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real-time-optimization

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redx94 / Dynamic-Neural-Network-Refinement

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Dynamic Neural Network Refinement (DNNR) is an advanced framework that allows neural networks to adapt in real time. Unlike static systems, DNNR refines network parameters on-the-fly to optimize performance. Its modularity ensures easy customization for versatile applications.

machine-learning deep-learning artificial-intelligence neural-networks intelligent-systems performance-optimization adaptive-systems machine-learning-framework data-driven-ai customizable-ai real-time-optimization ai-scalability on-the-fly-refinement network-parameter-tuning versatile-ai-solutions advanced-neural-architectures dynamic-learning-models ai-research-and-development
  • Updated Mar 9, 2026
  • Python

Jeremy-Cleland / parking_optimization

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Real-time collaborative parking optimization system using advanced algorithms including game theory Nash equilibrium, A* pathfinding, ML forecasting, and driver psychology modeling. CIS 505 project demonstrating practical algorithm applications in urban planning.

python machine-learning algorithms graph-algorithms simulation urban-planning data-visualization a-star game-theory dynamic-programming demand-forecasting nash-equilibrium university-of-michigan traffic-management route-optimization dynamic-pricing driver-behavior api-integration real-time-optimization parking-optimization
  • Updated Jul 19, 2025
  • Python

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