power-engineering machine-learning-applications smart-power-grids power-network-optimization
  • Updated Nov 1, 2026
  • Python

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